title stringlengths 1 185 | diff stringlengths 0 32.2M | body stringlengths 0 123k ⌀ | url stringlengths 57 58 | created_at stringlengths 20 20 | closed_at stringlengths 20 20 | merged_at stringlengths 20 20 ⌀ | updated_at stringlengths 20 20 |
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REF: collect arithmetic benchmarks | diff --git a/asv_bench/benchmarks/binary_ops.py b/asv_bench/benchmarks/arithmetic.py
similarity index 51%
rename from asv_bench/benchmarks/binary_ops.py
rename to asv_bench/benchmarks/arithmetic.py
index 64e067d25a454..d1e94f62967f4 100644
--- a/asv_bench/benchmarks/binary_ops.py
+++ b/asv_bench/benchmarks/arithmetic.py
@@ -1,14 +1,23 @@
import operator
+import warnings
import numpy as np
-from pandas import DataFrame, Series, date_range
+import pandas as pd
+from pandas import DataFrame, Series, Timestamp, date_range, to_timedelta
+import pandas._testing as tm
from pandas.core.algorithms import checked_add_with_arr
+from .pandas_vb_common import numeric_dtypes
+
try:
import pandas.core.computation.expressions as expr
except ImportError:
import pandas.computation.expressions as expr
+try:
+ import pandas.tseries.holiday
+except ImportError:
+ pass
class IntFrameWithScalar:
@@ -151,6 +160,110 @@ def time_timestamp_ops_diff_with_shift(self, tz):
self.s - self.s.shift()
+class IrregularOps:
+ def setup(self):
+ N = 10 ** 5
+ idx = date_range(start="1/1/2000", periods=N, freq="s")
+ s = Series(np.random.randn(N), index=idx)
+ self.left = s.sample(frac=1)
+ self.right = s.sample(frac=1)
+
+ def time_add(self):
+ self.left + self.right
+
+
+class TimedeltaOps:
+ def setup(self):
+ self.td = to_timedelta(np.arange(1000000))
+ self.ts = Timestamp("2000")
+
+ def time_add_td_ts(self):
+ self.td + self.ts
+
+
+class CategoricalComparisons:
+ params = ["__lt__", "__le__", "__eq__", "__ne__", "__ge__", "__gt__"]
+ param_names = ["op"]
+
+ def setup(self, op):
+ N = 10 ** 5
+ self.cat = pd.Categorical(list("aabbcd") * N, ordered=True)
+
+ def time_categorical_op(self, op):
+ getattr(self.cat, op)("b")
+
+
+class IndexArithmetic:
+
+ params = ["float", "int"]
+ param_names = ["dtype"]
+
+ def setup(self, dtype):
+ N = 10 ** 6
+ indexes = {"int": "makeIntIndex", "float": "makeFloatIndex"}
+ self.index = getattr(tm, indexes[dtype])(N)
+
+ def time_add(self, dtype):
+ self.index + 2
+
+ def time_subtract(self, dtype):
+ self.index - 2
+
+ def time_multiply(self, dtype):
+ self.index * 2
+
+ def time_divide(self, dtype):
+ self.index / 2
+
+ def time_modulo(self, dtype):
+ self.index % 2
+
+
+class NumericInferOps:
+ # from GH 7332
+ params = numeric_dtypes
+ param_names = ["dtype"]
+
+ def setup(self, dtype):
+ N = 5 * 10 ** 5
+ self.df = DataFrame(
+ {"A": np.arange(N).astype(dtype), "B": np.arange(N).astype(dtype)}
+ )
+
+ def time_add(self, dtype):
+ self.df["A"] + self.df["B"]
+
+ def time_subtract(self, dtype):
+ self.df["A"] - self.df["B"]
+
+ def time_multiply(self, dtype):
+ self.df["A"] * self.df["B"]
+
+ def time_divide(self, dtype):
+ self.df["A"] / self.df["B"]
+
+ def time_modulo(self, dtype):
+ self.df["A"] % self.df["B"]
+
+
+class DateInferOps:
+ # from GH 7332
+ def setup_cache(self):
+ N = 5 * 10 ** 5
+ df = DataFrame({"datetime64": np.arange(N).astype("datetime64[ms]")})
+ df["timedelta"] = df["datetime64"] - df["datetime64"]
+ return df
+
+ def time_subtract_datetimes(self, df):
+ df["datetime64"] - df["datetime64"]
+
+ def time_timedelta_plus_datetime(self, df):
+ df["timedelta"] + df["datetime64"]
+
+ def time_add_timedeltas(self, df):
+ df["timedelta"] + df["timedelta"]
+
+
class AddOverflowScalar:
params = [1, -1, 0]
@@ -188,4 +301,68 @@ def time_add_overflow_both_arg_nan(self):
)
+hcal = pd.tseries.holiday.USFederalHolidayCalendar()
+# These offsets currently raise a NotImplimentedError with .apply_index()
+non_apply = [
+ pd.offsets.Day(),
+ pd.offsets.BYearEnd(),
+ pd.offsets.BYearBegin(),
+ pd.offsets.BQuarterEnd(),
+ pd.offsets.BQuarterBegin(),
+ pd.offsets.BMonthEnd(),
+ pd.offsets.BMonthBegin(),
+ pd.offsets.CustomBusinessDay(),
+ pd.offsets.CustomBusinessDay(calendar=hcal),
+ pd.offsets.CustomBusinessMonthBegin(calendar=hcal),
+ pd.offsets.CustomBusinessMonthEnd(calendar=hcal),
+ pd.offsets.CustomBusinessMonthEnd(calendar=hcal),
+]
+other_offsets = [
+ pd.offsets.YearEnd(),
+ pd.offsets.YearBegin(),
+ pd.offsets.QuarterEnd(),
+ pd.offsets.QuarterBegin(),
+ pd.offsets.MonthEnd(),
+ pd.offsets.MonthBegin(),
+ pd.offsets.DateOffset(months=2, days=2),
+ pd.offsets.BusinessDay(),
+ pd.offsets.SemiMonthEnd(),
+ pd.offsets.SemiMonthBegin(),
+]
+offsets = non_apply + other_offsets
+
+
+class OffsetArrayArithmetic:
+
+ params = offsets
+ param_names = ["offset"]
+
+ def setup(self, offset):
+ N = 10000
+ rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
+ self.rng = rng
+ self.ser = pd.Series(rng)
+
+ def time_add_series_offset(self, offset):
+ with warnings.catch_warnings(record=True):
+ self.ser + offset
+
+ def time_add_dti_offset(self, offset):
+ with warnings.catch_warnings(record=True):
+ self.rng + offset
+
+
+class ApplyIndex:
+ params = other_offsets
+ param_names = ["offset"]
+
+ def setup(self, offset):
+ N = 10000
+ rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
+ self.rng = rng
+
+ def time_apply_index(self, offset):
+ offset.apply_index(self.rng)
+
+
from .pandas_vb_common import setup # noqa: F401 isort:skip
diff --git a/asv_bench/benchmarks/categoricals.py b/asv_bench/benchmarks/categoricals.py
index 1dcd52ac074a6..6f43a6fd3fc9b 100644
--- a/asv_bench/benchmarks/categoricals.py
+++ b/asv_bench/benchmarks/categoricals.py
@@ -63,18 +63,6 @@ def time_existing_series(self):
pd.Categorical(self.series)
-class CategoricalOps:
- params = ["__lt__", "__le__", "__eq__", "__ne__", "__ge__", "__gt__"]
- param_names = ["op"]
-
- def setup(self, op):
- N = 10 ** 5
- self.cat = pd.Categorical(list("aabbcd") * N, ordered=True)
-
- def time_categorical_op(self, op):
- getattr(self.cat, op)("b")
-
-
class Concat:
def setup(self):
N = 10 ** 5
diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py
index 103141545504b..cf51a4d35f805 100644
--- a/asv_bench/benchmarks/index_object.py
+++ b/asv_bench/benchmarks/index_object.py
@@ -63,32 +63,6 @@ def time_is_dates_only(self):
self.dr._is_dates_only
-class Ops:
-
- params = ["float", "int"]
- param_names = ["dtype"]
-
- def setup(self, dtype):
- N = 10 ** 6
- indexes = {"int": "makeIntIndex", "float": "makeFloatIndex"}
- self.index = getattr(tm, indexes[dtype])(N)
-
- def time_add(self, dtype):
- self.index + 2
-
- def time_subtract(self, dtype):
- self.index - 2
-
- def time_multiply(self, dtype):
- self.index * 2
-
- def time_divide(self, dtype):
- self.index / 2
-
- def time_modulo(self, dtype):
- self.index % 2
-
-
class Range:
def setup(self):
self.idx_inc = RangeIndex(start=0, stop=10 ** 7, step=3)
diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py
index 1a8d5ede52512..40b064229ae49 100644
--- a/asv_bench/benchmarks/inference.py
+++ b/asv_bench/benchmarks/inference.py
@@ -1,53 +1,8 @@
import numpy as np
-from pandas import DataFrame, Series, to_numeric
+from pandas import Series, to_numeric
-from .pandas_vb_common import lib, numeric_dtypes, tm
-
-
-class NumericInferOps:
- # from GH 7332
- params = numeric_dtypes
- param_names = ["dtype"]
-
- def setup(self, dtype):
- N = 5 * 10 ** 5
- self.df = DataFrame(
- {"A": np.arange(N).astype(dtype), "B": np.arange(N).astype(dtype)}
- )
-
- def time_add(self, dtype):
- self.df["A"] + self.df["B"]
-
- def time_subtract(self, dtype):
- self.df["A"] - self.df["B"]
-
- def time_multiply(self, dtype):
- self.df["A"] * self.df["B"]
-
- def time_divide(self, dtype):
- self.df["A"] / self.df["B"]
-
- def time_modulo(self, dtype):
- self.df["A"] % self.df["B"]
-
-
-class DateInferOps:
- # from GH 7332
- def setup_cache(self):
- N = 5 * 10 ** 5
- df = DataFrame({"datetime64": np.arange(N).astype("datetime64[ms]")})
- df["timedelta"] = df["datetime64"] - df["datetime64"]
- return df
-
- def time_subtract_datetimes(self, df):
- df["datetime64"] - df["datetime64"]
-
- def time_timedelta_plus_datetime(self, df):
- df["timedelta"] + df["datetime64"]
-
- def time_add_timedeltas(self, df):
- df["timedelta"] + df["timedelta"]
+from .pandas_vb_common import lib, tm
class ToNumeric:
diff --git a/asv_bench/benchmarks/offset.py b/asv_bench/benchmarks/offset.py
deleted file mode 100644
index 77ce1b2763bce..0000000000000
--- a/asv_bench/benchmarks/offset.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import warnings
-
-import pandas as pd
-
-try:
- import pandas.tseries.holiday
-except ImportError:
- pass
-
-hcal = pd.tseries.holiday.USFederalHolidayCalendar()
-# These offsets currently raise a NotImplimentedError with .apply_index()
-non_apply = [
- pd.offsets.Day(),
- pd.offsets.BYearEnd(),
- pd.offsets.BYearBegin(),
- pd.offsets.BQuarterEnd(),
- pd.offsets.BQuarterBegin(),
- pd.offsets.BMonthEnd(),
- pd.offsets.BMonthBegin(),
- pd.offsets.CustomBusinessDay(),
- pd.offsets.CustomBusinessDay(calendar=hcal),
- pd.offsets.CustomBusinessMonthBegin(calendar=hcal),
- pd.offsets.CustomBusinessMonthEnd(calendar=hcal),
- pd.offsets.CustomBusinessMonthEnd(calendar=hcal),
-]
-other_offsets = [
- pd.offsets.YearEnd(),
- pd.offsets.YearBegin(),
- pd.offsets.QuarterEnd(),
- pd.offsets.QuarterBegin(),
- pd.offsets.MonthEnd(),
- pd.offsets.MonthBegin(),
- pd.offsets.DateOffset(months=2, days=2),
- pd.offsets.BusinessDay(),
- pd.offsets.SemiMonthEnd(),
- pd.offsets.SemiMonthBegin(),
-]
-offsets = non_apply + other_offsets
-
-
-class ApplyIndex:
-
- params = other_offsets
- param_names = ["offset"]
-
- def setup(self, offset):
- N = 10000
- self.rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
-
- def time_apply_index(self, offset):
- offset.apply_index(self.rng)
-
-
-class OffsetSeriesArithmetic:
-
- params = offsets
- param_names = ["offset"]
-
- def setup(self, offset):
- N = 1000
- rng = pd.date_range(start="1/1/2000", periods=N, freq="T")
- self.data = pd.Series(rng)
-
- def time_add_offset(self, offset):
- with warnings.catch_warnings(record=True):
- self.data + offset
-
-
-class OffsetDatetimeIndexArithmetic:
-
- params = offsets
- param_names = ["offset"]
-
- def setup(self, offset):
- N = 1000
- self.data = pd.date_range(start="1/1/2000", periods=N, freq="T")
-
- def time_add_offset(self, offset):
- with warnings.catch_warnings(record=True):
- self.data + offset
diff --git a/asv_bench/benchmarks/timedelta.py b/asv_bench/benchmarks/timedelta.py
index 37418d752f833..208c8f9d14a5e 100644
--- a/asv_bench/benchmarks/timedelta.py
+++ b/asv_bench/benchmarks/timedelta.py
@@ -5,7 +5,7 @@
import numpy as np
-from pandas import DataFrame, Series, Timestamp, timedelta_range, to_timedelta
+from pandas import DataFrame, Series, timedelta_range, to_timedelta
class ToTimedelta:
@@ -41,15 +41,6 @@ def time_convert(self, errors):
to_timedelta(self.arr, errors=errors)
-class TimedeltaOps:
- def setup(self):
- self.td = to_timedelta(np.arange(1000000))
- self.ts = Timestamp("2000")
-
- def time_add_td_ts(self):
- self.td + self.ts
-
-
class DatetimeAccessor:
def setup_cache(self):
N = 100000
diff --git a/asv_bench/benchmarks/timeseries.py b/asv_bench/benchmarks/timeseries.py
index ba0b51922fd31..2f7ea8b9c0873 100644
--- a/asv_bench/benchmarks/timeseries.py
+++ b/asv_bench/benchmarks/timeseries.py
@@ -262,18 +262,6 @@ def time_get_slice(self, monotonic):
self.s[:10000]
-class IrregularOps:
- def setup(self):
- N = 10 ** 5
- idx = date_range(start="1/1/2000", periods=N, freq="s")
- s = Series(np.random.randn(N), index=idx)
- self.left = s.sample(frac=1)
- self.right = s.sample(frac=1)
-
- def time_add(self):
- self.left + self.right
-
-
class Lookup:
def setup(self):
N = 1500000
| ATM these are scattered, making it tough to a) run just arithmetic-relevant benchmarks and b) determine what we have good benchmark coverage for.
This collects the scattered benchmarks in one place, cleaning up and fleshing these out is for a separate pass.
I also intend to do something analogous for indexing benchmarks. | https://api.github.com/repos/pandas-dev/pandas/pulls/32116 | 2020-02-19T18:53:09Z | 2020-02-22T16:06:41Z | 2020-02-22T16:06:41Z | 2020-02-22T16:08:29Z |
add test for "Allow definition of `pd.CategoricalDtype` with a specific `categories.dtype`" | diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 8aaebe89871b6..70e677411b4a9 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -175,6 +175,8 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
----------
categories : sequence, optional
Must be unique, and must not contain any nulls.
+ The categories are stored in an Index,
+ and if an index is provided the dtype of that index will be used.
ordered : bool or None, default False
Whether or not this categorical is treated as a ordered categorical.
None can be used to maintain the ordered value of existing categoricals when
@@ -210,6 +212,12 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
3 NaN
dtype: category
Categories (2, object): [b < a]
+
+ An empty CategoricalDtype with a specific dtype can be created
+ by providing an empty index. As follows,
+
+ >>> pd.CategoricalDtype(pd.DatetimeIndex([])).categories.dtype
+ dtype('<M8[ns]')
"""
# TODO: Document public vs. private API
diff --git a/pandas/tests/dtypes/test_dtypes.py b/pandas/tests/dtypes/test_dtypes.py
index dd99b81fb6764..645912b2a2c21 100644
--- a/pandas/tests/dtypes/test_dtypes.py
+++ b/pandas/tests/dtypes/test_dtypes.py
@@ -26,7 +26,14 @@
)
import pandas as pd
-from pandas import Categorical, CategoricalIndex, IntervalIndex, Series, date_range
+from pandas import (
+ Categorical,
+ CategoricalIndex,
+ DatetimeIndex,
+ IntervalIndex,
+ Series,
+ date_range,
+)
import pandas._testing as tm
from pandas.core.arrays.sparse import SparseArray, SparseDtype
@@ -172,6 +179,11 @@ def test_is_boolean(self, categories, expected):
assert is_bool_dtype(cat) is expected
assert is_bool_dtype(cat.dtype) is expected
+ def test_dtype_specific_categorical_dtype(self):
+ expected = "datetime64[ns]"
+ result = str(Categorical(DatetimeIndex([])).categories.dtype)
+ assert result == expected
+
class TestDatetimeTZDtype(Base):
@pytest.fixture
| - [x] closes #32096
- [x] tests added / passed
| https://api.github.com/repos/pandas-dev/pandas/pulls/32115 | 2020-02-19T17:17:23Z | 2020-03-02T15:30:18Z | 2020-03-02T15:30:18Z | 2020-03-02T15:30:34Z |
REF/TST: implement test_interpolate for Series | diff --git a/pandas/tests/series/methods/test_interpolate.py b/pandas/tests/series/methods/test_interpolate.py
new file mode 100644
index 0000000000000..6844225a81a8f
--- /dev/null
+++ b/pandas/tests/series/methods/test_interpolate.py
@@ -0,0 +1,673 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import Index, MultiIndex, Series, date_range, isna
+import pandas._testing as tm
+
+
+@pytest.fixture(
+ params=[
+ "linear",
+ "index",
+ "values",
+ "nearest",
+ "slinear",
+ "zero",
+ "quadratic",
+ "cubic",
+ "barycentric",
+ "krogh",
+ "polynomial",
+ "spline",
+ "piecewise_polynomial",
+ "from_derivatives",
+ "pchip",
+ "akima",
+ ]
+)
+def nontemporal_method(request):
+ """ Fixture that returns an (method name, required kwargs) pair.
+
+ This fixture does not include method 'time' as a parameterization; that
+ method requires a Series with a DatetimeIndex, and is generally tested
+ separately from these non-temporal methods.
+ """
+ method = request.param
+ kwargs = dict(order=1) if method in ("spline", "polynomial") else dict()
+ return method, kwargs
+
+
+@pytest.fixture(
+ params=[
+ "linear",
+ "slinear",
+ "zero",
+ "quadratic",
+ "cubic",
+ "barycentric",
+ "krogh",
+ "polynomial",
+ "spline",
+ "piecewise_polynomial",
+ "from_derivatives",
+ "pchip",
+ "akima",
+ ]
+)
+def interp_methods_ind(request):
+ """ Fixture that returns a (method name, required kwargs) pair to
+ be tested for various Index types.
+
+ This fixture does not include methods - 'time', 'index', 'nearest',
+ 'values' as a parameterization
+ """
+ method = request.param
+ kwargs = dict(order=1) if method in ("spline", "polynomial") else dict()
+ return method, kwargs
+
+
+class TestSeriesInterpolateData:
+ def test_interpolate(self, datetime_series, string_series):
+ ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index)
+
+ ts_copy = ts.copy()
+ ts_copy[5:10] = np.NaN
+
+ linear_interp = ts_copy.interpolate(method="linear")
+ tm.assert_series_equal(linear_interp, ts)
+
+ ord_ts = Series(
+ [d.toordinal() for d in datetime_series.index], index=datetime_series.index
+ ).astype(float)
+
+ ord_ts_copy = ord_ts.copy()
+ ord_ts_copy[5:10] = np.NaN
+
+ time_interp = ord_ts_copy.interpolate(method="time")
+ tm.assert_series_equal(time_interp, ord_ts)
+
+ def test_interpolate_time_raises_for_non_timeseries(self):
+ # When method='time' is used on a non-TimeSeries that contains a null
+ # value, a ValueError should be raised.
+ non_ts = Series([0, 1, 2, np.NaN])
+ msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex"
+ with pytest.raises(ValueError, match=msg):
+ non_ts.interpolate(method="time")
+
+ @td.skip_if_no_scipy
+ def test_interpolate_pchip(self):
+
+ ser = Series(np.sort(np.random.uniform(size=100)))
+
+ # interpolate at new_index
+ new_index = ser.index.union(
+ Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
+ ).astype(float)
+ interp_s = ser.reindex(new_index).interpolate(method="pchip")
+ # does not blow up, GH5977
+ interp_s[49:51]
+
+ @td.skip_if_no_scipy
+ def test_interpolate_akima(self):
+
+ ser = Series([10, 11, 12, 13])
+
+ expected = Series(
+ [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+ index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+ )
+ # interpolate at new_index
+ new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+ float
+ )
+ interp_s = ser.reindex(new_index).interpolate(method="akima")
+ tm.assert_series_equal(interp_s[1:3], expected)
+
+ @td.skip_if_no_scipy
+ def test_interpolate_piecewise_polynomial(self):
+ ser = Series([10, 11, 12, 13])
+
+ expected = Series(
+ [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+ index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+ )
+ # interpolate at new_index
+ new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+ float
+ )
+ interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial")
+ tm.assert_series_equal(interp_s[1:3], expected)
+
+ @td.skip_if_no_scipy
+ def test_interpolate_from_derivatives(self):
+ ser = Series([10, 11, 12, 13])
+
+ expected = Series(
+ [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
+ index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
+ )
+ # interpolate at new_index
+ new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
+ float
+ )
+ interp_s = ser.reindex(new_index).interpolate(method="from_derivatives")
+ tm.assert_series_equal(interp_s[1:3], expected)
+
+ @pytest.mark.parametrize(
+ "kwargs",
+ [
+ {},
+ pytest.param(
+ {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
+ ),
+ ],
+ )
+ def test_interpolate_corners(self, kwargs):
+ s = Series([np.nan, np.nan])
+ tm.assert_series_equal(s.interpolate(**kwargs), s)
+
+ s = Series([], dtype=object).interpolate()
+ tm.assert_series_equal(s.interpolate(**kwargs), s)
+
+ def test_interpolate_index_values(self):
+ s = Series(np.nan, index=np.sort(np.random.rand(30)))
+ s[::3] = np.random.randn(10)
+
+ vals = s.index.values.astype(float)
+
+ result = s.interpolate(method="index")
+
+ expected = s.copy()
+ bad = isna(expected.values)
+ good = ~bad
+ expected = Series(
+ np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad]
+ )
+
+ tm.assert_series_equal(result[bad], expected)
+
+ # 'values' is synonymous with 'index' for the method kwarg
+ other_result = s.interpolate(method="values")
+
+ tm.assert_series_equal(other_result, result)
+ tm.assert_series_equal(other_result[bad], expected)
+
+ def test_interpolate_non_ts(self):
+ s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+ msg = (
+ "time-weighted interpolation only works on Series or DataFrames "
+ "with a DatetimeIndex"
+ )
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method="time")
+
+ @pytest.mark.parametrize(
+ "kwargs",
+ [
+ {},
+ pytest.param(
+ {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
+ ),
+ ],
+ )
+ def test_nan_interpolate(self, kwargs):
+ s = Series([0, 1, np.nan, 3])
+ result = s.interpolate(**kwargs)
+ expected = Series([0.0, 1.0, 2.0, 3.0])
+ tm.assert_series_equal(result, expected)
+
+ def test_nan_irregular_index(self):
+ s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9])
+ result = s.interpolate()
+ expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9])
+ tm.assert_series_equal(result, expected)
+
+ def test_nan_str_index(self):
+ s = Series([0, 1, 2, np.nan], index=list("abcd"))
+ result = s.interpolate()
+ expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd"))
+ tm.assert_series_equal(result, expected)
+
+ @td.skip_if_no_scipy
+ def test_interp_quad(self):
+ sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4])
+ result = sq.interpolate(method="quadratic")
+ expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4])
+ tm.assert_series_equal(result, expected)
+
+ @td.skip_if_no_scipy
+ def test_interp_scipy_basic(self):
+ s = Series([1, 3, np.nan, 12, np.nan, 25])
+ # slinear
+ expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0])
+ result = s.interpolate(method="slinear")
+ tm.assert_series_equal(result, expected)
+
+ result = s.interpolate(method="slinear", downcast="infer")
+ tm.assert_series_equal(result, expected)
+ # nearest
+ expected = Series([1, 3, 3, 12, 12, 25])
+ result = s.interpolate(method="nearest")
+ tm.assert_series_equal(result, expected.astype("float"))
+
+ result = s.interpolate(method="nearest", downcast="infer")
+ tm.assert_series_equal(result, expected)
+ # zero
+ expected = Series([1, 3, 3, 12, 12, 25])
+ result = s.interpolate(method="zero")
+ tm.assert_series_equal(result, expected.astype("float"))
+
+ result = s.interpolate(method="zero", downcast="infer")
+ tm.assert_series_equal(result, expected)
+ # quadratic
+ # GH #15662.
+ expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0])
+ result = s.interpolate(method="quadratic")
+ tm.assert_series_equal(result, expected)
+
+ result = s.interpolate(method="quadratic", downcast="infer")
+ tm.assert_series_equal(result, expected)
+ # cubic
+ expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0])
+ result = s.interpolate(method="cubic")
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_limit(self):
+ s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+ expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
+ result = s.interpolate(method="linear", limit=2)
+ tm.assert_series_equal(result, expected)
+
+ @pytest.mark.parametrize("limit", [-1, 0])
+ def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit):
+ # GH 9217: make sure limit is greater than zero.
+ s = pd.Series([1, 2, np.nan, 4])
+ method, kwargs = nontemporal_method
+ with pytest.raises(ValueError, match="Limit must be greater than 0"):
+ s.interpolate(limit=limit, method=method, **kwargs)
+
+ def test_interpolate_invalid_float_limit(self, nontemporal_method):
+ # GH 9217: make sure limit is an integer.
+ s = pd.Series([1, 2, np.nan, 4])
+ method, kwargs = nontemporal_method
+ limit = 2.0
+ with pytest.raises(ValueError, match="Limit must be an integer"):
+ s.interpolate(limit=limit, method=method, **kwargs)
+
+ @pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"])
+ def test_interp_invalid_method(self, invalid_method):
+ s = Series([1, 3, np.nan, 12, np.nan, 25])
+
+ msg = f"method must be one of.* Got '{invalid_method}' instead"
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method=invalid_method)
+
+ # When an invalid method and invalid limit (such as -1) are
+ # provided, the error message reflects the invalid method.
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method=invalid_method, limit=-1)
+
+ def test_interp_limit_forward(self):
+ s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+ # Provide 'forward' (the default) explicitly here.
+ expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
+
+ result = s.interpolate(method="linear", limit=2, limit_direction="forward")
+ tm.assert_series_equal(result, expected)
+
+ result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD")
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_unlimited(self):
+ # these test are for issue #16282 default Limit=None is unlimited
+ s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan])
+ expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
+ result = s.interpolate(method="linear", limit_direction="both")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
+ result = s.interpolate(method="linear", limit_direction="forward")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan])
+ result = s.interpolate(method="linear", limit_direction="backward")
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_limit_bad_direction(self):
+ s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+ msg = (
+ r"Invalid limit_direction: expecting one of \['forward', "
+ r"'backward', 'both'\], got 'abc'"
+ )
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method="linear", limit=2, limit_direction="abc")
+
+ # raises an error even if no limit is specified.
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method="linear", limit_direction="abc")
+
+ # limit_area introduced GH #16284
+ def test_interp_limit_area(self):
+ # These tests are for issue #9218 -- fill NaNs in both directions.
+ s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan])
+
+ expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan])
+ result = s.interpolate(method="linear", limit_area="inside")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series(
+ [np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan]
+ )
+ result = s.interpolate(method="linear", limit_area="inside", limit=1)
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan])
+ result = s.interpolate(
+ method="linear", limit_area="inside", limit_direction="both", limit=1
+ )
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0])
+ result = s.interpolate(method="linear", limit_area="outside")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series(
+ [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]
+ )
+ result = s.interpolate(method="linear", limit_area="outside", limit=1)
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan])
+ result = s.interpolate(
+ method="linear", limit_area="outside", limit_direction="both", limit=1
+ )
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan])
+ result = s.interpolate(
+ method="linear", limit_area="outside", limit_direction="backward"
+ )
+ tm.assert_series_equal(result, expected)
+
+ # raises an error even if limit type is wrong.
+ msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc"
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method="linear", limit_area="abc")
+
+ def test_interp_limit_direction(self):
+ # These tests are for issue #9218 -- fill NaNs in both directions.
+ s = Series([1, 3, np.nan, np.nan, np.nan, 11])
+
+ expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0])
+ result = s.interpolate(method="linear", limit=2, limit_direction="backward")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0])
+ result = s.interpolate(method="linear", limit=1, limit_direction="both")
+ tm.assert_series_equal(result, expected)
+
+ # Check that this works on a longer series of nans.
+ s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan])
+
+ expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0])
+ result = s.interpolate(method="linear", limit=2, limit_direction="both")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series(
+ [1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]
+ )
+ result = s.interpolate(method="linear", limit=1, limit_direction="both")
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_limit_to_ends(self):
+ # These test are for issue #10420 -- flow back to beginning.
+ s = Series([np.nan, np.nan, 5, 7, 9, np.nan])
+
+ expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan])
+ result = s.interpolate(method="linear", limit=2, limit_direction="backward")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0])
+ result = s.interpolate(method="linear", limit=2, limit_direction="both")
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_limit_before_ends(self):
+ # These test are for issue #11115 -- limit ends properly.
+ s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan])
+
+ expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan])
+ result = s.interpolate(method="linear", limit=1, limit_direction="forward")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan])
+ result = s.interpolate(method="linear", limit=1, limit_direction="backward")
+ tm.assert_series_equal(result, expected)
+
+ expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan])
+ result = s.interpolate(method="linear", limit=1, limit_direction="both")
+ tm.assert_series_equal(result, expected)
+
+ @td.skip_if_no_scipy
+ def test_interp_all_good(self):
+ s = Series([1, 2, 3])
+ result = s.interpolate(method="polynomial", order=1)
+ tm.assert_series_equal(result, s)
+
+ # non-scipy
+ result = s.interpolate()
+ tm.assert_series_equal(result, s)
+
+ @pytest.mark.parametrize(
+ "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)]
+ )
+ def test_interp_multiIndex(self, check_scipy):
+ idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")])
+ s = Series([1, 2, np.nan], index=idx)
+
+ expected = s.copy()
+ expected.loc[2] = 2
+ result = s.interpolate()
+ tm.assert_series_equal(result, expected)
+
+ msg = "Only `method=linear` interpolation is supported on MultiIndexes"
+ if check_scipy:
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method="polynomial", order=1)
+
+ @td.skip_if_no_scipy
+ def test_interp_nonmono_raise(self):
+ s = Series([1, np.nan, 3], index=[0, 2, 1])
+ msg = "krogh interpolation requires that the index be monotonic"
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method="krogh")
+
+ @td.skip_if_no_scipy
+ @pytest.mark.parametrize("method", ["nearest", "pad"])
+ def test_interp_datetime64(self, method, tz_naive_fixture):
+ df = Series(
+ [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture)
+ )
+ result = df.interpolate(method=method)
+ expected = Series(
+ [1.0, 1.0, 3.0],
+ index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture),
+ )
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_pad_datetime64tz_values(self):
+ # GH#27628 missing.interpolate_2d should handle datetimetz values
+ dti = pd.date_range("2015-04-05", periods=3, tz="US/Central")
+ ser = pd.Series(dti)
+ ser[1] = pd.NaT
+ result = ser.interpolate(method="pad")
+
+ expected = pd.Series(dti)
+ expected[1] = expected[0]
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_limit_no_nans(self):
+ # GH 7173
+ s = pd.Series([1.0, 2.0, 3.0])
+ result = s.interpolate(limit=1)
+ expected = s
+ tm.assert_series_equal(result, expected)
+
+ @td.skip_if_no_scipy
+ @pytest.mark.parametrize("method", ["polynomial", "spline"])
+ def test_no_order(self, method):
+ # see GH-10633, GH-24014
+ s = Series([0, 1, np.nan, 3])
+ msg = "You must specify the order of the spline or polynomial"
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method=method)
+
+ @td.skip_if_no_scipy
+ @pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan])
+ def test_interpolate_spline_invalid_order(self, order):
+ s = Series([0, 1, np.nan, 3])
+ msg = "order needs to be specified and greater than 0"
+ with pytest.raises(ValueError, match=msg):
+ s.interpolate(method="spline", order=order)
+
+ @td.skip_if_no_scipy
+ def test_spline(self):
+ s = Series([1, 2, np.nan, 4, 5, np.nan, 7])
+ result = s.interpolate(method="spline", order=1)
+ expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+ tm.assert_series_equal(result, expected)
+
+ @td.skip_if_no_scipy
+ def test_spline_extrapolate(self):
+ s = Series([1, 2, 3, 4, np.nan, 6, np.nan])
+ result3 = s.interpolate(method="spline", order=1, ext=3)
+ expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0])
+ tm.assert_series_equal(result3, expected3)
+
+ result1 = s.interpolate(method="spline", order=1, ext=0)
+ expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+ tm.assert_series_equal(result1, expected1)
+
+ @td.skip_if_no_scipy
+ def test_spline_smooth(self):
+ s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7])
+ assert (
+ s.interpolate(method="spline", order=3, s=0)[5]
+ != s.interpolate(method="spline", order=3)[5]
+ )
+
+ @td.skip_if_no_scipy
+ def test_spline_interpolation(self):
+ s = Series(np.arange(10) ** 2)
+ s[np.random.randint(0, 9, 3)] = np.nan
+ result1 = s.interpolate(method="spline", order=1)
+ expected1 = s.interpolate(method="spline", order=1)
+ tm.assert_series_equal(result1, expected1)
+
+ def test_interp_timedelta64(self):
+ # GH 6424
+ df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3]))
+ result = df.interpolate(method="time")
+ expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3]))
+ tm.assert_series_equal(result, expected)
+
+ # test for non uniform spacing
+ df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4]))
+ result = df.interpolate(method="time")
+ expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4]))
+ tm.assert_series_equal(result, expected)
+
+ def test_series_interpolate_method_values(self):
+ # GH#1646
+ rng = date_range("1/1/2000", "1/20/2000", freq="D")
+ ts = Series(np.random.randn(len(rng)), index=rng)
+
+ ts[::2] = np.nan
+
+ result = ts.interpolate(method="values")
+ exp = ts.interpolate()
+ tm.assert_series_equal(result, exp)
+
+ def test_series_interpolate_intraday(self):
+ # #1698
+ index = pd.date_range("1/1/2012", periods=4, freq="12D")
+ ts = pd.Series([0, 12, 24, 36], index)
+ new_index = index.append(index + pd.DateOffset(days=1)).sort_values()
+
+ exp = ts.reindex(new_index).interpolate(method="time")
+
+ index = pd.date_range("1/1/2012", periods=4, freq="12H")
+ ts = pd.Series([0, 12, 24, 36], index)
+ new_index = index.append(index + pd.DateOffset(hours=1)).sort_values()
+ result = ts.reindex(new_index).interpolate(method="time")
+
+ tm.assert_numpy_array_equal(result.values, exp.values)
+
+ @pytest.mark.parametrize(
+ "ind",
+ [
+ ["a", "b", "c", "d"],
+ pd.period_range(start="2019-01-01", periods=4),
+ pd.interval_range(start=0, end=4),
+ ],
+ )
+ def test_interp_non_timedelta_index(self, interp_methods_ind, ind):
+ # gh 21662
+ df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
+
+ method, kwargs = interp_methods_ind
+ if method == "pchip":
+ pytest.importorskip("scipy")
+
+ if method == "linear":
+ result = df[0].interpolate(**kwargs)
+ expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
+ tm.assert_series_equal(result, expected)
+ else:
+ expected_error = (
+ "Index column must be numeric or datetime type when "
+ f"using {method} method other than linear. "
+ "Try setting a numeric or datetime index column before "
+ "interpolating."
+ )
+ with pytest.raises(ValueError, match=expected_error):
+ df[0].interpolate(method=method, **kwargs)
+
+ def test_interpolate_timedelta_index(self, interp_methods_ind):
+ """
+ Tests for non numerical index types - object, period, timedelta
+ Note that all methods except time, index, nearest and values
+ are tested here.
+ """
+ # gh 21662
+ ind = pd.timedelta_range(start=1, periods=4)
+ df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
+
+ method, kwargs = interp_methods_ind
+ if method == "pchip":
+ pytest.importorskip("scipy")
+
+ if method in {"linear", "pchip"}:
+ result = df[0].interpolate(method=method, **kwargs)
+ expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
+ tm.assert_series_equal(result, expected)
+ else:
+ pytest.skip(
+ "This interpolation method is not supported for Timedelta Index yet."
+ )
+
+ @pytest.mark.parametrize(
+ "ascending, expected_values",
+ [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])],
+ )
+ def test_interpolate_unsorted_index(self, ascending, expected_values):
+ # GH 21037
+ ts = pd.Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1])
+ result = ts.sort_index(ascending=ascending).interpolate(method="index")
+ expected = pd.Series(data=expected_values, index=expected_values, dtype=float)
+ tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/series/test_internals.py b/pandas/tests/series/test_internals.py
index 4c817ed2e2d59..1566d8f36373b 100644
--- a/pandas/tests/series/test_internals.py
+++ b/pandas/tests/series/test_internals.py
@@ -169,6 +169,7 @@ def test_convert(self):
result = s._convert(datetime=True, coerce=True)
tm.assert_series_equal(result, s)
+ # FIXME: dont leave commented-out
# r = s.copy()
# r[0] = np.nan
# result = r._convert(convert_dates=True,convert_numeric=False)
diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py
index 6b7d9e00a5228..bac005465034f 100644
--- a/pandas/tests/series/test_missing.py
+++ b/pandas/tests/series/test_missing.py
@@ -5,7 +5,6 @@
import pytz
from pandas._libs.tslib import iNaT
-import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
@@ -13,7 +12,6 @@
DataFrame,
Index,
IntervalIndex,
- MultiIndex,
NaT,
Series,
Timedelta,
@@ -24,11 +22,6 @@
import pandas._testing as tm
-def _simple_ts(start, end, freq="D"):
- rng = date_range(start, end, freq=freq)
- return Series(np.random.randn(len(rng)), index=rng)
-
-
class TestSeriesMissingData:
def test_timedelta_fillna(self):
# GH 3371
@@ -988,666 +981,3 @@ def test_series_pad_backfill_limit(self):
expected = s[-2:].reindex(index).fillna(method="backfill")
expected[:3] = np.nan
tm.assert_series_equal(result, expected)
-
-
-@pytest.fixture(
- params=[
- "linear",
- "index",
- "values",
- "nearest",
- "slinear",
- "zero",
- "quadratic",
- "cubic",
- "barycentric",
- "krogh",
- "polynomial",
- "spline",
- "piecewise_polynomial",
- "from_derivatives",
- "pchip",
- "akima",
- ]
-)
-def nontemporal_method(request):
- """ Fixture that returns an (method name, required kwargs) pair.
-
- This fixture does not include method 'time' as a parameterization; that
- method requires a Series with a DatetimeIndex, and is generally tested
- separately from these non-temporal methods.
- """
- method = request.param
- kwargs = dict(order=1) if method in ("spline", "polynomial") else dict()
- return method, kwargs
-
-
-@pytest.fixture(
- params=[
- "linear",
- "slinear",
- "zero",
- "quadratic",
- "cubic",
- "barycentric",
- "krogh",
- "polynomial",
- "spline",
- "piecewise_polynomial",
- "from_derivatives",
- "pchip",
- "akima",
- ]
-)
-def interp_methods_ind(request):
- """ Fixture that returns a (method name, required kwargs) pair to
- be tested for various Index types.
-
- This fixture does not include methods - 'time', 'index', 'nearest',
- 'values' as a parameterization
- """
- method = request.param
- kwargs = dict(order=1) if method in ("spline", "polynomial") else dict()
- return method, kwargs
-
-
-class TestSeriesInterpolateData:
- def test_interpolate(self, datetime_series, string_series):
- ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index)
-
- ts_copy = ts.copy()
- ts_copy[5:10] = np.NaN
-
- linear_interp = ts_copy.interpolate(method="linear")
- tm.assert_series_equal(linear_interp, ts)
-
- ord_ts = Series(
- [d.toordinal() for d in datetime_series.index], index=datetime_series.index
- ).astype(float)
-
- ord_ts_copy = ord_ts.copy()
- ord_ts_copy[5:10] = np.NaN
-
- time_interp = ord_ts_copy.interpolate(method="time")
- tm.assert_series_equal(time_interp, ord_ts)
-
- def test_interpolate_time_raises_for_non_timeseries(self):
- # When method='time' is used on a non-TimeSeries that contains a null
- # value, a ValueError should be raised.
- non_ts = Series([0, 1, 2, np.NaN])
- msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex"
- with pytest.raises(ValueError, match=msg):
- non_ts.interpolate(method="time")
-
- @td.skip_if_no_scipy
- def test_interpolate_pchip(self):
-
- ser = Series(np.sort(np.random.uniform(size=100)))
-
- # interpolate at new_index
- new_index = ser.index.union(
- Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
- ).astype(float)
- interp_s = ser.reindex(new_index).interpolate(method="pchip")
- # does not blow up, GH5977
- interp_s[49:51]
-
- @td.skip_if_no_scipy
- def test_interpolate_akima(self):
-
- ser = Series([10, 11, 12, 13])
-
- expected = Series(
- [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
- index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
- )
- # interpolate at new_index
- new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
- float
- )
- interp_s = ser.reindex(new_index).interpolate(method="akima")
- tm.assert_series_equal(interp_s[1:3], expected)
-
- @td.skip_if_no_scipy
- def test_interpolate_piecewise_polynomial(self):
- ser = Series([10, 11, 12, 13])
-
- expected = Series(
- [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
- index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
- )
- # interpolate at new_index
- new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
- float
- )
- interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial")
- tm.assert_series_equal(interp_s[1:3], expected)
-
- @td.skip_if_no_scipy
- def test_interpolate_from_derivatives(self):
- ser = Series([10, 11, 12, 13])
-
- expected = Series(
- [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
- index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
- )
- # interpolate at new_index
- new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
- float
- )
- interp_s = ser.reindex(new_index).interpolate(method="from_derivatives")
- tm.assert_series_equal(interp_s[1:3], expected)
-
- @pytest.mark.parametrize(
- "kwargs",
- [
- {},
- pytest.param(
- {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
- ),
- ],
- )
- def test_interpolate_corners(self, kwargs):
- s = Series([np.nan, np.nan])
- tm.assert_series_equal(s.interpolate(**kwargs), s)
-
- s = Series([], dtype=object).interpolate()
- tm.assert_series_equal(s.interpolate(**kwargs), s)
-
- def test_interpolate_index_values(self):
- s = Series(np.nan, index=np.sort(np.random.rand(30)))
- s[::3] = np.random.randn(10)
-
- vals = s.index.values.astype(float)
-
- result = s.interpolate(method="index")
-
- expected = s.copy()
- bad = isna(expected.values)
- good = ~bad
- expected = Series(
- np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad]
- )
-
- tm.assert_series_equal(result[bad], expected)
-
- # 'values' is synonymous with 'index' for the method kwarg
- other_result = s.interpolate(method="values")
-
- tm.assert_series_equal(other_result, result)
- tm.assert_series_equal(other_result[bad], expected)
-
- def test_interpolate_non_ts(self):
- s = Series([1, 3, np.nan, np.nan, np.nan, 11])
- msg = (
- "time-weighted interpolation only works on Series or DataFrames "
- "with a DatetimeIndex"
- )
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method="time")
-
- @pytest.mark.parametrize(
- "kwargs",
- [
- {},
- pytest.param(
- {"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
- ),
- ],
- )
- def test_nan_interpolate(self, kwargs):
- s = Series([0, 1, np.nan, 3])
- result = s.interpolate(**kwargs)
- expected = Series([0.0, 1.0, 2.0, 3.0])
- tm.assert_series_equal(result, expected)
-
- def test_nan_irregular_index(self):
- s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9])
- result = s.interpolate()
- expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9])
- tm.assert_series_equal(result, expected)
-
- def test_nan_str_index(self):
- s = Series([0, 1, 2, np.nan], index=list("abcd"))
- result = s.interpolate()
- expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd"))
- tm.assert_series_equal(result, expected)
-
- @td.skip_if_no_scipy
- def test_interp_quad(self):
- sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4])
- result = sq.interpolate(method="quadratic")
- expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4])
- tm.assert_series_equal(result, expected)
-
- @td.skip_if_no_scipy
- def test_interp_scipy_basic(self):
- s = Series([1, 3, np.nan, 12, np.nan, 25])
- # slinear
- expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0])
- result = s.interpolate(method="slinear")
- tm.assert_series_equal(result, expected)
-
- result = s.interpolate(method="slinear", downcast="infer")
- tm.assert_series_equal(result, expected)
- # nearest
- expected = Series([1, 3, 3, 12, 12, 25])
- result = s.interpolate(method="nearest")
- tm.assert_series_equal(result, expected.astype("float"))
-
- result = s.interpolate(method="nearest", downcast="infer")
- tm.assert_series_equal(result, expected)
- # zero
- expected = Series([1, 3, 3, 12, 12, 25])
- result = s.interpolate(method="zero")
- tm.assert_series_equal(result, expected.astype("float"))
-
- result = s.interpolate(method="zero", downcast="infer")
- tm.assert_series_equal(result, expected)
- # quadratic
- # GH #15662.
- expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0])
- result = s.interpolate(method="quadratic")
- tm.assert_series_equal(result, expected)
-
- result = s.interpolate(method="quadratic", downcast="infer")
- tm.assert_series_equal(result, expected)
- # cubic
- expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0])
- result = s.interpolate(method="cubic")
- tm.assert_series_equal(result, expected)
-
- def test_interp_limit(self):
- s = Series([1, 3, np.nan, np.nan, np.nan, 11])
-
- expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
- result = s.interpolate(method="linear", limit=2)
- tm.assert_series_equal(result, expected)
-
- @pytest.mark.parametrize("limit", [-1, 0])
- def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit):
- # GH 9217: make sure limit is greater than zero.
- s = pd.Series([1, 2, np.nan, 4])
- method, kwargs = nontemporal_method
- with pytest.raises(ValueError, match="Limit must be greater than 0"):
- s.interpolate(limit=limit, method=method, **kwargs)
-
- def test_interpolate_invalid_float_limit(self, nontemporal_method):
- # GH 9217: make sure limit is an integer.
- s = pd.Series([1, 2, np.nan, 4])
- method, kwargs = nontemporal_method
- limit = 2.0
- with pytest.raises(ValueError, match="Limit must be an integer"):
- s.interpolate(limit=limit, method=method, **kwargs)
-
- @pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"])
- def test_interp_invalid_method(self, invalid_method):
- s = Series([1, 3, np.nan, 12, np.nan, 25])
-
- msg = f"method must be one of.* Got '{invalid_method}' instead"
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method=invalid_method)
-
- # When an invalid method and invalid limit (such as -1) are
- # provided, the error message reflects the invalid method.
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method=invalid_method, limit=-1)
-
- def test_interp_limit_forward(self):
- s = Series([1, 3, np.nan, np.nan, np.nan, 11])
-
- # Provide 'forward' (the default) explicitly here.
- expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
-
- result = s.interpolate(method="linear", limit=2, limit_direction="forward")
- tm.assert_series_equal(result, expected)
-
- result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD")
- tm.assert_series_equal(result, expected)
-
- def test_interp_unlimited(self):
- # these test are for issue #16282 default Limit=None is unlimited
- s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan])
- expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
- result = s.interpolate(method="linear", limit_direction="both")
- tm.assert_series_equal(result, expected)
-
- expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
- result = s.interpolate(method="linear", limit_direction="forward")
- tm.assert_series_equal(result, expected)
-
- expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan])
- result = s.interpolate(method="linear", limit_direction="backward")
- tm.assert_series_equal(result, expected)
-
- def test_interp_limit_bad_direction(self):
- s = Series([1, 3, np.nan, np.nan, np.nan, 11])
-
- msg = (
- r"Invalid limit_direction: expecting one of \['forward', "
- r"'backward', 'both'\], got 'abc'"
- )
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method="linear", limit=2, limit_direction="abc")
-
- # raises an error even if no limit is specified.
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method="linear", limit_direction="abc")
-
- # limit_area introduced GH #16284
- def test_interp_limit_area(self):
- # These tests are for issue #9218 -- fill NaNs in both directions.
- s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan])
-
- expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan])
- result = s.interpolate(method="linear", limit_area="inside")
- tm.assert_series_equal(result, expected)
-
- expected = Series(
- [np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan]
- )
- result = s.interpolate(method="linear", limit_area="inside", limit=1)
- tm.assert_series_equal(result, expected)
-
- expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan])
- result = s.interpolate(
- method="linear", limit_area="inside", limit_direction="both", limit=1
- )
- tm.assert_series_equal(result, expected)
-
- expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0])
- result = s.interpolate(method="linear", limit_area="outside")
- tm.assert_series_equal(result, expected)
-
- expected = Series(
- [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]
- )
- result = s.interpolate(method="linear", limit_area="outside", limit=1)
- tm.assert_series_equal(result, expected)
-
- expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan])
- result = s.interpolate(
- method="linear", limit_area="outside", limit_direction="both", limit=1
- )
- tm.assert_series_equal(result, expected)
-
- expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan])
- result = s.interpolate(
- method="linear", limit_area="outside", limit_direction="backward"
- )
- tm.assert_series_equal(result, expected)
-
- # raises an error even if limit type is wrong.
- msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc"
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method="linear", limit_area="abc")
-
- def test_interp_limit_direction(self):
- # These tests are for issue #9218 -- fill NaNs in both directions.
- s = Series([1, 3, np.nan, np.nan, np.nan, 11])
-
- expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0])
- result = s.interpolate(method="linear", limit=2, limit_direction="backward")
- tm.assert_series_equal(result, expected)
-
- expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0])
- result = s.interpolate(method="linear", limit=1, limit_direction="both")
- tm.assert_series_equal(result, expected)
-
- # Check that this works on a longer series of nans.
- s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan])
-
- expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0])
- result = s.interpolate(method="linear", limit=2, limit_direction="both")
- tm.assert_series_equal(result, expected)
-
- expected = Series(
- [1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]
- )
- result = s.interpolate(method="linear", limit=1, limit_direction="both")
- tm.assert_series_equal(result, expected)
-
- def test_interp_limit_to_ends(self):
- # These test are for issue #10420 -- flow back to beginning.
- s = Series([np.nan, np.nan, 5, 7, 9, np.nan])
-
- expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan])
- result = s.interpolate(method="linear", limit=2, limit_direction="backward")
- tm.assert_series_equal(result, expected)
-
- expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0])
- result = s.interpolate(method="linear", limit=2, limit_direction="both")
- tm.assert_series_equal(result, expected)
-
- def test_interp_limit_before_ends(self):
- # These test are for issue #11115 -- limit ends properly.
- s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan])
-
- expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan])
- result = s.interpolate(method="linear", limit=1, limit_direction="forward")
- tm.assert_series_equal(result, expected)
-
- expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan])
- result = s.interpolate(method="linear", limit=1, limit_direction="backward")
- tm.assert_series_equal(result, expected)
-
- expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan])
- result = s.interpolate(method="linear", limit=1, limit_direction="both")
- tm.assert_series_equal(result, expected)
-
- @td.skip_if_no_scipy
- def test_interp_all_good(self):
- s = Series([1, 2, 3])
- result = s.interpolate(method="polynomial", order=1)
- tm.assert_series_equal(result, s)
-
- # non-scipy
- result = s.interpolate()
- tm.assert_series_equal(result, s)
-
- @pytest.mark.parametrize(
- "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)]
- )
- def test_interp_multiIndex(self, check_scipy):
- idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")])
- s = Series([1, 2, np.nan], index=idx)
-
- expected = s.copy()
- expected.loc[2] = 2
- result = s.interpolate()
- tm.assert_series_equal(result, expected)
-
- msg = "Only `method=linear` interpolation is supported on MultiIndexes"
- if check_scipy:
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method="polynomial", order=1)
-
- @td.skip_if_no_scipy
- def test_interp_nonmono_raise(self):
- s = Series([1, np.nan, 3], index=[0, 2, 1])
- msg = "krogh interpolation requires that the index be monotonic"
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method="krogh")
-
- @td.skip_if_no_scipy
- @pytest.mark.parametrize("method", ["nearest", "pad"])
- def test_interp_datetime64(self, method, tz_naive_fixture):
- df = Series(
- [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture)
- )
- result = df.interpolate(method=method)
- expected = Series(
- [1.0, 1.0, 3.0],
- index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture),
- )
- tm.assert_series_equal(result, expected)
-
- def test_interp_pad_datetime64tz_values(self):
- # GH#27628 missing.interpolate_2d should handle datetimetz values
- dti = pd.date_range("2015-04-05", periods=3, tz="US/Central")
- ser = pd.Series(dti)
- ser[1] = pd.NaT
- result = ser.interpolate(method="pad")
-
- expected = pd.Series(dti)
- expected[1] = expected[0]
- tm.assert_series_equal(result, expected)
-
- def test_interp_limit_no_nans(self):
- # GH 7173
- s = pd.Series([1.0, 2.0, 3.0])
- result = s.interpolate(limit=1)
- expected = s
- tm.assert_series_equal(result, expected)
-
- @td.skip_if_no_scipy
- @pytest.mark.parametrize("method", ["polynomial", "spline"])
- def test_no_order(self, method):
- # see GH-10633, GH-24014
- s = Series([0, 1, np.nan, 3])
- msg = "You must specify the order of the spline or polynomial"
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method=method)
-
- @td.skip_if_no_scipy
- @pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan])
- def test_interpolate_spline_invalid_order(self, order):
- s = Series([0, 1, np.nan, 3])
- msg = "order needs to be specified and greater than 0"
- with pytest.raises(ValueError, match=msg):
- s.interpolate(method="spline", order=order)
-
- @td.skip_if_no_scipy
- def test_spline(self):
- s = Series([1, 2, np.nan, 4, 5, np.nan, 7])
- result = s.interpolate(method="spline", order=1)
- expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
- tm.assert_series_equal(result, expected)
-
- @td.skip_if_no_scipy
- def test_spline_extrapolate(self):
- s = Series([1, 2, 3, 4, np.nan, 6, np.nan])
- result3 = s.interpolate(method="spline", order=1, ext=3)
- expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0])
- tm.assert_series_equal(result3, expected3)
-
- result1 = s.interpolate(method="spline", order=1, ext=0)
- expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
- tm.assert_series_equal(result1, expected1)
-
- @td.skip_if_no_scipy
- def test_spline_smooth(self):
- s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7])
- assert (
- s.interpolate(method="spline", order=3, s=0)[5]
- != s.interpolate(method="spline", order=3)[5]
- )
-
- @td.skip_if_no_scipy
- def test_spline_interpolation(self):
- s = Series(np.arange(10) ** 2)
- s[np.random.randint(0, 9, 3)] = np.nan
- result1 = s.interpolate(method="spline", order=1)
- expected1 = s.interpolate(method="spline", order=1)
- tm.assert_series_equal(result1, expected1)
-
- def test_interp_timedelta64(self):
- # GH 6424
- df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3]))
- result = df.interpolate(method="time")
- expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3]))
- tm.assert_series_equal(result, expected)
-
- # test for non uniform spacing
- df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4]))
- result = df.interpolate(method="time")
- expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4]))
- tm.assert_series_equal(result, expected)
-
- def test_series_interpolate_method_values(self):
- # #1646
- ts = _simple_ts("1/1/2000", "1/20/2000")
- ts[::2] = np.nan
-
- result = ts.interpolate(method="values")
- exp = ts.interpolate()
- tm.assert_series_equal(result, exp)
-
- def test_series_interpolate_intraday(self):
- # #1698
- index = pd.date_range("1/1/2012", periods=4, freq="12D")
- ts = pd.Series([0, 12, 24, 36], index)
- new_index = index.append(index + pd.DateOffset(days=1)).sort_values()
-
- exp = ts.reindex(new_index).interpolate(method="time")
-
- index = pd.date_range("1/1/2012", periods=4, freq="12H")
- ts = pd.Series([0, 12, 24, 36], index)
- new_index = index.append(index + pd.DateOffset(hours=1)).sort_values()
- result = ts.reindex(new_index).interpolate(method="time")
-
- tm.assert_numpy_array_equal(result.values, exp.values)
-
- @pytest.mark.parametrize(
- "ind",
- [
- ["a", "b", "c", "d"],
- pd.period_range(start="2019-01-01", periods=4),
- pd.interval_range(start=0, end=4),
- ],
- )
- def test_interp_non_timedelta_index(self, interp_methods_ind, ind):
- # gh 21662
- df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
-
- method, kwargs = interp_methods_ind
- if method == "pchip":
- pytest.importorskip("scipy")
-
- if method == "linear":
- result = df[0].interpolate(**kwargs)
- expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
- tm.assert_series_equal(result, expected)
- else:
- expected_error = (
- "Index column must be numeric or datetime type when "
- f"using {method} method other than linear. "
- "Try setting a numeric or datetime index column before "
- "interpolating."
- )
- with pytest.raises(ValueError, match=expected_error):
- df[0].interpolate(method=method, **kwargs)
-
- def test_interpolate_timedelta_index(self, interp_methods_ind):
- """
- Tests for non numerical index types - object, period, timedelta
- Note that all methods except time, index, nearest and values
- are tested here.
- """
- # gh 21662
- ind = pd.timedelta_range(start=1, periods=4)
- df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
-
- method, kwargs = interp_methods_ind
- if method == "pchip":
- pytest.importorskip("scipy")
-
- if method in {"linear", "pchip"}:
- result = df[0].interpolate(method=method, **kwargs)
- expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
- tm.assert_series_equal(result, expected)
- else:
- pytest.skip(
- "This interpolation method is not supported for Timedelta Index yet."
- )
-
- @pytest.mark.parametrize(
- "ascending, expected_values",
- [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])],
- )
- def test_interpolate_unsorted_index(self, ascending, expected_values):
- # GH 21037
- ts = pd.Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1])
- result = ts.sort_index(ascending=ascending).interpolate(method="index")
- expected = pd.Series(data=expected_values, index=expected_values, dtype=float)
- tm.assert_series_equal(result, expected)
| https://api.github.com/repos/pandas-dev/pandas/pulls/32112 | 2020-02-19T16:34:58Z | 2020-02-22T16:01:54Z | 2020-02-22T16:01:54Z | 2020-02-22T16:49:18Z | |
REF: misplaced Series.combine_first tests | diff --git a/pandas/tests/series/methods/test_combine_first.py b/pandas/tests/series/methods/test_combine_first.py
index aed6425e50117..1ee55fbe39513 100644
--- a/pandas/tests/series/methods/test_combine_first.py
+++ b/pandas/tests/series/methods/test_combine_first.py
@@ -1,6 +1,9 @@
+from datetime import datetime
+
import numpy as np
-from pandas import Period, Series, date_range, period_range
+import pandas as pd
+from pandas import Period, Series, date_range, period_range, to_datetime
import pandas._testing as tm
@@ -17,3 +20,75 @@ def test_combine_first_period_datetime(self):
result = a.combine_first(b)
expected = Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64)
tm.assert_series_equal(result, expected)
+
+ def test_combine_first_name(self, datetime_series):
+ result = datetime_series.combine_first(datetime_series[:5])
+ assert result.name == datetime_series.name
+
+ def test_combine_first(self):
+ values = tm.makeIntIndex(20).values.astype(float)
+ series = Series(values, index=tm.makeIntIndex(20))
+
+ series_copy = series * 2
+ series_copy[::2] = np.NaN
+
+ # nothing used from the input
+ combined = series.combine_first(series_copy)
+
+ tm.assert_series_equal(combined, series)
+
+ # Holes filled from input
+ combined = series_copy.combine_first(series)
+ assert np.isfinite(combined).all()
+
+ tm.assert_series_equal(combined[::2], series[::2])
+ tm.assert_series_equal(combined[1::2], series_copy[1::2])
+
+ # mixed types
+ index = tm.makeStringIndex(20)
+ floats = Series(tm.randn(20), index=index)
+ strings = Series(tm.makeStringIndex(10), index=index[::2])
+
+ combined = strings.combine_first(floats)
+
+ tm.assert_series_equal(strings, combined.loc[index[::2]])
+ tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]])
+
+ # corner case
+ ser = Series([1.0, 2, 3], index=[0, 1, 2])
+ empty = Series([], index=[], dtype=object)
+ result = ser.combine_first(empty)
+ ser.index = ser.index.astype("O")
+ tm.assert_series_equal(ser, result)
+
+ def test_combine_first_dt64(self):
+
+ s0 = to_datetime(Series(["2010", np.NaN]))
+ s1 = to_datetime(Series([np.NaN, "2011"]))
+ rs = s0.combine_first(s1)
+ xp = to_datetime(Series(["2010", "2011"]))
+ tm.assert_series_equal(rs, xp)
+
+ s0 = to_datetime(Series(["2010", np.NaN]))
+ s1 = Series([np.NaN, "2011"])
+ rs = s0.combine_first(s1)
+ xp = Series([datetime(2010, 1, 1), "2011"])
+ tm.assert_series_equal(rs, xp)
+
+ def test_combine_first_dt_tz_values(self, tz_naive_fixture):
+ ser1 = pd.Series(
+ pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture),
+ name="ser1",
+ )
+ ser2 = pd.Series(
+ pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture),
+ index=[2, 3, 4],
+ name="ser2",
+ )
+ result = ser1.combine_first(ser2)
+ exp_vals = pd.DatetimeIndex(
+ ["20150101", "20150102", "20150103", "20160515", "20160516"],
+ tz=tz_naive_fixture,
+ )
+ exp = pd.Series(exp_vals, name="ser1")
+ tm.assert_series_equal(exp, result)
diff --git a/pandas/tests/series/test_api.py b/pandas/tests/series/test_api.py
index 33706c00c53f4..3e877cf2fc787 100644
--- a/pandas/tests/series/test_api.py
+++ b/pandas/tests/series/test_api.py
@@ -85,10 +85,6 @@ def test_binop_maybe_preserve_name(self, datetime_series):
result = getattr(s, op)(cp)
assert result.name is None
- def test_combine_first_name(self, datetime_series):
- result = datetime_series.combine_first(datetime_series[:5])
- assert result.name == datetime_series.name
-
def test_getitem_preserve_name(self, datetime_series):
result = datetime_series[datetime_series > 0]
assert result.name == datetime_series.name
diff --git a/pandas/tests/series/test_combine_concat.py b/pandas/tests/series/test_combine_concat.py
index 4cb471597b67a..4afa083e97c7c 100644
--- a/pandas/tests/series/test_combine_concat.py
+++ b/pandas/tests/series/test_combine_concat.py
@@ -1,10 +1,8 @@
-from datetime import datetime
-
import numpy as np
import pytest
import pandas as pd
-from pandas import DataFrame, Series, to_datetime
+from pandas import DataFrame, Series
import pandas._testing as tm
@@ -22,42 +20,6 @@ def test_combine_scalar(self):
expected = pd.Series([min(i * 10, 22) for i in range(5)])
tm.assert_series_equal(result, expected)
- def test_combine_first(self):
- values = tm.makeIntIndex(20).values.astype(float)
- series = Series(values, index=tm.makeIntIndex(20))
-
- series_copy = series * 2
- series_copy[::2] = np.NaN
-
- # nothing used from the input
- combined = series.combine_first(series_copy)
-
- tm.assert_series_equal(combined, series)
-
- # Holes filled from input
- combined = series_copy.combine_first(series)
- assert np.isfinite(combined).all()
-
- tm.assert_series_equal(combined[::2], series[::2])
- tm.assert_series_equal(combined[1::2], series_copy[1::2])
-
- # mixed types
- index = tm.makeStringIndex(20)
- floats = Series(tm.randn(20), index=index)
- strings = Series(tm.makeStringIndex(10), index=index[::2])
-
- combined = strings.combine_first(floats)
-
- tm.assert_series_equal(strings, combined.loc[index[::2]])
- tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]])
-
- # corner case
- s = Series([1.0, 2, 3], index=[0, 1, 2])
- empty = Series([], index=[], dtype=object)
- result = s.combine_first(empty)
- s.index = s.index.astype("O")
- tm.assert_series_equal(s, result)
-
def test_update(self):
s = Series([1.5, np.nan, 3.0, 4.0, np.nan])
s2 = Series([np.nan, 3.5, np.nan, 5.0])
@@ -156,24 +118,6 @@ def get_result_type(dtype, dtype2):
result = pd.concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype
assert result.kind == expected
- def test_combine_first_dt_tz_values(self, tz_naive_fixture):
- ser1 = pd.Series(
- pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture),
- name="ser1",
- )
- ser2 = pd.Series(
- pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture),
- index=[2, 3, 4],
- name="ser2",
- )
- result = ser1.combine_first(ser2)
- exp_vals = pd.DatetimeIndex(
- ["20150101", "20150102", "20150103", "20160515", "20160516"],
- tz=tz_naive_fixture,
- )
- exp = pd.Series(exp_vals, name="ser1")
- tm.assert_series_equal(exp, result)
-
def test_concat_empty_series_dtypes(self):
# booleans
@@ -250,17 +194,3 @@ def test_concat_empty_series_dtypes(self):
# TODO: release-note: concat sparse dtype
expected = pd.SparseDtype("object")
assert result.dtype == expected
-
- def test_combine_first_dt64(self):
-
- s0 = to_datetime(Series(["2010", np.NaN]))
- s1 = to_datetime(Series([np.NaN, "2011"]))
- rs = s0.combine_first(s1)
- xp = to_datetime(Series(["2010", "2011"]))
- tm.assert_series_equal(rs, xp)
-
- s0 = to_datetime(Series(["2010", np.NaN]))
- s1 = Series([np.NaN, "2011"])
- rs = s0.combine_first(s1)
- xp = Series([datetime(2010, 1, 1), "2011"])
- tm.assert_series_equal(rs, xp)
| https://api.github.com/repos/pandas-dev/pandas/pulls/32111 | 2020-02-19T16:18:36Z | 2020-02-20T12:44:52Z | 2020-02-20T12:44:52Z | 2020-02-20T15:00:13Z | |
TST: method-specific files for DataFrame assign, interpolate | diff --git a/pandas/tests/frame/methods/test_assign.py b/pandas/tests/frame/methods/test_assign.py
new file mode 100644
index 0000000000000..63b9f031de188
--- /dev/null
+++ b/pandas/tests/frame/methods/test_assign.py
@@ -0,0 +1,82 @@
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+class TestAssign:
+ def test_assign(self):
+ df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+ original = df.copy()
+ result = df.assign(C=df.B / df.A)
+ expected = df.copy()
+ expected["C"] = [4, 2.5, 2]
+ tm.assert_frame_equal(result, expected)
+
+ # lambda syntax
+ result = df.assign(C=lambda x: x.B / x.A)
+ tm.assert_frame_equal(result, expected)
+
+ # original is unmodified
+ tm.assert_frame_equal(df, original)
+
+ # Non-Series array-like
+ result = df.assign(C=[4, 2.5, 2])
+ tm.assert_frame_equal(result, expected)
+ # original is unmodified
+ tm.assert_frame_equal(df, original)
+
+ result = df.assign(B=df.B / df.A)
+ expected = expected.drop("B", axis=1).rename(columns={"C": "B"})
+ tm.assert_frame_equal(result, expected)
+
+ # overwrite
+ result = df.assign(A=df.A + df.B)
+ expected = df.copy()
+ expected["A"] = [5, 7, 9]
+ tm.assert_frame_equal(result, expected)
+
+ # lambda
+ result = df.assign(A=lambda x: x.A + x.B)
+ tm.assert_frame_equal(result, expected)
+
+ def test_assign_multiple(self):
+ df = DataFrame([[1, 4], [2, 5], [3, 6]], columns=["A", "B"])
+ result = df.assign(C=[7, 8, 9], D=df.A, E=lambda x: x.B)
+ expected = DataFrame(
+ [[1, 4, 7, 1, 4], [2, 5, 8, 2, 5], [3, 6, 9, 3, 6]], columns=list("ABCDE")
+ )
+ tm.assert_frame_equal(result, expected)
+
+ def test_assign_order(self):
+ # GH 9818
+ df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
+ result = df.assign(D=df.A + df.B, C=df.A - df.B)
+
+ expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC"))
+ tm.assert_frame_equal(result, expected)
+ result = df.assign(C=df.A - df.B, D=df.A + df.B)
+
+ expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], columns=list("ABCD"))
+
+ tm.assert_frame_equal(result, expected)
+
+ def test_assign_bad(self):
+ df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+
+ # non-keyword argument
+ with pytest.raises(TypeError):
+ df.assign(lambda x: x.A)
+ with pytest.raises(AttributeError):
+ df.assign(C=df.A, D=df.A + df.C)
+
+ def test_assign_dependent(self):
+ df = DataFrame({"A": [1, 2], "B": [3, 4]})
+
+ result = df.assign(C=df.A, D=lambda x: x["A"] + x["C"])
+ expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD"))
+ tm.assert_frame_equal(result, expected)
+
+ result = df.assign(C=lambda df: df.A, D=lambda df: df["A"] + df["C"])
+ expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD"))
+ tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/frame/methods/test_interpolate.py b/pandas/tests/frame/methods/test_interpolate.py
new file mode 100644
index 0000000000000..3b8fa0dfbb603
--- /dev/null
+++ b/pandas/tests/frame/methods/test_interpolate.py
@@ -0,0 +1,286 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import DataFrame, Series, date_range
+import pandas._testing as tm
+
+
+class TestDataFrameInterpolate:
+ def test_interp_basic(self):
+ df = DataFrame(
+ {
+ "A": [1, 2, np.nan, 4],
+ "B": [1, 4, 9, np.nan],
+ "C": [1, 2, 3, 5],
+ "D": list("abcd"),
+ }
+ )
+ expected = DataFrame(
+ {
+ "A": [1.0, 2.0, 3.0, 4.0],
+ "B": [1.0, 4.0, 9.0, 9.0],
+ "C": [1, 2, 3, 5],
+ "D": list("abcd"),
+ }
+ )
+ result = df.interpolate()
+ tm.assert_frame_equal(result, expected)
+
+ result = df.set_index("C").interpolate()
+ expected = df.set_index("C")
+ expected.loc[3, "A"] = 3
+ expected.loc[5, "B"] = 9
+ tm.assert_frame_equal(result, expected)
+
+ def test_interp_bad_method(self):
+ df = DataFrame(
+ {
+ "A": [1, 2, np.nan, 4],
+ "B": [1, 4, 9, np.nan],
+ "C": [1, 2, 3, 5],
+ "D": list("abcd"),
+ }
+ )
+ with pytest.raises(ValueError):
+ df.interpolate(method="not_a_method")
+
+ def test_interp_combo(self):
+ df = DataFrame(
+ {
+ "A": [1.0, 2.0, np.nan, 4.0],
+ "B": [1, 4, 9, np.nan],
+ "C": [1, 2, 3, 5],
+ "D": list("abcd"),
+ }
+ )
+
+ result = df["A"].interpolate()
+ expected = Series([1.0, 2.0, 3.0, 4.0], name="A")
+ tm.assert_series_equal(result, expected)
+
+ result = df["A"].interpolate(downcast="infer")
+ expected = Series([1, 2, 3, 4], name="A")
+ tm.assert_series_equal(result, expected)
+
+ def test_interp_nan_idx(self):
+ df = DataFrame({"A": [1, 2, np.nan, 4], "B": [np.nan, 2, 3, 4]})
+ df = df.set_index("A")
+ with pytest.raises(NotImplementedError):
+ df.interpolate(method="values")
+
+ @td.skip_if_no_scipy
+ def test_interp_various(self):
+ df = DataFrame(
+ {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]}
+ )
+ df = df.set_index("C")
+ expected = df.copy()
+ result = df.interpolate(method="polynomial", order=1)
+
+ expected.A.loc[3] = 2.66666667
+ expected.A.loc[13] = 5.76923076
+ tm.assert_frame_equal(result, expected)
+
+ result = df.interpolate(method="cubic")
+ # GH #15662.
+ expected.A.loc[3] = 2.81547781
+ expected.A.loc[13] = 5.52964175
+ tm.assert_frame_equal(result, expected)
+
+ result = df.interpolate(method="nearest")
+ expected.A.loc[3] = 2
+ expected.A.loc[13] = 5
+ tm.assert_frame_equal(result, expected, check_dtype=False)
+
+ result = df.interpolate(method="quadratic")
+ expected.A.loc[3] = 2.82150771
+ expected.A.loc[13] = 6.12648668
+ tm.assert_frame_equal(result, expected)
+
+ result = df.interpolate(method="slinear")
+ expected.A.loc[3] = 2.66666667
+ expected.A.loc[13] = 5.76923077
+ tm.assert_frame_equal(result, expected)
+
+ result = df.interpolate(method="zero")
+ expected.A.loc[3] = 2.0
+ expected.A.loc[13] = 5
+ tm.assert_frame_equal(result, expected, check_dtype=False)
+
+ @td.skip_if_no_scipy
+ def test_interp_alt_scipy(self):
+ df = DataFrame(
+ {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]}
+ )
+ result = df.interpolate(method="barycentric")
+ expected = df.copy()
+ expected.loc[2, "A"] = 3
+ expected.loc[5, "A"] = 6
+ tm.assert_frame_equal(result, expected)
+
+ result = df.interpolate(method="barycentric", downcast="infer")
+ tm.assert_frame_equal(result, expected.astype(np.int64))
+
+ result = df.interpolate(method="krogh")
+ expectedk = df.copy()
+ expectedk["A"] = expected["A"]
+ tm.assert_frame_equal(result, expectedk)
+
+ result = df.interpolate(method="pchip")
+ expected.loc[2, "A"] = 3
+ expected.loc[5, "A"] = 6.0
+
+ tm.assert_frame_equal(result, expected)
+
+ def test_interp_rowwise(self):
+ df = DataFrame(
+ {
+ 0: [1, 2, np.nan, 4],
+ 1: [2, 3, 4, np.nan],
+ 2: [np.nan, 4, 5, 6],
+ 3: [4, np.nan, 6, 7],
+ 4: [1, 2, 3, 4],
+ }
+ )
+ result = df.interpolate(axis=1)
+ expected = df.copy()
+ expected.loc[3, 1] = 5
+ expected.loc[0, 2] = 3
+ expected.loc[1, 3] = 3
+ expected[4] = expected[4].astype(np.float64)
+ tm.assert_frame_equal(result, expected)
+
+ result = df.interpolate(axis=1, method="values")
+ tm.assert_frame_equal(result, expected)
+
+ result = df.interpolate(axis=0)
+ expected = df.interpolate()
+ tm.assert_frame_equal(result, expected)
+
+ @pytest.mark.parametrize(
+ "axis_name, axis_number",
+ [
+ pytest.param("rows", 0, id="rows_0"),
+ pytest.param("index", 0, id="index_0"),
+ pytest.param("columns", 1, id="columns_1"),
+ ],
+ )
+ def test_interp_axis_names(self, axis_name, axis_number):
+ # GH 29132: test axis names
+ data = {0: [0, np.nan, 6], 1: [1, np.nan, 7], 2: [2, 5, 8]}
+
+ df = DataFrame(data, dtype=np.float64)
+ result = df.interpolate(axis=axis_name, method="linear")
+ expected = df.interpolate(axis=axis_number, method="linear")
+ tm.assert_frame_equal(result, expected)
+
+ def test_rowwise_alt(self):
+ df = DataFrame(
+ {
+ 0: [0, 0.5, 1.0, np.nan, 4, 8, np.nan, np.nan, 64],
+ 1: [1, 2, 3, 4, 3, 2, 1, 0, -1],
+ }
+ )
+ df.interpolate(axis=0)
+ # TODO: assert something?
+
+ @pytest.mark.parametrize(
+ "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)]
+ )
+ def test_interp_leading_nans(self, check_scipy):
+ df = DataFrame(
+ {"A": [np.nan, np.nan, 0.5, 0.25, 0], "B": [np.nan, -3, -3.5, np.nan, -4]}
+ )
+ result = df.interpolate()
+ expected = df.copy()
+ expected["B"].loc[3] = -3.75
+ tm.assert_frame_equal(result, expected)
+
+ if check_scipy:
+ result = df.interpolate(method="polynomial", order=1)
+ tm.assert_frame_equal(result, expected)
+
+ def test_interp_raise_on_only_mixed(self):
+ df = DataFrame(
+ {
+ "A": [1, 2, np.nan, 4],
+ "B": ["a", "b", "c", "d"],
+ "C": [np.nan, 2, 5, 7],
+ "D": [np.nan, np.nan, 9, 9],
+ "E": [1, 2, 3, 4],
+ }
+ )
+ with pytest.raises(TypeError):
+ df.interpolate(axis=1)
+
+ def test_interp_raise_on_all_object_dtype(self):
+ # GH 22985
+ df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, dtype="object")
+ msg = (
+ "Cannot interpolate with all object-dtype columns "
+ "in the DataFrame. Try setting at least one "
+ "column to a numeric dtype."
+ )
+ with pytest.raises(TypeError, match=msg):
+ df.interpolate()
+
+ def test_interp_inplace(self):
+ df = DataFrame({"a": [1.0, 2.0, np.nan, 4.0]})
+ expected = DataFrame({"a": [1.0, 2.0, 3.0, 4.0]})
+ result = df.copy()
+ result["a"].interpolate(inplace=True)
+ tm.assert_frame_equal(result, expected)
+
+ result = df.copy()
+ result["a"].interpolate(inplace=True, downcast="infer")
+ tm.assert_frame_equal(result, expected.astype("int64"))
+
+ def test_interp_inplace_row(self):
+ # GH 10395
+ result = DataFrame(
+ {"a": [1.0, 2.0, 3.0, 4.0], "b": [np.nan, 2.0, 3.0, 4.0], "c": [3, 2, 2, 2]}
+ )
+ expected = result.interpolate(method="linear", axis=1, inplace=False)
+ result.interpolate(method="linear", axis=1, inplace=True)
+ tm.assert_frame_equal(result, expected)
+
+ def test_interp_ignore_all_good(self):
+ # GH
+ df = DataFrame(
+ {
+ "A": [1, 2, np.nan, 4],
+ "B": [1, 2, 3, 4],
+ "C": [1.0, 2.0, np.nan, 4.0],
+ "D": [1.0, 2.0, 3.0, 4.0],
+ }
+ )
+ expected = DataFrame(
+ {
+ "A": np.array([1, 2, 3, 4], dtype="float64"),
+ "B": np.array([1, 2, 3, 4], dtype="int64"),
+ "C": np.array([1.0, 2.0, 3, 4.0], dtype="float64"),
+ "D": np.array([1.0, 2.0, 3.0, 4.0], dtype="float64"),
+ }
+ )
+
+ result = df.interpolate(downcast=None)
+ tm.assert_frame_equal(result, expected)
+
+ # all good
+ result = df[["B", "D"]].interpolate(downcast=None)
+ tm.assert_frame_equal(result, df[["B", "D"]])
+
+ @pytest.mark.parametrize("axis", [0, 1])
+ def test_interp_time_inplace_axis(self, axis):
+ # GH 9687
+ periods = 5
+ idx = date_range(start="2014-01-01", periods=periods)
+ data = np.random.rand(periods, periods)
+ data[data < 0.5] = np.nan
+ expected = DataFrame(index=idx, columns=idx, data=data)
+
+ result = expected.interpolate(axis=0, method="time")
+ expected.interpolate(axis=0, method="time", inplace=True)
+ tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/frame/test_missing.py b/pandas/tests/frame/test_missing.py
index ae0516dd29a1f..196df8ba00476 100644
--- a/pandas/tests/frame/test_missing.py
+++ b/pandas/tests/frame/test_missing.py
@@ -4,8 +4,6 @@
import numpy as np
import pytest
-import pandas.util._test_decorators as td
-
import pandas as pd
from pandas import Categorical, DataFrame, Series, Timestamp, date_range
import pandas._testing as tm
@@ -705,281 +703,3 @@ def test_fill_value_when_combine_const(self):
exp = df.fillna(0).add(2)
res = df.add(2, fill_value=0)
tm.assert_frame_equal(res, exp)
-
-
-class TestDataFrameInterpolate:
- def test_interp_basic(self):
- df = DataFrame(
- {
- "A": [1, 2, np.nan, 4],
- "B": [1, 4, 9, np.nan],
- "C": [1, 2, 3, 5],
- "D": list("abcd"),
- }
- )
- expected = DataFrame(
- {
- "A": [1.0, 2.0, 3.0, 4.0],
- "B": [1.0, 4.0, 9.0, 9.0],
- "C": [1, 2, 3, 5],
- "D": list("abcd"),
- }
- )
- result = df.interpolate()
- tm.assert_frame_equal(result, expected)
-
- result = df.set_index("C").interpolate()
- expected = df.set_index("C")
- expected.loc[3, "A"] = 3
- expected.loc[5, "B"] = 9
- tm.assert_frame_equal(result, expected)
-
- def test_interp_bad_method(self):
- df = DataFrame(
- {
- "A": [1, 2, np.nan, 4],
- "B": [1, 4, 9, np.nan],
- "C": [1, 2, 3, 5],
- "D": list("abcd"),
- }
- )
- with pytest.raises(ValueError):
- df.interpolate(method="not_a_method")
-
- def test_interp_combo(self):
- df = DataFrame(
- {
- "A": [1.0, 2.0, np.nan, 4.0],
- "B": [1, 4, 9, np.nan],
- "C": [1, 2, 3, 5],
- "D": list("abcd"),
- }
- )
-
- result = df["A"].interpolate()
- expected = Series([1.0, 2.0, 3.0, 4.0], name="A")
- tm.assert_series_equal(result, expected)
-
- result = df["A"].interpolate(downcast="infer")
- expected = Series([1, 2, 3, 4], name="A")
- tm.assert_series_equal(result, expected)
-
- def test_interp_nan_idx(self):
- df = DataFrame({"A": [1, 2, np.nan, 4], "B": [np.nan, 2, 3, 4]})
- df = df.set_index("A")
- with pytest.raises(NotImplementedError):
- df.interpolate(method="values")
-
- @td.skip_if_no_scipy
- def test_interp_various(self):
- df = DataFrame(
- {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]}
- )
- df = df.set_index("C")
- expected = df.copy()
- result = df.interpolate(method="polynomial", order=1)
-
- expected.A.loc[3] = 2.66666667
- expected.A.loc[13] = 5.76923076
- tm.assert_frame_equal(result, expected)
-
- result = df.interpolate(method="cubic")
- # GH #15662.
- expected.A.loc[3] = 2.81547781
- expected.A.loc[13] = 5.52964175
- tm.assert_frame_equal(result, expected)
-
- result = df.interpolate(method="nearest")
- expected.A.loc[3] = 2
- expected.A.loc[13] = 5
- tm.assert_frame_equal(result, expected, check_dtype=False)
-
- result = df.interpolate(method="quadratic")
- expected.A.loc[3] = 2.82150771
- expected.A.loc[13] = 6.12648668
- tm.assert_frame_equal(result, expected)
-
- result = df.interpolate(method="slinear")
- expected.A.loc[3] = 2.66666667
- expected.A.loc[13] = 5.76923077
- tm.assert_frame_equal(result, expected)
-
- result = df.interpolate(method="zero")
- expected.A.loc[3] = 2.0
- expected.A.loc[13] = 5
- tm.assert_frame_equal(result, expected, check_dtype=False)
-
- @td.skip_if_no_scipy
- def test_interp_alt_scipy(self):
- df = DataFrame(
- {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]}
- )
- result = df.interpolate(method="barycentric")
- expected = df.copy()
- expected.loc[2, "A"] = 3
- expected.loc[5, "A"] = 6
- tm.assert_frame_equal(result, expected)
-
- result = df.interpolate(method="barycentric", downcast="infer")
- tm.assert_frame_equal(result, expected.astype(np.int64))
-
- result = df.interpolate(method="krogh")
- expectedk = df.copy()
- expectedk["A"] = expected["A"]
- tm.assert_frame_equal(result, expectedk)
-
- result = df.interpolate(method="pchip")
- expected.loc[2, "A"] = 3
- expected.loc[5, "A"] = 6.0
-
- tm.assert_frame_equal(result, expected)
-
- def test_interp_rowwise(self):
- df = DataFrame(
- {
- 0: [1, 2, np.nan, 4],
- 1: [2, 3, 4, np.nan],
- 2: [np.nan, 4, 5, 6],
- 3: [4, np.nan, 6, 7],
- 4: [1, 2, 3, 4],
- }
- )
- result = df.interpolate(axis=1)
- expected = df.copy()
- expected.loc[3, 1] = 5
- expected.loc[0, 2] = 3
- expected.loc[1, 3] = 3
- expected[4] = expected[4].astype(np.float64)
- tm.assert_frame_equal(result, expected)
-
- result = df.interpolate(axis=1, method="values")
- tm.assert_frame_equal(result, expected)
-
- result = df.interpolate(axis=0)
- expected = df.interpolate()
- tm.assert_frame_equal(result, expected)
-
- @pytest.mark.parametrize(
- "axis_name, axis_number",
- [
- pytest.param("rows", 0, id="rows_0"),
- pytest.param("index", 0, id="index_0"),
- pytest.param("columns", 1, id="columns_1"),
- ],
- )
- def test_interp_axis_names(self, axis_name, axis_number):
- # GH 29132: test axis names
- data = {0: [0, np.nan, 6], 1: [1, np.nan, 7], 2: [2, 5, 8]}
-
- df = DataFrame(data, dtype=np.float64)
- result = df.interpolate(axis=axis_name, method="linear")
- expected = df.interpolate(axis=axis_number, method="linear")
- tm.assert_frame_equal(result, expected)
-
- def test_rowwise_alt(self):
- df = DataFrame(
- {
- 0: [0, 0.5, 1.0, np.nan, 4, 8, np.nan, np.nan, 64],
- 1: [1, 2, 3, 4, 3, 2, 1, 0, -1],
- }
- )
- df.interpolate(axis=0)
-
- @pytest.mark.parametrize(
- "check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)]
- )
- def test_interp_leading_nans(self, check_scipy):
- df = DataFrame(
- {"A": [np.nan, np.nan, 0.5, 0.25, 0], "B": [np.nan, -3, -3.5, np.nan, -4]}
- )
- result = df.interpolate()
- expected = df.copy()
- expected["B"].loc[3] = -3.75
- tm.assert_frame_equal(result, expected)
-
- if check_scipy:
- result = df.interpolate(method="polynomial", order=1)
- tm.assert_frame_equal(result, expected)
-
- def test_interp_raise_on_only_mixed(self):
- df = DataFrame(
- {
- "A": [1, 2, np.nan, 4],
- "B": ["a", "b", "c", "d"],
- "C": [np.nan, 2, 5, 7],
- "D": [np.nan, np.nan, 9, 9],
- "E": [1, 2, 3, 4],
- }
- )
- with pytest.raises(TypeError):
- df.interpolate(axis=1)
-
- def test_interp_raise_on_all_object_dtype(self):
- # GH 22985
- df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, dtype="object")
- msg = (
- "Cannot interpolate with all object-dtype columns "
- "in the DataFrame. Try setting at least one "
- "column to a numeric dtype."
- )
- with pytest.raises(TypeError, match=msg):
- df.interpolate()
-
- def test_interp_inplace(self):
- df = DataFrame({"a": [1.0, 2.0, np.nan, 4.0]})
- expected = DataFrame({"a": [1.0, 2.0, 3.0, 4.0]})
- result = df.copy()
- result["a"].interpolate(inplace=True)
- tm.assert_frame_equal(result, expected)
-
- result = df.copy()
- result["a"].interpolate(inplace=True, downcast="infer")
- tm.assert_frame_equal(result, expected.astype("int64"))
-
- def test_interp_inplace_row(self):
- # GH 10395
- result = DataFrame(
- {"a": [1.0, 2.0, 3.0, 4.0], "b": [np.nan, 2.0, 3.0, 4.0], "c": [3, 2, 2, 2]}
- )
- expected = result.interpolate(method="linear", axis=1, inplace=False)
- result.interpolate(method="linear", axis=1, inplace=True)
- tm.assert_frame_equal(result, expected)
-
- def test_interp_ignore_all_good(self):
- # GH
- df = DataFrame(
- {
- "A": [1, 2, np.nan, 4],
- "B": [1, 2, 3, 4],
- "C": [1.0, 2.0, np.nan, 4.0],
- "D": [1.0, 2.0, 3.0, 4.0],
- }
- )
- expected = DataFrame(
- {
- "A": np.array([1, 2, 3, 4], dtype="float64"),
- "B": np.array([1, 2, 3, 4], dtype="int64"),
- "C": np.array([1.0, 2.0, 3, 4.0], dtype="float64"),
- "D": np.array([1.0, 2.0, 3.0, 4.0], dtype="float64"),
- }
- )
-
- result = df.interpolate(downcast=None)
- tm.assert_frame_equal(result, expected)
-
- # all good
- result = df[["B", "D"]].interpolate(downcast=None)
- tm.assert_frame_equal(result, df[["B", "D"]])
-
- @pytest.mark.parametrize("axis", [0, 1])
- def test_interp_time_inplace_axis(self, axis):
- # GH 9687
- periods = 5
- idx = pd.date_range(start="2014-01-01", periods=periods)
- data = np.random.rand(periods, periods)
- data[data < 0.5] = np.nan
- expected = pd.DataFrame(index=idx, columns=idx, data=data)
-
- result = expected.interpolate(axis=0, method="time")
- expected.interpolate(axis=0, method="time", inplace=True)
- tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/frame/test_mutate_columns.py b/pandas/tests/frame/test_mutate_columns.py
index 8bc2aa214e035..33f71602f4713 100644
--- a/pandas/tests/frame/test_mutate_columns.py
+++ b/pandas/tests/frame/test_mutate_columns.py
@@ -10,82 +10,6 @@
class TestDataFrameMutateColumns:
- def test_assign(self):
- df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
- original = df.copy()
- result = df.assign(C=df.B / df.A)
- expected = df.copy()
- expected["C"] = [4, 2.5, 2]
- tm.assert_frame_equal(result, expected)
-
- # lambda syntax
- result = df.assign(C=lambda x: x.B / x.A)
- tm.assert_frame_equal(result, expected)
-
- # original is unmodified
- tm.assert_frame_equal(df, original)
-
- # Non-Series array-like
- result = df.assign(C=[4, 2.5, 2])
- tm.assert_frame_equal(result, expected)
- # original is unmodified
- tm.assert_frame_equal(df, original)
-
- result = df.assign(B=df.B / df.A)
- expected = expected.drop("B", axis=1).rename(columns={"C": "B"})
- tm.assert_frame_equal(result, expected)
-
- # overwrite
- result = df.assign(A=df.A + df.B)
- expected = df.copy()
- expected["A"] = [5, 7, 9]
- tm.assert_frame_equal(result, expected)
-
- # lambda
- result = df.assign(A=lambda x: x.A + x.B)
- tm.assert_frame_equal(result, expected)
-
- def test_assign_multiple(self):
- df = DataFrame([[1, 4], [2, 5], [3, 6]], columns=["A", "B"])
- result = df.assign(C=[7, 8, 9], D=df.A, E=lambda x: x.B)
- expected = DataFrame(
- [[1, 4, 7, 1, 4], [2, 5, 8, 2, 5], [3, 6, 9, 3, 6]], columns=list("ABCDE")
- )
- tm.assert_frame_equal(result, expected)
-
- def test_assign_order(self):
- # GH 9818
- df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
- result = df.assign(D=df.A + df.B, C=df.A - df.B)
-
- expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC"))
- tm.assert_frame_equal(result, expected)
- result = df.assign(C=df.A - df.B, D=df.A + df.B)
-
- expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], columns=list("ABCD"))
-
- tm.assert_frame_equal(result, expected)
-
- def test_assign_bad(self):
- df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
-
- # non-keyword argument
- with pytest.raises(TypeError):
- df.assign(lambda x: x.A)
- with pytest.raises(AttributeError):
- df.assign(C=df.A, D=df.A + df.C)
-
- def test_assign_dependent(self):
- df = DataFrame({"A": [1, 2], "B": [3, 4]})
-
- result = df.assign(C=df.A, D=lambda x: x["A"] + x["C"])
- expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD"))
- tm.assert_frame_equal(result, expected)
-
- result = df.assign(C=lambda df: df.A, D=lambda df: df["A"] + df["C"])
- expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD"))
- tm.assert_frame_equal(result, expected)
-
def test_insert_error_msmgs(self):
# GH 7432
| https://api.github.com/repos/pandas-dev/pandas/pulls/32110 | 2020-02-19T16:05:16Z | 2020-02-22T16:03:21Z | 2020-02-22T16:03:21Z | 2020-02-22T16:48:55Z | |
BUG: Allow addition of Timedelta to Timestamp interval | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index cd1cb0b64f74a..8a05d0d36cd2f 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -525,6 +525,7 @@ Indexing
- Bug in :class:`Index` constructor where an unhelpful error message was raised for ``numpy`` scalars (:issue:`33017`)
- Bug in :meth:`DataFrame.lookup` incorrectly raising an ``AttributeError`` when ``frame.index`` or ``frame.columns`` is not unique; this will now raise a ``ValueError`` with a helpful error message (:issue:`33041`)
- Bug in :meth:`DataFrame.iloc.__setitem__` creating a new array instead of overwriting ``Categorical`` values in-place (:issue:`32831`)
+- Bug in :class:`Interval` where a :class:`Timedelta` could not be added or subtracted from a :class:`Timestamp` interval (:issue:`32023`)
- Bug in :meth:`DataFrame.copy` _item_cache not invalidated after copy causes post-copy value updates to not be reflected (:issue:`31784`)
- Bug in `Series.__getitem__` with an integer key and a :class:`MultiIndex` with leading integer level failing to raise ``KeyError`` if the key is not present in the first level (:issue:`33355`)
- Bug in :meth:`DataFrame.iloc` when slicing a single column-:class:`DataFrame`` with ``ExtensionDtype`` (e.g. ``df.iloc[:, :1]``) returning an invalid result (:issue:`32957`)
diff --git a/pandas/_libs/interval.pyx b/pandas/_libs/interval.pyx
index a47303ddc93cf..e52474e233510 100644
--- a/pandas/_libs/interval.pyx
+++ b/pandas/_libs/interval.pyx
@@ -1,6 +1,9 @@
import numbers
from operator import le, lt
+from cpython.datetime cimport PyDelta_Check, PyDateTime_IMPORT
+PyDateTime_IMPORT
+
from cpython.object cimport (
Py_EQ,
Py_GE,
@@ -11,7 +14,6 @@ from cpython.object cimport (
PyObject_RichCompare,
)
-
import cython
from cython import Py_ssize_t
@@ -34,7 +36,11 @@ cnp.import_array()
cimport pandas._libs.util as util
from pandas._libs.hashtable cimport Int64Vector
-from pandas._libs.tslibs.util cimport is_integer_object, is_float_object
+from pandas._libs.tslibs.util cimport (
+ is_integer_object,
+ is_float_object,
+ is_timedelta64_object,
+)
from pandas._libs.tslibs import Timestamp
from pandas._libs.tslibs.timedeltas import Timedelta
@@ -294,6 +300,7 @@ cdef class Interval(IntervalMixin):
True
"""
_typ = "interval"
+ __array_priority__ = 1000
cdef readonly object left
"""
@@ -398,14 +405,29 @@ cdef class Interval(IntervalMixin):
return f'{start_symbol}{left}, {right}{end_symbol}'
def __add__(self, y):
- if isinstance(y, numbers.Number):
+ if (
+ isinstance(y, numbers.Number)
+ or PyDelta_Check(y)
+ or is_timedelta64_object(y)
+ ):
return Interval(self.left + y, self.right + y, closed=self.closed)
- elif isinstance(y, Interval) and isinstance(self, numbers.Number):
+ elif (
+ isinstance(y, Interval)
+ and (
+ isinstance(self, numbers.Number)
+ or PyDelta_Check(self)
+ or is_timedelta64_object(self)
+ )
+ ):
return Interval(y.left + self, y.right + self, closed=y.closed)
return NotImplemented
def __sub__(self, y):
- if isinstance(y, numbers.Number):
+ if (
+ isinstance(y, numbers.Number)
+ or PyDelta_Check(y)
+ or is_timedelta64_object(y)
+ ):
return Interval(self.left - y, self.right - y, closed=self.closed)
return NotImplemented
diff --git a/pandas/tests/scalar/interval/test_arithmetic.py b/pandas/tests/scalar/interval/test_arithmetic.py
new file mode 100644
index 0000000000000..5252f1a4d5a24
--- /dev/null
+++ b/pandas/tests/scalar/interval/test_arithmetic.py
@@ -0,0 +1,47 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+from pandas import Interval, Timedelta, Timestamp
+
+
+@pytest.mark.parametrize("method", ["__add__", "__sub__"])
+@pytest.mark.parametrize(
+ "interval",
+ [
+ Interval(Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")),
+ Interval(Timedelta(days=7), Timedelta(days=14)),
+ ],
+)
+@pytest.mark.parametrize(
+ "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")]
+)
+def test_time_interval_add_subtract_timedelta(interval, delta, method):
+ # https://github.com/pandas-dev/pandas/issues/32023
+ result = getattr(interval, method)(delta)
+ left = getattr(interval.left, method)(delta)
+ right = getattr(interval.right, method)(delta)
+ expected = Interval(left, right)
+
+ assert result == expected
+
+
+@pytest.mark.parametrize("interval", [Interval(1, 2), Interval(1.0, 2.0)])
+@pytest.mark.parametrize(
+ "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")]
+)
+def test_numeric_interval_add_timedelta_raises(interval, delta):
+ # https://github.com/pandas-dev/pandas/issues/32023
+ msg = "|".join(
+ [
+ "unsupported operand",
+ "cannot use operands",
+ "Only numeric, Timestamp and Timedelta endpoints are allowed",
+ ]
+ )
+ with pytest.raises((TypeError, ValueError), match=msg):
+ interval + delta
+
+ with pytest.raises((TypeError, ValueError), match=msg):
+ delta + interval
| - [x] closes #32023
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32107 | 2020-02-19T14:53:31Z | 2020-04-25T23:58:52Z | 2020-04-25T23:58:52Z | 2020-04-26T00:06:54Z |
Added in a error message | diff --git a/pandas/tests/arrays/test_array.py b/pandas/tests/arrays/test_array.py
index b1b5a9482e34f..f42b16cf18f20 100644
--- a/pandas/tests/arrays/test_array.py
+++ b/pandas/tests/arrays/test_array.py
@@ -291,7 +291,7 @@ class DecimalArray2(DecimalArray):
@classmethod
def _from_sequence(cls, scalars, dtype=None, copy=False):
if isinstance(scalars, (pd.Series, pd.Index)):
- raise TypeError
+ raise TypeError("scalars should not be of type pd.Series or pd.Index")
return super()._from_sequence(scalars, dtype=dtype, copy=copy)
@@ -301,7 +301,9 @@ def test_array_unboxes(index_or_series):
data = box([decimal.Decimal("1"), decimal.Decimal("2")])
# make sure it works
- with pytest.raises(TypeError):
+ with pytest.raises(
+ TypeError, match="scalars should not be of type pd.Series or pd.Index"
+ ):
DecimalArray2._from_sequence(data)
result = pd.array(data, dtype="decimal2")
| - [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] Added in an error message
| https://api.github.com/repos/pandas-dev/pandas/pulls/32105 | 2020-02-19T14:01:05Z | 2020-02-27T19:31:58Z | 2020-02-27T19:31:57Z | 2020-02-27T21:00:45Z |
BUG: Pickle NA objects | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 1b6098e6b6ac1..808e6ae709ce9 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -74,6 +74,7 @@ Bug fixes
**I/O**
- Using ``pd.NA`` with :meth:`DataFrame.to_json` now correctly outputs a null value instead of an empty object (:issue:`31615`)
+- Fixed pickling of ``pandas.NA``. Previously a new object was returned, which broke computations relying on ``NA`` being a singleton (:issue:`31847`)
- Fixed bug in parquet roundtrip with nullable unsigned integer dtypes (:issue:`31896`).
**Experimental dtypes**
diff --git a/pandas/_libs/missing.pyx b/pandas/_libs/missing.pyx
index 4d17a6f883c1c..c54cb652d7b21 100644
--- a/pandas/_libs/missing.pyx
+++ b/pandas/_libs/missing.pyx
@@ -364,6 +364,9 @@ class NAType(C_NAType):
exponent = 31 if is_32bit else 61
return 2 ** exponent - 1
+ def __reduce__(self):
+ return "NA"
+
# Binary arithmetic and comparison ops -> propagate
__add__ = _create_binary_propagating_op("__add__")
diff --git a/pandas/tests/scalar/test_na_scalar.py b/pandas/tests/scalar/test_na_scalar.py
index dcb9d66708724..07656de2e9062 100644
--- a/pandas/tests/scalar/test_na_scalar.py
+++ b/pandas/tests/scalar/test_na_scalar.py
@@ -1,3 +1,5 @@
+import pickle
+
import numpy as np
import pytest
@@ -267,3 +269,26 @@ def test_integer_hash_collision_set():
assert len(result) == 2
assert NA in result
assert hash(NA) in result
+
+
+def test_pickle_roundtrip():
+ # https://github.com/pandas-dev/pandas/issues/31847
+ result = pickle.loads(pickle.dumps(pd.NA))
+ assert result is pd.NA
+
+
+def test_pickle_roundtrip_pandas():
+ result = tm.round_trip_pickle(pd.NA)
+ assert result is pd.NA
+
+
+@pytest.mark.parametrize(
+ "values, dtype", [([1, 2, pd.NA], "Int64"), (["A", "B", pd.NA], "string")]
+)
+@pytest.mark.parametrize("as_frame", [True, False])
+def test_pickle_roundtrip_containers(as_frame, values, dtype):
+ s = pd.Series(pd.array(values, dtype=dtype))
+ if as_frame:
+ s = s.to_frame(name="A")
+ result = tm.round_trip_pickle(s)
+ tm.assert_equal(result, s)
| According to
https://docs.python.org/3/library/pickle.html#object.__reduce__,
> If a string is returned, the string should be interpreted as the name
> of a global variable. It should be the object’s local name relative to
> its module; the pickle module searches the module namespace to determine
> the object’s module. This behaviour is typically useful for singletons.
Closes https://github.com/pandas-dev/pandas/issues/31847 | https://api.github.com/repos/pandas-dev/pandas/pulls/32104 | 2020-02-19T13:46:22Z | 2020-03-02T23:13:33Z | 2020-03-02T23:13:33Z | 2020-03-03T12:44:06Z |
added msg to TypeError test_to_boolean_array_error | diff --git a/pandas/tests/arrays/test_boolean.py b/pandas/tests/arrays/test_boolean.py
index cb9b07db4a0df..d14d6f3ff0c41 100644
--- a/pandas/tests/arrays/test_boolean.py
+++ b/pandas/tests/arrays/test_boolean.py
@@ -131,7 +131,8 @@ def test_to_boolean_array_missing_indicators(a, b):
)
def test_to_boolean_array_error(values):
# error in converting existing arrays to BooleanArray
- with pytest.raises(TypeError):
+ msg = "Need to pass bool-like value"
+ with pytest.raises(TypeError, match=msg):
pd.array(values, dtype="boolean")
| - [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Fixed bare pytest.raises (#30999) for test_to_boolean_array_error in test_boolean.py
| https://api.github.com/repos/pandas-dev/pandas/pulls/32103 | 2020-02-19T12:52:58Z | 2020-02-20T15:01:13Z | 2020-02-20T15:01:13Z | 2020-02-25T10:48:57Z |
TST: Fix bare pytest.raises in test_parsing.py | diff --git a/pandas/tests/tslibs/test_parsing.py b/pandas/tests/tslibs/test_parsing.py
index 1ba7832c47e6c..dc7421ea63464 100644
--- a/pandas/tests/tslibs/test_parsing.py
+++ b/pandas/tests/tslibs/test_parsing.py
@@ -2,6 +2,7 @@
Tests for Timestamp parsing, aimed at pandas/_libs/tslibs/parsing.pyx
"""
from datetime import datetime
+import re
from dateutil.parser import parse
import numpy as np
@@ -24,7 +25,8 @@ def test_parse_time_string():
def test_parse_time_string_invalid_type():
# Raise on invalid input, don't just return it
- with pytest.raises(TypeError):
+ msg = "Argument 'arg' has incorrect type (expected str, got tuple)"
+ with pytest.raises(TypeError, match=re.escape(msg)):
parse_time_string((4, 5))
@@ -217,7 +219,8 @@ def test_try_parse_dates():
def test_parse_time_string_check_instance_type_raise_exception():
# issue 20684
- with pytest.raises(TypeError):
+ msg = "Argument 'arg' has incorrect type (expected str, got tuple)"
+ with pytest.raises(TypeError, match=re.escape(msg)):
parse_time_string((1, 2, 3))
result = parse_time_string("2019")
| - [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Fixes bare pytest.raises issue for `test_parsing.py`. | https://api.github.com/repos/pandas-dev/pandas/pulls/32102 | 2020-02-19T12:52:56Z | 2020-02-20T16:39:44Z | 2020-02-20T16:39:44Z | 2020-02-20T16:39:53Z |
TYP: check_untyped_defs core.tools.datetimes | diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py
index 6d45ddd29d783..b10b736b9134e 100644
--- a/pandas/core/tools/datetimes.py
+++ b/pandas/core/tools/datetimes.py
@@ -2,7 +2,7 @@
from datetime import datetime, time
from functools import partial
from itertools import islice
-from typing import Optional, TypeVar, Union
+from typing import List, Optional, TypeVar, Union
import numpy as np
@@ -296,7 +296,9 @@ def _convert_listlike_datetimes(
if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
return DatetimeIndex(arg, tz=tz, name=name)
if tz == "utc":
- arg = arg.tz_convert(None).tz_localize(tz)
+ # error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has
+ # no attribute "tz_convert"
+ arg = arg.tz_convert(None).tz_localize(tz) # type: ignore
return arg
elif is_datetime64_ns_dtype(arg):
@@ -307,7 +309,9 @@ def _convert_listlike_datetimes(
pass
elif tz:
# DatetimeArray, DatetimeIndex
- return arg.tz_localize(tz)
+ # error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has
+ # no attribute "tz_localize"
+ return arg.tz_localize(tz) # type: ignore
return arg
@@ -826,18 +830,18 @@ def f(value):
required = ["year", "month", "day"]
req = sorted(set(required) - set(unit_rev.keys()))
if len(req):
- required = ",".join(req)
+ _required = ",".join(req)
raise ValueError(
"to assemble mappings requires at least that "
- f"[year, month, day] be specified: [{required}] is missing"
+ f"[year, month, day] be specified: [{_required}] is missing"
)
# keys we don't recognize
excess = sorted(set(unit_rev.keys()) - set(_unit_map.values()))
if len(excess):
- excess = ",".join(excess)
+ _excess = ",".join(excess)
raise ValueError(
- f"extra keys have been passed to the datetime assemblage: [{excess}]"
+ f"extra keys have been passed to the datetime assemblage: [{_excess}]"
)
def coerce(values):
@@ -992,7 +996,7 @@ def _convert_listlike(arg, format):
if infer_time_format and format is None:
format = _guess_time_format_for_array(arg)
- times = []
+ times: List[Optional[time]] = []
if format is not None:
for element in arg:
try:
diff --git a/setup.cfg b/setup.cfg
index 4a900e581c353..32efec7d2cf0d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -234,9 +234,6 @@ check_untyped_defs=False
[mypy-pandas.core.strings]
check_untyped_defs=False
-[mypy-pandas.core.tools.datetimes]
-check_untyped_defs=False
-
[mypy-pandas.core.window.common]
check_untyped_defs=False
| pandas\core\tools\datetimes.py:299: error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has no attribute "tz_convert"
pandas\core\tools\datetimes.py:310: error: Item "DatetimeIndex" of "Union[DatetimeArray, DatetimeIndex]" has no attribute "tz_localize"
pandas\core\tools\datetimes.py:829: error: Incompatible types in assignment (expression has type "str", variable has type "List[str]")
pandas\core\tools\datetimes.py:838: error: Incompatible types in assignment (expression has type "str", variable has type "List[Any]")
pandas\core\tools\datetimes.py:1010: error: Argument 1 to "append" of "list" has incompatible type "None"; expected "time"
pandas\core\tools\datetimes.py:1035: error: Argument 1 to "append" of "list" has incompatible type "None"; expected "time" | https://api.github.com/repos/pandas-dev/pandas/pulls/32101 | 2020-02-19T12:33:10Z | 2020-02-20T01:24:38Z | 2020-02-20T01:24:38Z | 2020-02-20T09:40:16Z |
TYP: check_untyped_defs arrays.sparse.array | diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index b17a4647ffc9f..542cfd334b810 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -4,7 +4,7 @@
from collections import abc
import numbers
import operator
-from typing import Any, Callable
+from typing import Any, Callable, Union
import warnings
import numpy as np
@@ -479,7 +479,7 @@ def sp_index(self):
return self._sparse_index
@property
- def sp_values(self):
+ def sp_values(self) -> np.ndarray:
"""
An ndarray containing the non- ``fill_value`` values.
@@ -798,13 +798,13 @@ def _get_val_at(self, loc):
val = com.maybe_box_datetimelike(val, self.sp_values.dtype)
return val
- def take(self, indices, allow_fill=False, fill_value=None):
+ def take(self, indices, allow_fill=False, fill_value=None) -> "SparseArray":
if is_scalar(indices):
raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.")
indices = np.asarray(indices, dtype=np.int32)
if indices.size == 0:
- result = []
+ result = np.array([], dtype="object")
kwargs = {"dtype": self.dtype}
elif allow_fill:
result = self._take_with_fill(indices, fill_value=fill_value)
@@ -815,7 +815,7 @@ def take(self, indices, allow_fill=False, fill_value=None):
return type(self)(result, fill_value=self.fill_value, kind=self.kind, **kwargs)
- def _take_with_fill(self, indices, fill_value=None):
+ def _take_with_fill(self, indices, fill_value=None) -> np.ndarray:
if fill_value is None:
fill_value = self.dtype.na_value
@@ -878,7 +878,7 @@ def _take_with_fill(self, indices, fill_value=None):
return taken
- def _take_without_fill(self, indices):
+ def _take_without_fill(self, indices) -> Union[np.ndarray, "SparseArray"]:
to_shift = indices < 0
indices = indices.copy()
diff --git a/setup.cfg b/setup.cfg
index 87054f864813f..61d5b1030a500 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -150,9 +150,6 @@ check_untyped_defs=False
[mypy-pandas.core.arrays.interval]
check_untyped_defs=False
-[mypy-pandas.core.arrays.sparse.array]
-check_untyped_defs=False
-
[mypy-pandas.core.base]
check_untyped_defs=False
| pandas\core\arrays\sparse\array.py:807: error: Need type annotation for 'result' (hint: "result: List[<type>] = ...") | https://api.github.com/repos/pandas-dev/pandas/pulls/32099 | 2020-02-19T11:51:01Z | 2020-02-20T22:37:25Z | 2020-02-20T22:37:25Z | 2020-02-21T08:16:55Z |
TYP: check_untyped_defs core.arrays.categorical | diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index d469b574820f9..a5048e3aae899 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -341,10 +341,7 @@ def __init__(
values = _convert_to_list_like(values)
# By convention, empty lists result in object dtype:
- if len(values) == 0:
- sanitize_dtype = "object"
- else:
- sanitize_dtype = None
+ sanitize_dtype = "object" if len(values) == 0 else None
null_mask = isna(values)
if null_mask.any():
values = [values[idx] for idx in np.where(~null_mask)[0]]
@@ -1496,7 +1493,7 @@ def check_for_ordered(self, op):
def _values_for_argsort(self):
return self._codes.copy()
- def argsort(self, ascending=True, kind="quicksort", *args, **kwargs):
+ def argsort(self, ascending=True, kind="quicksort", **kwargs):
"""
Return the indices that would sort the Categorical.
@@ -1511,7 +1508,7 @@ def argsort(self, ascending=True, kind="quicksort", *args, **kwargs):
or descending sort.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm.
- *args, **kwargs:
+ **kwargs:
passed through to :func:`numpy.argsort`.
Returns
@@ -1547,7 +1544,7 @@ def argsort(self, ascending=True, kind="quicksort", *args, **kwargs):
>>> cat.argsort()
array([2, 0, 1])
"""
- return super().argsort(ascending=ascending, kind=kind, *args, **kwargs)
+ return super().argsort(ascending=ascending, kind=kind, **kwargs)
def sort_values(self, inplace=False, ascending=True, na_position="last"):
"""
diff --git a/setup.cfg b/setup.cfg
index 4a900e581c353..4b0b32b09ca8d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -147,9 +147,6 @@ check_untyped_defs=False
[mypy-pandas._version]
check_untyped_defs=False
-[mypy-pandas.core.arrays.categorical]
-check_untyped_defs=False
-
[mypy-pandas.core.arrays.interval]
check_untyped_defs=False
| pandas\core\arrays\categorical.py:347: error: Incompatible types in assignment (expression has type "None", variable has type "str")
pandas\core\arrays\categorical.py:1550: error: "argsort" of "ExtensionArray" gets multiple values for keyword argument "ascending"
pandas\core\arrays\categorical.py:1550: error: "argsort" of "ExtensionArray" gets multiple values for keyword argument "kind" | https://api.github.com/repos/pandas-dev/pandas/pulls/32097 | 2020-02-19T11:22:04Z | 2020-02-19T22:29:57Z | 2020-02-19T22:29:57Z | 2020-02-20T09:38:34Z |
Backport PR #31841: BUG: Fix rolling.corr with time frequency | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 1622a6dc8ce56..19358689a2186 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -18,6 +18,7 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`)
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
+- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index 3d5cee3bd5a31..b7e1779a08562 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -1782,7 +1782,7 @@ def corr(self, other=None, pairwise=None, **kwargs):
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy(other)
- window = self._get_window(other)
+ window = self._get_window(other) if not self.is_freq_type else self.win_freq
def _get_corr(a, b):
a = a.rolling(
diff --git a/pandas/tests/window/test_pairwise.py b/pandas/tests/window/test_pairwise.py
index 717273cff64ea..bb305e93a3cf1 100644
--- a/pandas/tests/window/test_pairwise.py
+++ b/pandas/tests/window/test_pairwise.py
@@ -1,8 +1,9 @@
import warnings
+import numpy as np
import pytest
-from pandas import DataFrame, Series
+from pandas import DataFrame, Series, date_range
import pandas._testing as tm
from pandas.core.algorithms import safe_sort
@@ -181,3 +182,10 @@ def test_pairwise_with_series(self, f):
for i, result in enumerate(results):
if i > 0:
self.compare(result, results[0])
+
+ def test_corr_freq_memory_error(self):
+ # GH 31789
+ s = Series(range(5), index=date_range("2020", periods=5))
+ result = s.rolling("12H").corr(s)
+ expected = Series([np.nan] * 5, index=date_range("2020", periods=5))
+ tm.assert_series_equal(result, expected)
| https://github.com/pandas-dev/pandas/pull/31841 | https://api.github.com/repos/pandas-dev/pandas/pulls/32094 | 2020-02-19T07:40:21Z | 2020-02-19T09:58:57Z | 2020-02-19T09:58:57Z | 2020-02-19T10:01:25Z |
Backport PR #31842 on branch 1.0.x (BUG: Fix raw parameter not being respected in groupby.rolling.apply) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 805f87b63eef8..1622a6dc8ce56 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -17,6 +17,7 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`)
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
+- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index c1e34757b45d4..3d5cee3bd5a31 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -1296,13 +1296,14 @@ def apply(
raise ValueError("engine must be either 'numba' or 'cython'")
# TODO: Why do we always pass center=False?
- # name=func for WindowGroupByMixin._apply
+ # name=func & raw=raw for WindowGroupByMixin._apply
return self._apply(
apply_func,
center=False,
floor=0,
name=func,
use_numba_cache=engine == "numba",
+ raw=raw,
)
def _generate_cython_apply_func(self, args, kwargs, raw, offset, func):
diff --git a/pandas/tests/window/test_grouper.py b/pandas/tests/window/test_grouper.py
index 355ef3a90d424..5b2687271f9d6 100644
--- a/pandas/tests/window/test_grouper.py
+++ b/pandas/tests/window/test_grouper.py
@@ -190,3 +190,21 @@ def test_expanding_apply(self, raw):
result = r.apply(lambda x: x.sum(), raw=raw)
expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw))
tm.assert_frame_equal(result, expected)
+
+ @pytest.mark.parametrize("expected_value,raw_value", [[1.0, True], [0.0, False]])
+ def test_groupby_rolling(self, expected_value, raw_value):
+ # GH 31754
+
+ def foo(x):
+ return int(isinstance(x, np.ndarray))
+
+ df = pd.DataFrame({"id": [1, 1, 1], "value": [1, 2, 3]})
+ result = df.groupby("id").value.rolling(1).apply(foo, raw=raw_value)
+ expected = Series(
+ [expected_value] * 3,
+ index=pd.MultiIndex.from_tuples(
+ ((1, 0), (1, 1), (1, 2)), names=["id", None]
+ ),
+ name="value",
+ )
+ tm.assert_series_equal(result, expected)
| Backport PR #31842: BUG: Fix raw parameter not being respected in groupby.rolling.apply | https://api.github.com/repos/pandas-dev/pandas/pulls/32093 | 2020-02-19T07:35:30Z | 2020-02-19T08:15:47Z | 2020-02-19T08:15:47Z | 2020-02-19T08:15:47Z |
TST: parametrize and de-duplicate timedelta64 arithmetic tests | diff --git a/pandas/tests/arithmetic/test_timedelta64.py b/pandas/tests/arithmetic/test_timedelta64.py
index abdeb1b30b626..300e468c34e65 100644
--- a/pandas/tests/arithmetic/test_timedelta64.py
+++ b/pandas/tests/arithmetic/test_timedelta64.py
@@ -25,6 +25,19 @@
get_upcast_box,
)
+
+def assert_dtype(obj, expected_dtype):
+ """
+ Helper to check the dtype for a Series, Index, or single-column DataFrame.
+ """
+ if isinstance(obj, DataFrame):
+ dtype = obj.dtypes.iat[0]
+ else:
+ dtype = obj.dtype
+
+ assert dtype == expected_dtype
+
+
# ------------------------------------------------------------------
# Timedelta64[ns] dtype Comparisons
@@ -522,19 +535,35 @@ def test_tda_add_sub_index(self):
# -------------------------------------------------------------
# Binary operations TimedeltaIndex and timedelta-like
- def test_tdi_iadd_timedeltalike(self, two_hours):
+ def test_tdi_iadd_timedeltalike(self, two_hours, box_with_array):
# only test adding/sub offsets as + is now numeric
rng = timedelta_range("1 days", "10 days")
expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D")
+
+ rng = tm.box_expected(rng, box_with_array)
+ expected = tm.box_expected(expected, box_with_array)
+
+ orig_rng = rng
rng += two_hours
- tm.assert_index_equal(rng, expected)
+ tm.assert_equal(rng, expected)
+ if box_with_array is not pd.Index:
+ # Check that operation is actually inplace
+ tm.assert_equal(orig_rng, expected)
- def test_tdi_isub_timedeltalike(self, two_hours):
+ def test_tdi_isub_timedeltalike(self, two_hours, box_with_array):
# only test adding/sub offsets as - is now numeric
rng = timedelta_range("1 days", "10 days")
expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00")
+
+ rng = tm.box_expected(rng, box_with_array)
+ expected = tm.box_expected(expected, box_with_array)
+
+ orig_rng = rng
rng -= two_hours
- tm.assert_index_equal(rng, expected)
+ tm.assert_equal(rng, expected)
+ if box_with_array is not pd.Index:
+ # Check that operation is actually inplace
+ tm.assert_equal(orig_rng, expected)
# -------------------------------------------------------------
@@ -1013,15 +1042,6 @@ def test_td64arr_add_datetime64_nat(self, box_with_array):
# ------------------------------------------------------------------
# Invalid __add__/__sub__ operations
- # TODO: moved from frame tests; needs parametrization/de-duplication
- def test_td64_df_add_int_frame(self):
- # GH#22696 Check that we don't dispatch to numpy implementation,
- # which treats int64 as m8[ns]
- tdi = pd.timedelta_range("1", periods=3)
- df = tdi.to_frame()
- other = pd.DataFrame([1, 2, 3], index=tdi) # indexed like `df`
- assert_invalid_addsub_type(df, other)
-
@pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "H"])
@pytest.mark.parametrize("tdi_freq", [None, "H"])
def test_td64arr_sub_periodlike(self, box_with_array, tdi_freq, pi_freq):
@@ -1100,6 +1120,9 @@ def test_td64arr_add_sub_int(self, box_with_array, one):
def test_td64arr_add_sub_integer_array(self, box_with_array):
# GH#19959, deprecated GH#22535
+ # GH#22696 for DataFrame case, check that we don't dispatch to numpy
+ # implementation, which treats int64 as m8[ns]
+
rng = timedelta_range("1 days 09:00:00", freq="H", periods=3)
tdarr = tm.box_expected(rng, box_with_array)
other = tm.box_expected([4, 3, 2], box_with_array)
@@ -1119,60 +1142,6 @@ def test_td64arr_addsub_integer_array_no_freq(self, box_with_array):
# ------------------------------------------------------------------
# Operations with timedelta-like others
- # TODO: this was taken from tests.series.test_ops; de-duplicate
- def test_operators_timedelta64_with_timedelta(self, scalar_td):
- # smoke tests
- td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
- td1.iloc[2] = np.nan
-
- td1 + scalar_td
- scalar_td + td1
- td1 - scalar_td
- scalar_td - td1
- td1 / scalar_td
- scalar_td / td1
-
- # TODO: this was taken from tests.series.test_ops; de-duplicate
- def test_timedelta64_operations_with_timedeltas(self):
- # td operate with td
- td1 = Series([timedelta(minutes=5, seconds=3)] * 3)
- td2 = timedelta(minutes=5, seconds=4)
- result = td1 - td2
- expected = Series([timedelta(seconds=0)] * 3) - Series(
- [timedelta(seconds=1)] * 3
- )
- assert result.dtype == "m8[ns]"
- tm.assert_series_equal(result, expected)
-
- result2 = td2 - td1
- expected = Series([timedelta(seconds=1)] * 3) - Series(
- [timedelta(seconds=0)] * 3
- )
- tm.assert_series_equal(result2, expected)
-
- # roundtrip
- tm.assert_series_equal(result + td2, td1)
-
- # Now again, using pd.to_timedelta, which should build
- # a Series or a scalar, depending on input.
- td1 = Series(pd.to_timedelta(["00:05:03"] * 3))
- td2 = pd.to_timedelta("00:05:04")
- result = td1 - td2
- expected = Series([timedelta(seconds=0)] * 3) - Series(
- [timedelta(seconds=1)] * 3
- )
- assert result.dtype == "m8[ns]"
- tm.assert_series_equal(result, expected)
-
- result2 = td2 - td1
- expected = Series([timedelta(seconds=1)] * 3) - Series(
- [timedelta(seconds=0)] * 3
- )
- tm.assert_series_equal(result2, expected)
-
- # roundtrip
- tm.assert_series_equal(result + td2, td1)
-
def test_td64arr_add_td64_array(self, box_with_array):
box = box_with_array
dti = pd.date_range("2016-01-01", periods=3)
@@ -1203,7 +1172,6 @@ def test_td64arr_sub_td64_array(self, box_with_array):
result = tdarr - tdi
tm.assert_equal(result, expected)
- # TODO: parametrize over [add, sub, radd, rsub]?
@pytest.mark.parametrize(
"names",
[
@@ -1232,17 +1200,11 @@ def test_td64arr_add_sub_tdi(self, box, names):
result = tdi + ser
tm.assert_equal(result, expected)
- if box is not pd.DataFrame:
- assert result.dtype == "timedelta64[ns]"
- else:
- assert result.dtypes[0] == "timedelta64[ns]"
+ assert_dtype(result, "timedelta64[ns]")
result = ser + tdi
tm.assert_equal(result, expected)
- if box is not pd.DataFrame:
- assert result.dtype == "timedelta64[ns]"
- else:
- assert result.dtypes[0] == "timedelta64[ns]"
+ assert_dtype(result, "timedelta64[ns]")
expected = Series(
[Timedelta(hours=-3), Timedelta(days=1, hours=-4)], name=names[2]
@@ -1251,17 +1213,11 @@ def test_td64arr_add_sub_tdi(self, box, names):
result = tdi - ser
tm.assert_equal(result, expected)
- if box is not pd.DataFrame:
- assert result.dtype == "timedelta64[ns]"
- else:
- assert result.dtypes[0] == "timedelta64[ns]"
+ assert_dtype(result, "timedelta64[ns]")
result = ser - tdi
tm.assert_equal(result, -expected)
- if box is not pd.DataFrame:
- assert result.dtype == "timedelta64[ns]"
- else:
- assert result.dtypes[0] == "timedelta64[ns]"
+ assert_dtype(result, "timedelta64[ns]")
def test_td64arr_add_sub_td64_nat(self, box_with_array):
# GH#23320 special handling for timedelta64("NaT")
@@ -1296,6 +1252,7 @@ def test_td64arr_sub_NaT(self, box_with_array):
def test_td64arr_add_timedeltalike(self, two_hours, box_with_array):
# only test adding/sub offsets as + is now numeric
+ # GH#10699 for Tick cases
box = box_with_array
rng = timedelta_range("1 days", "10 days")
expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D")
@@ -1305,8 +1262,12 @@ def test_td64arr_add_timedeltalike(self, two_hours, box_with_array):
result = rng + two_hours
tm.assert_equal(result, expected)
+ result = two_hours + rng
+ tm.assert_equal(result, expected)
+
def test_td64arr_sub_timedeltalike(self, two_hours, box_with_array):
# only test adding/sub offsets as - is now numeric
+ # GH#10699 for Tick cases
box = box_with_array
rng = timedelta_range("1 days", "10 days")
expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00")
@@ -1317,46 +1278,12 @@ def test_td64arr_sub_timedeltalike(self, two_hours, box_with_array):
result = rng - two_hours
tm.assert_equal(result, expected)
+ result = two_hours - rng
+ tm.assert_equal(result, -expected)
+
# ------------------------------------------------------------------
# __add__/__sub__ with DateOffsets and arrays of DateOffsets
- # TODO: this was taken from tests.series.test_operators; de-duplicate
- def test_timedelta64_operations_with_DateOffset(self):
- # GH#10699
- td = Series([timedelta(minutes=5, seconds=3)] * 3)
- result = td + pd.offsets.Minute(1)
- expected = Series([timedelta(minutes=6, seconds=3)] * 3)
- tm.assert_series_equal(result, expected)
-
- result = td - pd.offsets.Minute(1)
- expected = Series([timedelta(minutes=4, seconds=3)] * 3)
- tm.assert_series_equal(result, expected)
-
- with tm.assert_produces_warning(PerformanceWarning):
- result = td + Series(
- [pd.offsets.Minute(1), pd.offsets.Second(3), pd.offsets.Hour(2)]
- )
- expected = Series(
- [
- timedelta(minutes=6, seconds=3),
- timedelta(minutes=5, seconds=6),
- timedelta(hours=2, minutes=5, seconds=3),
- ]
- )
- tm.assert_series_equal(result, expected)
-
- result = td + pd.offsets.Minute(1) + pd.offsets.Second(12)
- expected = Series([timedelta(minutes=6, seconds=15)] * 3)
- tm.assert_series_equal(result, expected)
-
- # valid DateOffsets
- for do in ["Hour", "Minute", "Second", "Day", "Micro", "Milli", "Nano"]:
- op = getattr(pd.offsets, do)
- td + op(5)
- op(5) + td
- td - op(5)
- op(5) - td
-
@pytest.mark.parametrize(
"names", [(None, None, None), ("foo", "bar", None), ("foo", "foo", "foo")]
)
@@ -1561,26 +1488,6 @@ class TestTimedeltaArraylikeMulDivOps:
# Tests for timedelta64[ns]
# __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__
- # TODO: Moved from tests.series.test_operators; needs cleanup
- @pytest.mark.parametrize("m", [1, 3, 10])
- @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
- def test_timedelta64_conversions(self, m, unit):
- startdate = Series(pd.date_range("2013-01-01", "2013-01-03"))
- enddate = Series(pd.date_range("2013-03-01", "2013-03-03"))
-
- ser = enddate - startdate
- ser[2] = np.nan
-
- # op
- expected = Series([x / np.timedelta64(m, unit) for x in ser])
- result = ser / np.timedelta64(m, unit)
- tm.assert_series_equal(result, expected)
-
- # reverse op
- expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in ser])
- result = np.timedelta64(m, unit) / ser
- tm.assert_series_equal(result, expected)
-
# ------------------------------------------------------------------
# Multiplication
# organized with scalar others first, then array-like
@@ -1734,6 +1641,29 @@ def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array):
expected = 1 / expected
tm.assert_equal(result, expected)
+ @pytest.mark.parametrize("m", [1, 3, 10])
+ @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+ def test_td64arr_div_td64_scalar(self, m, unit, box_with_array):
+ startdate = Series(pd.date_range("2013-01-01", "2013-01-03"))
+ enddate = Series(pd.date_range("2013-03-01", "2013-03-03"))
+
+ ser = enddate - startdate
+ ser[2] = np.nan
+ flat = ser
+ ser = tm.box_expected(ser, box_with_array)
+
+ # op
+ expected = Series([x / np.timedelta64(m, unit) for x in flat])
+ expected = tm.box_expected(expected, box_with_array)
+ result = ser / np.timedelta64(m, unit)
+ tm.assert_equal(result, expected)
+
+ # reverse op
+ expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in flat])
+ expected = tm.box_expected(expected, box_with_array)
+ result = np.timedelta64(m, unit) / ser
+ tm.assert_equal(result, expected)
+
def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array):
rng = TimedeltaIndex(["1 days", pd.NaT, "2 days"], name="foo")
expected = pd.Float64Index([12, np.nan, 24], name="foo")
| https://api.github.com/repos/pandas-dev/pandas/pulls/32091 | 2020-02-19T03:45:07Z | 2020-02-23T15:56:30Z | 2020-02-23T15:56:30Z | 2020-02-23T17:05:17Z | |
Series append raises TypeError | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 57c53f73962dc..7c68005144cbc 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -327,6 +327,7 @@ Reshaping
- Bug in :func:`crosstab` when inputs are two Series and have tuple names, the output will keep dummy MultiIndex as columns. (:issue:`18321`)
- :meth:`DataFrame.pivot` can now take lists for ``index`` and ``columns`` arguments (:issue:`21425`)
- Bug in :func:`concat` where the resulting indices are not copied when ``copy=True`` (:issue:`29879`)
+- :meth:`Series.append` will now raise a ``TypeError`` when passed a DataFrame or a sequence containing Dataframe (:issue:`31413`)
- :meth:`DataFrame.replace` and :meth:`Series.replace` will raise a ``TypeError`` if ``to_replace`` is not an expected type. Previously the ``replace`` would fail silently (:issue:`18634`)
@@ -349,7 +350,6 @@ Other
instead of ``TypeError: Can only append a Series if ignore_index=True or if the Series has a name`` (:issue:`30871`)
- Set operations on an object-dtype :class:`Index` now always return object-dtype results (:issue:`31401`)
- Bug in :meth:`AbstractHolidayCalendar.holidays` when no rules were defined (:issue:`31415`)
--
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/series.py b/pandas/core/series.py
index d565cbbdd5344..9e67393df3370 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2535,6 +2535,12 @@ def append(self, to_append, ignore_index=False, verify_integrity=False):
to_concat.extend(to_append)
else:
to_concat = [self, to_append]
+ if any(isinstance(x, (ABCDataFrame,)) for x in to_concat[1:]):
+ msg = (
+ f"to_append should be a Series or list/tuple of Series, "
+ f"got DataFrame"
+ )
+ raise TypeError(msg)
return concat(
to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity
)
diff --git a/pandas/tests/series/methods/test_append.py b/pandas/tests/series/methods/test_append.py
index 4742d6ae3544f..158c759fdaef3 100644
--- a/pandas/tests/series/methods/test_append.py
+++ b/pandas/tests/series/methods/test_append.py
@@ -61,15 +61,15 @@ def test_append_tuples(self):
tm.assert_series_equal(expected, result)
- def test_append_dataframe_regression(self):
- # GH 30975
- df = pd.DataFrame({"A": [1, 2]})
- result = df.A.append([df])
- expected = pd.DataFrame(
- {0: [1.0, 2.0, None, None], "A": [None, None, 1.0, 2.0]}, index=[0, 1, 0, 1]
- )
-
- tm.assert_frame_equal(expected, result)
+ def test_append_dataframe_raises(self):
+ # GH 31413
+ df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
+
+ msg = "to_append should be a Series or list/tuple of Series, got DataFrame"
+ with pytest.raises(TypeError, match=msg):
+ df.A.append(df)
+ with pytest.raises(TypeError, match=msg):
+ df.A.append([df])
class TestSeriesAppendWithDatetimeIndex:
| - [ ] closes #31413
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Series.append now raises TypeError when a DataFrame or a sequence containing DataFrame is passed.
| https://api.github.com/repos/pandas-dev/pandas/pulls/32090 | 2020-02-19T02:18:53Z | 2020-03-04T14:17:16Z | 2020-03-04T14:17:16Z | 2020-03-04T21:07:57Z |
BUG: DataFrame.iat incorrectly wrapping datetime objects | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 13827e8fc4c33..add046674e9dc 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -160,6 +160,7 @@ Indexing
- Bug in :meth:`PeriodIndex.is_monotonic` incorrectly returning ``True`` when containing leading ``NaT`` entries (:issue:`31437`)
- Bug in :meth:`DatetimeIndex.get_loc` raising ``KeyError`` with converted-integer key instead of the user-passed key (:issue:`31425`)
- Bug in :meth:`Series.xs` incorrectly returning ``Timestamp`` instead of ``datetime64`` in some object-dtype cases (:issue:`31630`)
+- Bug in :meth:`DataFrame.iat` incorrectly returning ``Timestamp`` instead of ``datetime`` in some object-dtype cases (:issue:`32809`)
Missing
^^^^^^^
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 4ad80273f77ba..3651fdf081846 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2894,8 +2894,8 @@ def _get_value(self, index, col, takeable: bool = False):
scalar
"""
if takeable:
- series = self._iget_item_cache(col)
- return com.maybe_box_datetimelike(series._values[index])
+ series = self._ixs(col, axis=1)
+ return series._values[index]
series = self._get_item_cache(col)
engine = self.index._engine
diff --git a/pandas/tests/indexing/test_scalar.py b/pandas/tests/indexing/test_scalar.py
index 899c58eb5edea..c4750778e2eb8 100644
--- a/pandas/tests/indexing/test_scalar.py
+++ b/pandas/tests/indexing/test_scalar.py
@@ -1,4 +1,5 @@
""" test scalar indexing, including at and iat """
+from datetime import datetime, timedelta
import numpy as np
import pytest
@@ -288,3 +289,24 @@ def test_getitem_zerodim_np_array(self):
s = Series([1, 2])
result = s[np.array(0)]
assert result == 1
+
+
+def test_iat_dont_wrap_object_datetimelike():
+ # GH#32809 .iat calls go through DataFrame._get_value, should not
+ # call maybe_box_datetimelike
+ dti = date_range("2016-01-01", periods=3)
+ tdi = dti - dti
+ ser = Series(dti.to_pydatetime(), dtype=object)
+ ser2 = Series(tdi.to_pytimedelta(), dtype=object)
+ df = DataFrame({"A": ser, "B": ser2})
+ assert (df.dtypes == object).all()
+
+ for result in [df.at[0, "A"], df.iat[0, 0], df.loc[0, "A"], df.iloc[0, 0]]:
+ assert result is ser[0]
+ assert isinstance(result, datetime)
+ assert not isinstance(result, Timestamp)
+
+ for result in [df.at[1, "B"], df.iat[1, 1], df.loc[1, "B"], df.iloc[1, 1]]:
+ assert result is ser2[1]
+ assert isinstance(result, timedelta)
+ assert not isinstance(result, Timedelta)
| https://api.github.com/repos/pandas-dev/pandas/pulls/32089 | 2020-02-19T02:08:53Z | 2020-02-22T15:56:04Z | 2020-02-22T15:56:04Z | 2020-02-22T15:58:16Z | |
"Backport PR #31679 on branch 1.0.x" | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 19358689a2186..c9031ac1ae9fe 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -19,6 +19,7 @@ Fixed regressions
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`)
+- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index f51d71d5507a0..9db0306e53a50 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -5,7 +5,7 @@
"""
import datetime
import operator
-from typing import Set, Tuple, Union
+from typing import TYPE_CHECKING, Set, Tuple, Union
import numpy as np
@@ -60,6 +60,9 @@
rxor,
)
+if TYPE_CHECKING:
+ from pandas import DataFrame # noqa:F401
+
# -----------------------------------------------------------------------------
# constants
ARITHMETIC_BINOPS: Set[str] = {
@@ -675,6 +678,58 @@ def to_series(right):
return right
+def _should_reindex_frame_op(
+ left: "DataFrame", right, axis, default_axis: int, fill_value, level
+) -> bool:
+ """
+ Check if this is an operation between DataFrames that will need to reindex.
+ """
+ assert isinstance(left, ABCDataFrame)
+
+ if not isinstance(right, ABCDataFrame):
+ return False
+
+ if fill_value is None and level is None and axis is default_axis:
+ # TODO: any other cases we should handle here?
+ cols = left.columns.intersection(right.columns)
+ if not (cols.equals(left.columns) and cols.equals(right.columns)):
+ return True
+
+ return False
+
+
+def _frame_arith_method_with_reindex(
+ left: "DataFrame", right: "DataFrame", op
+) -> "DataFrame":
+ """
+ For DataFrame-with-DataFrame operations that require reindexing,
+ operate only on shared columns, then reindex.
+
+ Parameters
+ ----------
+ left : DataFrame
+ right : DataFrame
+ op : binary operator
+
+ Returns
+ -------
+ DataFrame
+ """
+ # GH#31623, only operate on shared columns
+ cols = left.columns.intersection(right.columns)
+
+ new_left = left[cols]
+ new_right = right[cols]
+ result = op(new_left, new_right)
+
+ # Do the join on the columns instead of using _align_method_FRAME
+ # to avoid constructing two potentially large/sparse DataFrames
+ join_columns, _, _ = left.columns.join(
+ right.columns, how="outer", level=None, return_indexers=True
+ )
+ return result.reindex(join_columns, axis=1)
+
+
def _arith_method_FRAME(cls, op, special):
str_rep = _get_opstr(op)
op_name = _get_op_name(op, special)
@@ -692,6 +747,9 @@ def _arith_method_FRAME(cls, op, special):
@Appender(doc)
def f(self, other, axis=default_axis, level=None, fill_value=None):
+ if _should_reindex_frame_op(self, other, axis, default_axis, fill_value, level):
+ return _frame_arith_method_with_reindex(self, other, op)
+
other = _align_method_FRAME(self, other, axis)
if isinstance(other, ABCDataFrame):
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index 659b55756c4b6..fadba0a8673d0 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -696,6 +696,25 @@ def test_operations_with_interval_categories_index(self, all_arithmetic_operator
expected = pd.DataFrame([[getattr(n, op)(num) for n in data]], columns=ind)
tm.assert_frame_equal(result, expected)
+ def test_frame_with_frame_reindex(self):
+ # GH#31623
+ df = pd.DataFrame(
+ {
+ "foo": [pd.Timestamp("2019"), pd.Timestamp("2020")],
+ "bar": [pd.Timestamp("2018"), pd.Timestamp("2021")],
+ },
+ columns=["foo", "bar"],
+ )
+ df2 = df[["foo"]]
+
+ result = df - df2
+
+ expected = pd.DataFrame(
+ {"foo": [pd.Timedelta(0), pd.Timedelta(0)], "bar": [np.nan, np.nan]},
+ columns=["bar", "foo"],
+ )
+ tm.assert_frame_equal(result, expected)
+
def test_frame_with_zero_len_series_corner_cases():
# GH#28600
| #31679 | https://api.github.com/repos/pandas-dev/pandas/pulls/32088 | 2020-02-19T01:33:22Z | 2020-02-19T11:05:40Z | 2020-02-19T11:05:39Z | 2020-02-19T15:55:41Z |
BUG: Fix incorrect _is_scalar_access check in iloc | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 579daae2b15c6..ac03843a0e27d 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3467,13 +3467,13 @@ def _get_item_cache(self, item):
res._is_copy = self._is_copy
return res
- def _iget_item_cache(self, item):
+ def _iget_item_cache(self, item: int):
"""Return the cached item, item represents a positional indexer."""
ax = self._info_axis
if ax.is_unique:
lower = self._get_item_cache(ax[item])
else:
- lower = self._take_with_is_copy(item, axis=self._info_axis_number)
+ return self._ixs(item, axis=1)
return lower
def _box_item_values(self, key, values):
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index d3e75d43c6bd7..c366b0621fe93 100755
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -1445,10 +1445,6 @@ def _is_scalar_access(self, key: Tuple) -> bool:
if not is_integer(k):
return False
- ax = self.obj.axes[i]
- if not ax.is_unique:
- return False
-
return True
def _validate_integer(self, key: int, axis: int) -> None:
diff --git a/pandas/tests/indexing/test_iloc.py b/pandas/tests/indexing/test_iloc.py
index 500bd1853e9a4..683d4f2605712 100644
--- a/pandas/tests/indexing/test_iloc.py
+++ b/pandas/tests/indexing/test_iloc.py
@@ -47,6 +47,17 @@ def test_iloc_getitem_list_int(self):
class TestiLoc2:
# TODO: better name, just separating out things that dont rely on base class
+
+ def test_is_scalar_access(self):
+ # GH#32085 index with duplicates doesnt matter for _is_scalar_access
+ index = pd.Index([1, 2, 1])
+ ser = pd.Series(range(3), index=index)
+
+ assert ser.iloc._is_scalar_access((1,))
+
+ df = ser.to_frame()
+ assert df.iloc._is_scalar_access((1, 0,))
+
def test_iloc_exceeds_bounds(self):
# GH6296
| We only have two test cases that make it to the _take_with_is_copy line in _iget_item_cache | https://api.github.com/repos/pandas-dev/pandas/pulls/32085 | 2020-02-19T00:08:20Z | 2020-02-22T16:08:23Z | 2020-02-22T16:08:23Z | 2020-02-22T16:10:24Z |
CLN: indexing comments and cleanups | diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index c53e690b5f46c..a614c731df879 100755
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -732,14 +732,15 @@ def _getitem_lowerdim(self, tup: Tuple):
raise IndexingError("Too many indexers. handle elsewhere")
for i, key in enumerate(tup):
- if is_label_like(key) or isinstance(key, tuple):
+ if is_label_like(key):
+ # We don't need to check for tuples here because those are
+ # caught by the _is_nested_tuple_indexer check above.
section = self._getitem_axis(key, axis=i)
- # we have yielded a scalar ?
- if not is_list_like_indexer(section):
- return section
-
- elif section.ndim == self.ndim:
+ # We should never have a scalar section here, because
+ # _getitem_lowerdim is only called after a check for
+ # is_scalar_access, which that would be.
+ if section.ndim == self.ndim:
# we're in the middle of slicing through a MultiIndex
# revise the key wrt to `section` by inserting an _NS
new_key = tup[:i] + (_NS,) + tup[i + 1 :]
@@ -757,7 +758,7 @@ def _getitem_lowerdim(self, tup: Tuple):
# slice returns a new object.
if com.is_null_slice(new_key):
return section
- # This is an elided recursive call to iloc/loc/etc'
+ # This is an elided recursive call to iloc/loc
return getattr(section, self.name)[new_key]
raise IndexingError("not applicable")
@@ -1013,15 +1014,7 @@ def _getitem_tuple(self, tup: Tuple):
return self._getitem_tuple_same_dim(tup)
def _get_label(self, label, axis: int):
- if self.ndim == 1:
- # for perf reasons we want to try _xs first
- # as its basically direct indexing
- # but will fail when the index is not present
- # see GH5667
- return self.obj._xs(label, axis=axis)
- elif isinstance(label, tuple) and isinstance(label[axis], slice):
- raise IndexingError("no slices here, handle elsewhere")
-
+ # GH#5667 this will fail if the label is not present in the axis.
return self.obj._xs(label, axis=axis)
def _handle_lowerdim_multi_index_axis0(self, tup: Tuple):
@@ -1298,7 +1291,7 @@ def _validate_read_indexer(
# We (temporarily) allow for some missing keys with .loc, except in
# some cases (e.g. setting) in which "raise_missing" will be False
- if not (self.name == "loc" and not raise_missing):
+ if raise_missing:
not_found = list(set(key) - set(ax))
raise KeyError(f"{not_found} not in index")
@@ -1363,10 +1356,7 @@ def _validate_key(self, key, axis: int):
else:
raise ValueError(f"Can only index by location with a [{self._valid_types}]")
- def _has_valid_setitem_indexer(self, indexer):
- self._has_valid_positional_setitem_indexer(indexer)
-
- def _has_valid_positional_setitem_indexer(self, indexer) -> bool:
+ def _has_valid_setitem_indexer(self, indexer) -> bool:
"""
Validate that a positional indexer cannot enlarge its target
will raise if needed, does not modify the indexer externally.
@@ -1376,7 +1366,7 @@ def _has_valid_positional_setitem_indexer(self, indexer) -> bool:
bool
"""
if isinstance(indexer, dict):
- raise IndexError(f"{self.name} cannot enlarge its target object")
+ raise IndexError("iloc cannot enlarge its target object")
else:
if not isinstance(indexer, tuple):
indexer = _tuplify(self.ndim, indexer)
@@ -1389,11 +1379,9 @@ def _has_valid_positional_setitem_indexer(self, indexer) -> bool:
pass
elif is_integer(i):
if i >= len(ax):
- raise IndexError(
- f"{self.name} cannot enlarge its target object"
- )
+ raise IndexError("iloc cannot enlarge its target object")
elif isinstance(i, dict):
- raise IndexError(f"{self.name} cannot enlarge its target object")
+ raise IndexError("iloc cannot enlarge its target object")
return True
@@ -1520,8 +1508,8 @@ def _convert_to_indexer(self, key, axis: int, is_setter: bool = False):
return key
elif is_float(key):
+ # _validate_indexer call will always raise
labels._validate_indexer("positional", key, "iloc")
- return key
self._validate_key(key, axis)
return key
@@ -1582,7 +1570,7 @@ def _setitem_with_indexer(self, indexer, value):
# this correctly sets the dtype and avoids cache issues
# essentially this separates out the block that is needed
# to possibly be modified
- if self.ndim > 1 and i == self.obj._info_axis_number:
+ if self.ndim > 1 and i == info_axis:
# add the new item, and set the value
# must have all defined axes if we have a scalar
| https://api.github.com/repos/pandas-dev/pandas/pulls/32082 | 2020-02-18T19:57:15Z | 2020-02-23T15:52:04Z | 2020-02-23T15:52:04Z | 2020-02-23T17:10:08Z | |
BUG: fix in categorical merges | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 705c335acfb48..822b7dc2e5caa 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -103,9 +103,10 @@ Bug fixes
Categorical
^^^^^^^^^^^
+
+- Bug where :func:`merge` was unable to join on non-unique categorical indices (:issue:`28189`)
- Bug when passing categorical data to :class:`Index` constructor along with ``dtype=object`` incorrectly returning a :class:`CategoricalIndex` instead of object-dtype :class:`Index` (:issue:`32167`)
-
--
Datetimelike
^^^^^^^^^^^^
diff --git a/pandas/_libs/join.pyx b/pandas/_libs/join.pyx
index dfa7aa708d681..f696591cf3bd1 100644
--- a/pandas/_libs/join.pyx
+++ b/pandas/_libs/join.pyx
@@ -254,6 +254,8 @@ ctypedef fused join_t:
float64_t
float32_t
object
+ int8_t
+ int16_t
int32_t
int64_t
uint64_t
diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index caa6a9a93141f..11b603f3478ae 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -28,6 +28,7 @@
from pandas.core.indexes.base import Index, _index_shared_docs, maybe_extract_name
from pandas.core.indexes.extension import ExtensionIndex, inherit_names
import pandas.core.missing as missing
+from pandas.core.ops import get_op_result_name
_index_doc_kwargs = dict(ibase._index_doc_kwargs)
_index_doc_kwargs.update(dict(target_klass="CategoricalIndex"))
@@ -785,6 +786,12 @@ def _delegate_method(self, name: str, *args, **kwargs):
return res
return CategoricalIndex(res, name=self.name)
+ def _wrap_joined_index(
+ self, joined: np.ndarray, other: "CategoricalIndex"
+ ) -> "CategoricalIndex":
+ name = get_op_result_name(self, other)
+ return self._create_from_codes(joined, name=name)
+
CategoricalIndex._add_numeric_methods_add_sub_disabled()
CategoricalIndex._add_numeric_methods_disabled()
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index 4f2cd878df613..d80e2e7afceef 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -2163,3 +2163,25 @@ def test_merge_datetime_upcast_dtype():
}
)
tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("n_categories", [5, 128])
+def test_categorical_non_unique_monotonic(n_categories):
+ # GH 28189
+ # With n_categories as 5, we test the int8 case is hit in libjoin,
+ # with n_categories as 128 we test the int16 case.
+ left_index = CategoricalIndex([0] + list(range(n_categories)))
+ df1 = DataFrame(range(n_categories + 1), columns=["value"], index=left_index)
+ df2 = DataFrame(
+ [[6]],
+ columns=["value"],
+ index=CategoricalIndex([0], categories=np.arange(n_categories)),
+ )
+
+ result = merge(df1, df2, how="left", left_index=True, right_index=True)
+ expected = DataFrame(
+ [[i, 6.0] if i < 2 else [i, np.nan] for i in range(n_categories + 1)],
+ columns=["value_x", "value_y"],
+ index=left_index,
+ )
+ tm.assert_frame_equal(expected, result)
| test adapted (at this point kind of loosely) from #28296
- [x] closes #28189
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32079 | 2020-02-18T18:12:14Z | 2020-02-27T23:07:26Z | 2020-02-27T23:07:26Z | 2020-10-10T14:14:58Z |
REF: de-nest Series.__setitem__ | diff --git a/pandas/core/series.py b/pandas/core/series.py
index 12164a4b8ff6b..e3f4261eb0b73 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -978,14 +978,15 @@ def __setitem__(self, key, value):
key = com.apply_if_callable(key, self)
cacher_needs_updating = self._check_is_chained_assignment_possible()
+ if key is Ellipsis:
+ key = slice(None)
+
try:
self._set_with_engine(key, value)
except (KeyError, ValueError):
values = self._values
if is_integer(key) and not self.index.inferred_type == "integer":
values[key] = value
- elif key is Ellipsis:
- self[:] = value
else:
self.loc[key] = value
@@ -1003,9 +1004,10 @@ def __setitem__(self, key, value):
self._where(~key, value, inplace=True)
return
except InvalidIndexError:
- pass
+ self._set_values(key.astype(np.bool_), value)
- self._set_with(key, value)
+ else:
+ self._set_with(key, value)
if cacher_needs_updating:
self._maybe_update_cacher()
@@ -1046,13 +1048,13 @@ def _set_with(self, key, value):
else:
key_type = lib.infer_dtype(key, skipna=False)
+ # Note: key_type == "boolean" should not occur because that
+ # should be caught by the is_bool_indexer check in __setitem__
if key_type == "integer":
if self.index.inferred_type == "integer":
self._set_labels(key, value)
else:
return self._set_values(key, value)
- elif key_type == "boolean":
- self._set_values(key.astype(np.bool_), value)
else:
self._set_labels(key, value)
| Splitting this into a couple of steps for readability. The two main things I have in mind here are a) de-nesting, b) try to parallel the structure of `__getitem__` | https://api.github.com/repos/pandas-dev/pandas/pulls/32078 | 2020-02-18T17:32:32Z | 2020-03-07T11:18:57Z | 2020-03-07T11:18:57Z | 2020-03-07T15:16:18Z |
CI: Remove docs build from pipelines | diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d87fa5203bd52..7493be34d10c7 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -154,6 +154,39 @@ jobs:
echo "region = BHS" >> $RCLONE_CONFIG_PATH
if: github.event_name == 'push'
- - name: Sync web
+ - name: Sync web with OVH
run: rclone sync pandas_web ovh_cloud_pandas_web:dev
if: github.event_name == 'push'
+
+ - name: Create git repo to upload the built docs to GitHub pages
+ run: |
+ cd pandas_web
+ git init
+ touch .nojekyll
+ echo "dev.pandas.io" > CNAME
+ printf "User-agent: *\nDisallow: /" > robots.txt
+ git add --all .
+ git config user.email "pandas-dev@python.org"
+ git config user.name "pandas-bot"
+ git commit -m "pandas web and documentation in master"
+ if: github.event_name == 'push'
+
+ # For this task to work, next steps are required:
+ # 1. Generate a pair of private/public keys (i.e. `ssh-keygen -t rsa -b 4096 -C "your_email@example.com"`)
+ # 2. Go to https://github.com/pandas-dev/pandas/settings/secrets
+ # 3. Click on "Add a new secret"
+ # 4. Name: "github_pagas_ssh_key", Value: <Content of the private ssh key>
+ # 5. The public key needs to be upladed to https://github.com/pandas-dev/pandas-dev.github.io/settings/keys
+ - name: Install GitHub pages ssh deployment key
+ uses: shimataro/ssh-key-action@v2
+ with:
+ key: ${{ secrets.github_pages_ssh_key }}
+ known_hosts: 'github.com,192.30.252.128 ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ=='
+ if: github.event_name == 'push'
+
+ - name: Publish web and docs to GitHub pages
+ run: |
+ cd pandas_web
+ git remote add origin git@github.com:pandas-dev/pandas-dev.github.io.git
+ git push -f origin master
+ if: github.event_name == 'push'
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index d992c64073476..42a039af46e94 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -15,78 +15,3 @@ jobs:
parameters:
name: Windows
vmImage: vs2017-win2016
-
-- job: 'Web_and_Docs'
- pool:
- vmImage: ubuntu-16.04
- timeoutInMinutes: 90
- steps:
- - script: |
- echo '##vso[task.setvariable variable=ENV_FILE]environment.yml'
- echo '##vso[task.prependpath]$(HOME)/miniconda3/bin'
- displayName: 'Setting environment variables'
-
- - script: |
- sudo apt-get install -y libc6-dev-i386
- ci/setup_env.sh
- displayName: 'Setup environment and build pandas'
-
- - script: |
- source activate pandas-dev
- python web/pandas_web.py web/pandas --target-path=web/build
- displayName: 'Build website'
-
- - script: |
- source activate pandas-dev
- # Next we should simply have `doc/make.py --warnings-are-errors`, everything else is required because the ipython directive doesn't fail the build on errors (https://github.com/ipython/ipython/issues/11547)
- doc/make.py --warnings-are-errors | tee sphinx.log ; SPHINX_RET=${PIPESTATUS[0]}
- grep -B1 "^<<<-------------------------------------------------------------------------$" sphinx.log ; IPY_RET=$(( $? != 1 ))
- exit $(( $SPHINX_RET + $IPY_RET ))
- displayName: 'Build documentation'
-
- - script: |
- mkdir -p to_deploy/docs
- cp -r web/build/* to_deploy/
- cp -r doc/build/html/* to_deploy/docs/
- displayName: 'Merge website and docs'
-
- - script: |
- cd to_deploy
- git init
- touch .nojekyll
- echo "dev.pandas.io" > CNAME
- printf "User-agent: *\nDisallow: /" > robots.txt
- git add --all .
- git config user.email "pandas-dev@python.org"
- git config user.name "pandas-bot"
- git commit -m "pandas web and documentation in master"
- displayName: 'Create git repo for docs build'
- condition : |
- and(not(eq(variables['Build.Reason'], 'PullRequest')),
- eq(variables['Build.SourceBranch'], 'refs/heads/master'))
-
- # For `InstallSSHKey@0` to work, next steps are required:
- # 1. Generate a pair of private/public keys (i.e. `ssh-keygen -t rsa -b 4096 -C "your_email@example.com"`)
- # 2. Go to "Library > Secure files" in the Azure Pipelines dashboard: https://dev.azure.com/pandas-dev/pandas/_library?itemType=SecureFiles
- # 3. Click on "+ Secure file"
- # 4. Upload the private key (the name of the file must match with the specified in "sshKeySecureFile" input below, "pandas_docs_key")
- # 5. Click on file name after it is created, tick the box "Authorize for use in all pipelines" and save
- # 6. The public key specified in "sshPublicKey" is the pair of the uploaded private key, and needs to be set as a deploy key of the repo where the docs will be pushed (with write access): https://github.com/pandas-dev/pandas-dev.github.io/settings/keys
- - task: InstallSSHKey@0
- inputs:
- hostName: 'github.com,192.30.252.128 ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAQEAq2A7hRGmdnm9tUDbO9IDSwBK6TbQa+PXYPCPy6rbTrTtw7PHkccKrpp0yVhp5HdEIcKr6pLlVDBfOLX9QUsyCOV0wzfjIJNlGEYsdlLJizHhbn2mUjvSAHQqZETYP81eFzLQNnPHt4EVVUh7VfDESU84KezmD5QlWpXLmvU31/yMf+Se8xhHTvKSCZIFImWwoG6mbUoWf9nzpIoaSjB+weqqUUmpaaasXVal72J+UX2B+2RPW3RcT0eOzQgqlJL3RKrTJvdsjE3JEAvGq3lGHSZXy28G3skua2SmVi/w4yCE6gbODqnTWlg7+wC604ydGXA8VJiS5ap43JXiUFFAaQ=='
- sshPublicKey: 'ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAACAQDHmz3l/EdqrgNxEUKkwDUuUcLv91unig03pYFGO/DMIgCmPdMG96zAgfnESd837Rm0wSSqylwSzkRJt5MV/TpFlcVifDLDQmUhqCeO8Z6dLl/oe35UKmyYICVwcvQTAaHNnYRpKC5IUlTh0JEtw9fGlnp1Ta7U1ENBLbKdpywczElhZu+hOQ892zqOj3CwA+U2329/d6cd7YnqIKoFN9DWT3kS5K6JE4IoBfQEVekIOs23bKjNLvPoOmi6CroAhu/K8j+NCWQjge5eJf2x/yTnIIP1PlEcXoHIr8io517posIx3TBup+CN8bNS1PpDW3jyD3ttl1uoBudjOQrobNnJeR6Rn67DRkG6IhSwr3BWj8alwUG5mTdZzwV5Pa9KZFdIiqX7NoDGg+itsR39QCn0thK8lGRNSR8KrWC1PSjecwelKBO7uQ7rnk/rkrZdBWR4oEA8YgNH8tirUw5WfOr5a0AIaJicKxGKNdMxZt+zmC+bS7F4YCOGIm9KHa43RrKhoGRhRf9fHHHKUPwFGqtWG4ykcUgoamDOURJyepesBAO3FiRE9rLU6ILbB3yEqqoekborHmAJD5vf7PWItW3Q/YQKuk3kkqRcKnexPyzyyq5lUgTi8CxxZdaASIOu294wjBhhdyHlXEkVTNJ9JKkj/obF+XiIIp0cBDsOXY9hDQ== pandas-dev@python.org'
- sshKeySecureFile: 'pandas_docs_key'
- displayName: 'Install GitHub ssh deployment key'
- condition : |
- and(not(eq(variables['Build.Reason'], 'PullRequest')),
- eq(variables['Build.SourceBranch'], 'refs/heads/master'))
-
- - script: |
- cd to_deploy
- git remote add origin git@github.com:pandas-dev/pandas-dev.github.io.git
- git push -f origin master
- displayName: 'Publish web and docs to GitHub pages'
- condition : |
- and(not(eq(variables['Build.Reason'], 'PullRequest')),
- eq(variables['Build.SourceBranch'], 'refs/heads/master'))
| - [X] closes #29472
At the moment we're building the web and the docs from both Actions and Pipelines. While is still under discussion where things should be hosted, it's probably worth to just build on Actions for now, and publish from there to both the OVH cloud, and to GitHub pages.
**This needs the GitHub pages ssh key to be added to the secrets of this repo with name "github_pages_ssh_key".*** Can someone with permissions please add it? Or if we already got it with a different name, please let me know the name.
| https://api.github.com/repos/pandas-dev/pandas/pulls/32074 | 2020-02-18T10:18:24Z | 2020-02-26T21:08:06Z | 2020-02-26T21:08:05Z | 2020-02-26T21:08:06Z |
CLN: Remove unused script find_commits_touching_func.py | diff --git a/scripts/find_commits_touching_func.py b/scripts/find_commits_touching_func.py
deleted file mode 100755
index 85675cb6df42b..0000000000000
--- a/scripts/find_commits_touching_func.py
+++ /dev/null
@@ -1,244 +0,0 @@
-#!/usr/bin/env python3
-# copyright 2013, y-p @ github
-"""
-Search the git history for all commits touching a named method
-
-You need the sh module to run this
-WARNING: this script uses git clean -f, running it on a repo with untracked
-files will probably erase them.
-
-Usage::
- $ ./find_commits_touching_func.py (see arguments below)
-"""
-import argparse
-from collections import namedtuple
-import logging
-import os
-import re
-
-from dateutil.parser import parse
-
-try:
- import sh
-except ImportError:
- raise ImportError("The 'sh' package is required to run this script.")
-
-
-desc = """
-Find all commits touching a specified function across the codebase.
-""".strip()
-argparser = argparse.ArgumentParser(description=desc)
-argparser.add_argument(
- "funcname",
- metavar="FUNCNAME",
- help="Name of function/method to search for changes on",
-)
-argparser.add_argument(
- "-f",
- "--file-masks",
- metavar="f_re(,f_re)*",
- default=[r"\.py.?$"],
- help="comma separated list of regexes to match "
- "filenames against\ndefaults all .py? files",
-)
-argparser.add_argument(
- "-d",
- "--dir-masks",
- metavar="d_re(,d_re)*",
- default=[],
- help="comma separated list of regexes to match base path against",
-)
-argparser.add_argument(
- "-p",
- "--path-masks",
- metavar="p_re(,p_re)*",
- default=[],
- help="comma separated list of regexes to match full file path against",
-)
-argparser.add_argument(
- "-y",
- "--saw-the-warning",
- action="store_true",
- default=False,
- help="must specify this to run, acknowledge you "
- "realize this will erase untracked files",
-)
-argparser.add_argument(
- "--debug-level",
- default="CRITICAL",
- help="debug level of messages (DEBUG, INFO, etc...)",
-)
-args = argparser.parse_args()
-
-
-lfmt = logging.Formatter(fmt="%(levelname)-8s %(message)s", datefmt="%m-%d %H:%M:%S")
-shh = logging.StreamHandler()
-shh.setFormatter(lfmt)
-logger = logging.getLogger("findit")
-logger.addHandler(shh)
-
-Hit = namedtuple("Hit", "commit path")
-HASH_LEN = 8
-
-
-def clean_checkout(comm):
- h, s, d = get_commit_vitals(comm)
- if len(s) > 60:
- s = s[:60] + "..."
- s = s.split("\n")[0]
- logger.info("CO: %s %s" % (comm, s))
-
- sh.git("checkout", comm, _tty_out=False)
- sh.git("clean", "-f")
-
-
-def get_hits(defname, files=()):
- cs = set()
- for f in files:
- try:
- r = sh.git(
- "blame",
- "-L",
- r"/def\s*{start}/,/def/".format(start=defname),
- f,
- _tty_out=False,
- )
- except sh.ErrorReturnCode_128:
- logger.debug("no matches in %s" % f)
- continue
-
- lines = r.strip().splitlines()[:-1]
- # remove comment lines
- lines = [x for x in lines if not re.search(r"^\w+\s*\(.+\)\s*#", x)]
- hits = set(map(lambda x: x.split(" ")[0], lines))
- cs.update({Hit(commit=c, path=f) for c in hits})
-
- return cs
-
-
-def get_commit_info(c, fmt, sep="\t"):
- r = sh.git(
- "log",
- "--format={}".format(fmt),
- "{}^..{}".format(c, c),
- "-n",
- "1",
- _tty_out=False,
- )
- return str(r).split(sep)
-
-
-def get_commit_vitals(c, hlen=HASH_LEN):
- h, s, d = get_commit_info(c, "%H\t%s\t%ci", "\t")
- return h[:hlen], s, parse(d)
-
-
-def file_filter(state, dirname, fnames):
- if args.dir_masks and not any(re.search(x, dirname) for x in args.dir_masks):
- return
- for f in fnames:
- p = os.path.abspath(os.path.join(os.path.realpath(dirname), f))
- if any(re.search(x, f) for x in args.file_masks) or any(
- re.search(x, p) for x in args.path_masks
- ):
- if os.path.isfile(p):
- state["files"].append(p)
-
-
-def search(defname, head_commit="HEAD"):
- HEAD, s = get_commit_vitals("HEAD")[:2]
- logger.info("HEAD at %s: %s" % (HEAD, s))
- done_commits = set()
- # allhits = set()
- files = []
- state = dict(files=files)
- os.walk(".", file_filter, state)
- # files now holds a list of paths to files
-
- # seed with hits from q
- allhits = set(get_hits(defname, files=files))
- q = {HEAD}
- try:
- while q:
- h = q.pop()
- clean_checkout(h)
- hits = get_hits(defname, files=files)
- for x in hits:
- prevc = get_commit_vitals(x.commit + "^")[0]
- if prevc not in done_commits:
- q.add(prevc)
- allhits.update(hits)
- done_commits.add(h)
-
- logger.debug("Remaining: %s" % q)
- finally:
- logger.info("Restoring HEAD to %s" % HEAD)
- clean_checkout(HEAD)
- return allhits
-
-
-def pprint_hits(hits):
- SUBJ_LEN = 50
- PATH_LEN = 20
- hits = list(hits)
- max_p = 0
- for hit in hits:
- p = hit.path.split(os.path.realpath(os.curdir) + os.path.sep)[-1]
- max_p = max(max_p, len(p))
-
- if max_p < PATH_LEN:
- SUBJ_LEN += PATH_LEN - max_p
- PATH_LEN = max_p
-
- def sorter(i):
- h, s, d = get_commit_vitals(hits[i].commit)
- return hits[i].path, d
-
- print(
- ("\nThese commits touched the %s method in these files on these dates:\n")
- % args.funcname
- )
- for i in sorted(range(len(hits)), key=sorter):
- hit = hits[i]
- h, s, d = get_commit_vitals(hit.commit)
- p = hit.path.split(os.path.realpath(os.curdir) + os.path.sep)[-1]
-
- fmt = "{:%d} {:10} {:<%d} {:<%d}" % (HASH_LEN, SUBJ_LEN, PATH_LEN)
- if len(s) > SUBJ_LEN:
- s = s[: SUBJ_LEN - 5] + " ..."
- print(fmt.format(h[:HASH_LEN], d.isoformat()[:10], s, p[-20:]))
-
- print("\n")
-
-
-def main():
- if not args.saw_the_warning:
- argparser.print_help()
- print(
- """
-!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-WARNING:
-this script uses git clean -f, running it on a repo with untracked files.
-It's recommended that you make a fresh clone and run from its root directory.
-You must specify the -y argument to ignore this warning.
-!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-"""
- )
- return
- if isinstance(args.file_masks, str):
- args.file_masks = args.file_masks.split(",")
- if isinstance(args.path_masks, str):
- args.path_masks = args.path_masks.split(",")
- if isinstance(args.dir_masks, str):
- args.dir_masks = args.dir_masks.split(",")
-
- logger.setLevel(getattr(logging, args.debug_level))
-
- hits = search(args.funcname)
- pprint_hits(hits)
-
-
-if __name__ == "__main__":
- import sys
-
- sys.exit(main())
| - [X] xref #31039
I've been trying the script `find_commits_touching_func.py` and doesn't seem to be working. Tried for many existing functions, and never get any result. I guess it doesn't work with newer git versions, or with the latest dependencies. And if nobody realized of this before, I assume nobody is using this script (and in any case, a separate project would be more appropriate for it, since I don't think it's pandas specific).
The script was being refactored in #32044, that's why I was having a look.
| https://api.github.com/repos/pandas-dev/pandas/pulls/32071 | 2020-02-18T09:17:07Z | 2020-02-20T01:04:11Z | 2020-02-20T01:04:11Z | 2020-02-20T01:04:20Z |
DOC: Add example for multiindex series and dataframe merge | diff --git a/doc/source/user_guide/merging.rst b/doc/source/user_guide/merging.rst
index 8fdcd8d281a41..8302b5c5dea60 100644
--- a/doc/source/user_guide/merging.rst
+++ b/doc/source/user_guide/merging.rst
@@ -724,6 +724,27 @@ either the left or right tables, the values in the joined table will be
labels=['left', 'right'], vertical=False);
plt.close('all');
+You can merge a mult-indexed Series and a DataFrame, if the names of
+the MultiIndex correspond to the columns from the DataFrame. Transform
+the Series to a DataFrame using :meth:`Series.reset_index` before merging,
+as shown in the following example.
+
+.. ipython:: python
+
+ df = pd.DataFrame({"Let": ["A", "B", "C"], "Num": [1, 2, 3]})
+ df
+
+ ser = pd.Series(
+ ["a", "b", "c", "d", "e", "f"],
+ index=pd.MultiIndex.from_arrays(
+ [["A", "B", "C"] * 2, [1, 2, 3, 4, 5, 6]], names=["Let", "Num"]
+ ),
+ )
+ ser
+
+ result = pd.merge(df, ser.reset_index(), on=['Let', 'Num'])
+
+
Here is another example with duplicate join keys in DataFrames:
.. ipython:: python
| - [x] closes #12550
| https://api.github.com/repos/pandas-dev/pandas/pulls/32068 | 2020-02-18T00:05:32Z | 2020-02-27T21:42:40Z | 2020-02-27T21:42:39Z | 2020-03-02T11:41:50Z |
Backport PR #32064 on branch 1.0.x (DOC: pin gitdb2) | diff --git a/environment.yml b/environment.yml
index e244350a0bea0..0d624c3c716a0 100644
--- a/environment.yml
+++ b/environment.yml
@@ -26,6 +26,7 @@ dependencies:
# documentation
- gitpython # obtain contributors from git for whatsnew
+ - gitdb2=2.0.6 # GH-32060
- sphinx
- numpydoc>=0.9.0
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 017e6258d9941..b10ea0c54b96c 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -15,6 +15,7 @@ isort
mypy==0.730
pycodestyle
gitpython
+gitdb2==2.0.6
sphinx
numpydoc>=0.9.0
nbconvert>=5.4.1
| Backport PR #32064: DOC: pin gitdb2 | https://api.github.com/repos/pandas-dev/pandas/pulls/32066 | 2020-02-17T21:35:32Z | 2020-02-17T22:57:28Z | 2020-02-17T22:57:27Z | 2020-02-17T22:57:28Z |
DOC: pin gitdb2 | diff --git a/environment.yml b/environment.yml
index 5f1184e921119..cbdaf8e6c4217 100644
--- a/environment.yml
+++ b/environment.yml
@@ -26,6 +26,7 @@ dependencies:
# documentation
- gitpython # obtain contributors from git for whatsnew
+ - gitdb2=2.0.6 # GH-32060
- sphinx
# documentation (jupyter notebooks)
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 08cbef2c7fc6b..a469cbdd93ceb 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -15,6 +15,7 @@ isort
mypy==0.730
pycodestyle
gitpython
+gitdb2==2.0.6
sphinx
nbconvert>=5.4.1
nbsphinx
| ref #32060 | https://api.github.com/repos/pandas-dev/pandas/pulls/32064 | 2020-02-17T18:44:41Z | 2020-02-17T21:35:22Z | 2020-02-17T21:35:22Z | 2020-02-17T21:35:25Z |
CLN: GH29547 replace old string formatting | diff --git a/pandas/tests/tslibs/test_parsing.py b/pandas/tests/tslibs/test_parsing.py
index c452d5b12ce01..1ba7832c47e6c 100644
--- a/pandas/tests/tslibs/test_parsing.py
+++ b/pandas/tests/tslibs/test_parsing.py
@@ -42,9 +42,9 @@ def test_parse_time_quarter_with_dash(dashed, normal):
@pytest.mark.parametrize("dashed", ["-2Q1992", "2-Q1992", "4-4Q1992"])
def test_parse_time_quarter_with_dash_error(dashed):
- msg = "Unknown datetime string format, unable to parse: {dashed}"
+ msg = f"Unknown datetime string format, unable to parse: {dashed}"
- with pytest.raises(parsing.DateParseError, match=msg.format(dashed=dashed)):
+ with pytest.raises(parsing.DateParseError, match=msg):
parse_time_string(dashed)
@@ -115,12 +115,12 @@ def test_parsers_quarter_invalid(date_str):
if date_str == "6Q-20":
msg = (
"Incorrect quarterly string is given, quarter "
- "must be between 1 and 4: {date_str}"
+ f"must be between 1 and 4: {date_str}"
)
else:
- msg = "Unknown datetime string format, unable to parse: {date_str}"
+ msg = f"Unknown datetime string format, unable to parse: {date_str}"
- with pytest.raises(ValueError, match=msg.format(date_str=date_str)):
+ with pytest.raises(ValueError, match=msg):
parsing.parse_time_string(date_str)
| Hi there! I've added some missed f-strings to:
* tests/tslibs
* tests/tseries
- [x] tests passed
- [x] passes `black pandas`
Thank you.
| https://api.github.com/repos/pandas-dev/pandas/pulls/32063 | 2020-02-17T17:06:14Z | 2020-02-18T11:56:22Z | 2020-02-18T11:56:21Z | 2020-02-18T15:18:19Z |
Backport PR #31734 on branch 1.0.x (BUG: list-like to_replace on Categorical.replace is ignored or crash) | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 0c012e5c1417b..805f87b63eef8 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -29,6 +29,7 @@ Bug fixes
**Categorical**
- Fixed bug where :meth:`Categorical.from_codes` improperly raised a ``ValueError`` when passed nullable integer codes. (:issue:`31779`)
+- Bug in :class:`Categorical` that would ignore or crash when calling :meth:`Series.replace` with a list-like ``to_replace`` (:issue:`31720`)
**I/O**
diff --git a/pandas/_testing.py b/pandas/_testing.py
index 1fdc5d478aaf6..ca378e5ce8f77 100644
--- a/pandas/_testing.py
+++ b/pandas/_testing.py
@@ -1056,6 +1056,7 @@ def assert_series_equal(
check_exact=False,
check_datetimelike_compat=False,
check_categorical=True,
+ check_category_order=True,
obj="Series",
):
"""
@@ -1090,6 +1091,10 @@ def assert_series_equal(
Compare datetime-like which is comparable ignoring dtype.
check_categorical : bool, default True
Whether to compare internal Categorical exactly.
+ check_category_order : bool, default True
+ Whether to compare category order of internal Categoricals
+
+ .. versionadded:: 1.0.2
obj : str, default 'Series'
Specify object name being compared, internally used to show appropriate
assertion message.
@@ -1192,7 +1197,12 @@ def assert_series_equal(
if check_categorical:
if is_categorical_dtype(left) or is_categorical_dtype(right):
- assert_categorical_equal(left.values, right.values, obj=f"{obj} category")
+ assert_categorical_equal(
+ left.values,
+ right.values,
+ obj=f"{obj} category",
+ check_category_order=check_category_order,
+ )
# This could be refactored to use the NDFrame.equals method
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 52d9df0c2d508..d8a2fbdd58382 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -2447,18 +2447,30 @@ def replace(self, to_replace, value, inplace: bool = False):
"""
inplace = validate_bool_kwarg(inplace, "inplace")
cat = self if inplace else self.copy()
- if to_replace in cat.categories:
- if isna(value):
- cat.remove_categories(to_replace, inplace=True)
- else:
+
+ # build a dict of (to replace -> value) pairs
+ if is_list_like(to_replace):
+ # if to_replace is list-like and value is scalar
+ replace_dict = {replace_value: value for replace_value in to_replace}
+ else:
+ # if both to_replace and value are scalar
+ replace_dict = {to_replace: value}
+
+ # other cases, like if both to_replace and value are list-like or if
+ # to_replace is a dict, are handled separately in NDFrame
+ for replace_value, new_value in replace_dict.items():
+ if replace_value in cat.categories:
+ if isna(new_value):
+ cat.remove_categories(replace_value, inplace=True)
+ continue
categories = cat.categories.tolist()
- index = categories.index(to_replace)
- if value in cat.categories:
- value_index = categories.index(value)
+ index = categories.index(replace_value)
+ if new_value in cat.categories:
+ value_index = categories.index(new_value)
cat._codes[cat._codes == index] = value_index
- cat.remove_categories(to_replace, inplace=True)
+ cat.remove_categories(replace_value, inplace=True)
else:
- categories[index] = value
+ categories[index] = new_value
cat.rename_categories(categories, inplace=True)
if not inplace:
return cat
diff --git a/pandas/tests/arrays/categorical/test_replace.py b/pandas/tests/arrays/categorical/test_replace.py
new file mode 100644
index 0000000000000..52530123bd52f
--- /dev/null
+++ b/pandas/tests/arrays/categorical/test_replace.py
@@ -0,0 +1,48 @@
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+ "to_replace,value,expected,check_types,check_categorical",
+ [
+ # one-to-one
+ (1, 2, [2, 2, 3], True, True),
+ (1, 4, [4, 2, 3], True, True),
+ (4, 1, [1, 2, 3], True, True),
+ (5, 6, [1, 2, 3], True, True),
+ # many-to-one
+ ([1], 2, [2, 2, 3], True, True),
+ ([1, 2], 3, [3, 3, 3], True, True),
+ ([1, 2], 4, [4, 4, 3], True, True),
+ ((1, 2, 4), 5, [5, 5, 3], True, True),
+ ((5, 6), 2, [1, 2, 3], True, True),
+ # many-to-many, handled outside of Categorical and results in separate dtype
+ ([1], [2], [2, 2, 3], False, False),
+ ([1, 4], [5, 2], [5, 2, 3], False, False),
+ # check_categorical sorts categories, which crashes on mixed dtypes
+ (3, "4", [1, 2, "4"], True, False),
+ ([1, 2, "3"], "5", ["5", "5", 3], True, False),
+ ],
+)
+def test_replace(to_replace, value, expected, check_types, check_categorical):
+ # GH 31720
+ s = pd.Series([1, 2, 3], dtype="category")
+ result = s.replace(to_replace, value)
+ expected = pd.Series(expected, dtype="category")
+ s.replace(to_replace, value, inplace=True)
+ tm.assert_series_equal(
+ expected,
+ result,
+ check_dtype=check_types,
+ check_categorical=check_categorical,
+ check_category_order=False,
+ )
+ tm.assert_series_equal(
+ expected,
+ s,
+ check_dtype=check_types,
+ check_categorical=check_categorical,
+ check_category_order=False,
+ )
| Backport PR #31734: BUG: list-like to_replace on Categorical.replace is ignored or crash | https://api.github.com/repos/pandas-dev/pandas/pulls/32062 | 2020-02-17T17:00:05Z | 2020-02-19T07:32:30Z | 2020-02-19T07:32:30Z | 2020-02-19T07:32:30Z |
Backport PR #32025 and #32031 silence numpy-dev failures | diff --git a/pandas/__init__.py b/pandas/__init__.py
index 491bcb21f245d..6eda468eed23a 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -25,6 +25,7 @@
_np_version_under1p16,
_np_version_under1p17,
_np_version_under1p18,
+ _is_numpy_dev,
)
try:
diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py
index 406d5f055797d..5aab5b814bae7 100644
--- a/pandas/tests/api/test_api.py
+++ b/pandas/tests/api/test_api.py
@@ -198,6 +198,7 @@ class TestPDApi(Base):
"_np_version_under1p16",
"_np_version_under1p17",
"_np_version_under1p18",
+ "_is_numpy_dev",
"_testing",
"_tslib",
"_typing",
diff --git a/pandas/tests/frame/test_cumulative.py b/pandas/tests/frame/test_cumulative.py
index b545d6aa8afd3..486cbfb2761e0 100644
--- a/pandas/tests/frame/test_cumulative.py
+++ b/pandas/tests/frame/test_cumulative.py
@@ -7,8 +7,9 @@
"""
import numpy as np
+import pytest
-from pandas import DataFrame, Series
+from pandas import DataFrame, Series, _is_numpy_dev
import pandas._testing as tm
@@ -73,6 +74,11 @@ def test_cumprod(self, datetime_frame):
df.cumprod(0)
df.cumprod(1)
+ @pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+ )
def test_cummin(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
@@ -96,6 +102,11 @@ def test_cummin(self, datetime_frame):
cummin_xs = datetime_frame.cummin(axis=1)
assert np.shape(cummin_xs) == np.shape(datetime_frame)
+ @pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+ )
def test_cummax(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 97cf1af1d2e9e..087b1286151d7 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -17,6 +17,7 @@
NaT,
Series,
Timestamp,
+ _is_numpy_dev,
date_range,
isna,
)
@@ -685,6 +686,11 @@ def test_numpy_compat(func):
getattr(g, func)(foo=1)
+@pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+)
def test_cummin_cummax():
# GH 15048
num_types = [np.int32, np.int64, np.float32, np.float64]
diff --git a/pandas/tests/scalar/timedelta/test_arithmetic.py b/pandas/tests/scalar/timedelta/test_arithmetic.py
index 6a9ef86c11292..555b47c8dc0fc 100644
--- a/pandas/tests/scalar/timedelta/test_arithmetic.py
+++ b/pandas/tests/scalar/timedelta/test_arithmetic.py
@@ -8,7 +8,7 @@
import pytest
import pandas as pd
-from pandas import NaT, Timedelta, Timestamp, offsets
+from pandas import NaT, Timedelta, Timestamp, _is_numpy_dev, offsets
import pandas._testing as tm
from pandas.core import ops
@@ -377,7 +377,21 @@ def test_td_div_numeric_scalar(self):
assert isinstance(result, Timedelta)
assert result == Timedelta(days=2)
- @pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")])
+ @pytest.mark.parametrize(
+ "nan",
+ [
+ np.nan,
+ pytest.param(
+ np.float64("NaN"),
+ marks=pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+ ),
+ ),
+ float("nan"),
+ ],
+ )
def test_td_div_nan(self, nan):
# np.float64('NaN') has a 'dtype' attr, avoid treating as array
td = Timedelta(10, unit="d")
diff --git a/pandas/tests/series/test_cumulative.py b/pandas/tests/series/test_cumulative.py
index 885b5bf0476f2..0cb1c038478f5 100644
--- a/pandas/tests/series/test_cumulative.py
+++ b/pandas/tests/series/test_cumulative.py
@@ -11,6 +11,7 @@
import pytest
import pandas as pd
+from pandas import _is_numpy_dev
import pandas._testing as tm
@@ -37,6 +38,11 @@ def test_cumsum(self, datetime_series):
def test_cumprod(self, datetime_series):
_check_accum_op("cumprod", datetime_series)
+ @pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+ )
def test_cummin(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummin().values,
@@ -49,6 +55,11 @@ def test_cummin(self, datetime_series):
tm.assert_series_equal(result, expected)
+ @pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+ )
def test_cummax(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummax().values,
| https://api.github.com/repos/pandas-dev/pandas/pulls/32057 | 2020-02-17T13:20:18Z | 2020-02-17T22:57:40Z | 2020-02-17T22:57:40Z | 2020-02-18T10:54:15Z | |
REGR: read_pickle fallback to encoding=latin_1 upon a UnicodeDecodeError | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index c9031ac1ae9fe..57ed6adf667c8 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -19,6 +19,7 @@ Fixed regressions
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
- Fixed regression in :meth:`pandas.core.groupby.RollingGroupby.apply` where the ``raw`` parameter was ignored (:issue:`31754`)
- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.Rolling.corr>` when using a time offset (:issue:`31789`)
+- Fixed regression where :func:`read_pickle` raised a ``UnicodeDecodeError`` when reading a py27 pickle with :class:`MultiIndex` column (:issue:`31988`).
- Fixed regression in :class:`DataFrame` arithmetic operations with mis-matched columns (:issue:`31623`)
-
diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py
index e51f24b551f31..4e731b8ecca11 100644
--- a/pandas/io/pickle.py
+++ b/pandas/io/pickle.py
@@ -171,21 +171,22 @@ def read_pickle(
# 1) try standard library Pickle
# 2) try pickle_compat (older pandas version) to handle subclass changes
-
- excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError)
+ # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError
try:
- with warnings.catch_warnings(record=True):
- # We want to silence any warnings about, e.g. moved modules.
- warnings.simplefilter("ignore", Warning)
- return pickle.load(f)
- except excs_to_catch:
- # e.g.
- # "No module named 'pandas.core.sparse.series'"
- # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
- return pc.load(f, encoding=None)
+ excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError)
+ try:
+ with warnings.catch_warnings(record=True):
+ # We want to silence any warnings about, e.g. moved modules.
+ warnings.simplefilter("ignore", Warning)
+ return pickle.load(f)
+ except excs_to_catch:
+ # e.g.
+ # "No module named 'pandas.core.sparse.series'"
+ # "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
+ return pc.load(f, encoding=None)
except UnicodeDecodeError:
- # e.g. can occur for files written in py27; see GH#28645
+ # e.g. can occur for files written in py27; see GH#28645 and GH#31988
return pc.load(f, encoding="latin-1")
finally:
f.close()
diff --git a/pandas/tests/io/data/pickle/test_mi_py27.pkl b/pandas/tests/io/data/pickle/test_mi_py27.pkl
new file mode 100644
index 0000000000000..89021dd828108
Binary files /dev/null and b/pandas/tests/io/data/pickle/test_mi_py27.pkl differ
diff --git a/pandas/tests/io/test_pickle.py b/pandas/tests/io/test_pickle.py
index 78b630bb5ada1..584a545769c4c 100644
--- a/pandas/tests/io/test_pickle.py
+++ b/pandas/tests/io/test_pickle.py
@@ -382,14 +382,23 @@ def test_read(self, protocol, get_random_path):
tm.assert_frame_equal(df, df2)
-def test_unicode_decode_error(datapath):
+@pytest.mark.parametrize(
+ ["pickle_file", "excols"],
+ [
+ ("test_py27.pkl", pd.Index(["a", "b", "c"])),
+ (
+ "test_mi_py27.pkl",
+ pd.MultiIndex.from_arrays([["a", "b", "c"], ["A", "B", "C"]]),
+ ),
+ ],
+)
+def test_unicode_decode_error(datapath, pickle_file, excols):
# pickle file written with py27, should be readable without raising
- # UnicodeDecodeError, see GH#28645
- path = datapath("io", "data", "pickle", "test_py27.pkl")
+ # UnicodeDecodeError, see GH#28645 and GH#31988
+ path = datapath("io", "data", "pickle", pickle_file)
df = pd.read_pickle(path)
# just test the columns are correct since the values are random
- excols = pd.Index(["a", "b", "c"])
tm.assert_index_equal(df.columns, excols)
| When a reading a pickle with MultiIndex columns generated in py27
`pickle_compat.load()` with `enconding=None` would throw an UnicodeDecodeError
when reading a pickle created in py27. Now, `read_pickle` catches that exception and
fallback to use `latin-1` explicitly.
- [x] closes #31988
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32055 | 2020-02-17T12:56:42Z | 2020-02-21T14:52:24Z | 2020-02-21T14:52:24Z | 2020-02-21T14:52:39Z |
Backport PR #31156 on branch 1.0.x (DOC: Update of the 'getting started' pages in the sphinx section of the documentation) | diff --git a/doc/data/air_quality_long.csv b/doc/data/air_quality_long.csv
new file mode 100644
index 0000000000000..6225d65d8e276
--- /dev/null
+++ b/doc/data/air_quality_long.csv
@@ -0,0 +1,5273 @@
+city,country,date.utc,location,parameter,value,unit
+Antwerpen,BE,2019-06-18 06:00:00+00:00,BETR801,pm25,18.0,µg/m³
+Antwerpen,BE,2019-06-17 08:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-06-17 07:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-06-17 06:00:00+00:00,BETR801,pm25,16.0,µg/m³
+Antwerpen,BE,2019-06-17 05:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-06-17 04:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-06-17 03:00:00+00:00,BETR801,pm25,7.0,µg/m³
+Antwerpen,BE,2019-06-17 02:00:00+00:00,BETR801,pm25,7.0,µg/m³
+Antwerpen,BE,2019-06-17 01:00:00+00:00,BETR801,pm25,8.0,µg/m³
+Antwerpen,BE,2019-06-16 01:00:00+00:00,BETR801,pm25,15.0,µg/m³
+Antwerpen,BE,2019-06-15 01:00:00+00:00,BETR801,pm25,11.0,µg/m³
+Antwerpen,BE,2019-06-14 09:00:00+00:00,BETR801,pm25,12.0,µg/m³
+Antwerpen,BE,2019-06-13 01:00:00+00:00,BETR801,pm25,3.0,µg/m³
+Antwerpen,BE,2019-06-12 01:00:00+00:00,BETR801,pm25,16.0,µg/m³
+Antwerpen,BE,2019-06-11 01:00:00+00:00,BETR801,pm25,3.5,µg/m³
+Antwerpen,BE,2019-06-10 01:00:00+00:00,BETR801,pm25,8.5,µg/m³
+Antwerpen,BE,2019-06-09 01:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-06-08 01:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-06-06 01:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-06-05 01:00:00+00:00,BETR801,pm25,11.0,µg/m³
+Antwerpen,BE,2019-06-04 01:00:00+00:00,BETR801,pm25,10.5,µg/m³
+Antwerpen,BE,2019-06-03 01:00:00+00:00,BETR801,pm25,12.5,µg/m³
+Antwerpen,BE,2019-06-02 01:00:00+00:00,BETR801,pm25,19.0,µg/m³
+Antwerpen,BE,2019-06-01 01:00:00+00:00,BETR801,pm25,9.0,µg/m³
+Antwerpen,BE,2019-05-31 01:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-05-30 01:00:00+00:00,BETR801,pm25,5.0,µg/m³
+Antwerpen,BE,2019-05-29 01:00:00+00:00,BETR801,pm25,5.5,µg/m³
+Antwerpen,BE,2019-05-28 01:00:00+00:00,BETR801,pm25,7.0,µg/m³
+Antwerpen,BE,2019-05-27 01:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-05-26 01:00:00+00:00,BETR801,pm25,26.5,µg/m³
+Antwerpen,BE,2019-05-25 01:00:00+00:00,BETR801,pm25,10.0,µg/m³
+Antwerpen,BE,2019-05-24 01:00:00+00:00,BETR801,pm25,13.0,µg/m³
+Antwerpen,BE,2019-05-23 01:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-05-22 01:00:00+00:00,BETR801,pm25,15.5,µg/m³
+Antwerpen,BE,2019-05-21 01:00:00+00:00,BETR801,pm25,20.5,µg/m³
+Antwerpen,BE,2019-05-20 17:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-05-20 16:00:00+00:00,BETR801,pm25,17.0,µg/m³
+Antwerpen,BE,2019-05-20 15:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-05-20 14:00:00+00:00,BETR801,pm25,14.5,µg/m³
+Antwerpen,BE,2019-05-20 13:00:00+00:00,BETR801,pm25,17.0,µg/m³
+Antwerpen,BE,2019-05-20 12:00:00+00:00,BETR801,pm25,17.5,µg/m³
+Antwerpen,BE,2019-05-20 11:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-20 10:00:00+00:00,BETR801,pm25,10.5,µg/m³
+Antwerpen,BE,2019-05-20 09:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-20 08:00:00+00:00,BETR801,pm25,19.5,µg/m³
+Antwerpen,BE,2019-05-20 07:00:00+00:00,BETR801,pm25,23.5,µg/m³
+Antwerpen,BE,2019-05-20 06:00:00+00:00,BETR801,pm25,22.0,µg/m³
+Antwerpen,BE,2019-05-20 05:00:00+00:00,BETR801,pm25,25.0,µg/m³
+Antwerpen,BE,2019-05-20 04:00:00+00:00,BETR801,pm25,24.5,µg/m³
+Antwerpen,BE,2019-05-20 03:00:00+00:00,BETR801,pm25,15.0,µg/m³
+Antwerpen,BE,2019-05-20 02:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-05-20 01:00:00+00:00,BETR801,pm25,28.0,µg/m³
+Antwerpen,BE,2019-05-19 21:00:00+00:00,BETR801,pm25,35.5,µg/m³
+Antwerpen,BE,2019-05-19 20:00:00+00:00,BETR801,pm25,40.0,µg/m³
+Antwerpen,BE,2019-05-19 19:00:00+00:00,BETR801,pm25,43.5,µg/m³
+Antwerpen,BE,2019-05-19 18:00:00+00:00,BETR801,pm25,35.0,µg/m³
+Antwerpen,BE,2019-05-19 17:00:00+00:00,BETR801,pm25,34.0,µg/m³
+Antwerpen,BE,2019-05-19 16:00:00+00:00,BETR801,pm25,36.5,µg/m³
+Antwerpen,BE,2019-05-19 15:00:00+00:00,BETR801,pm25,44.0,µg/m³
+Antwerpen,BE,2019-05-19 14:00:00+00:00,BETR801,pm25,43.5,µg/m³
+Antwerpen,BE,2019-05-19 13:00:00+00:00,BETR801,pm25,46.0,µg/m³
+Antwerpen,BE,2019-05-19 12:00:00+00:00,BETR801,pm25,43.0,µg/m³
+Antwerpen,BE,2019-05-19 11:00:00+00:00,BETR801,pm25,41.0,µg/m³
+Antwerpen,BE,2019-05-19 10:00:00+00:00,BETR801,pm25,41.5,µg/m³
+Antwerpen,BE,2019-05-19 09:00:00+00:00,BETR801,pm25,42.5,µg/m³
+Antwerpen,BE,2019-05-19 08:00:00+00:00,BETR801,pm25,51.5,µg/m³
+Antwerpen,BE,2019-05-19 07:00:00+00:00,BETR801,pm25,56.0,µg/m³
+Antwerpen,BE,2019-05-19 06:00:00+00:00,BETR801,pm25,58.5,µg/m³
+Antwerpen,BE,2019-05-19 05:00:00+00:00,BETR801,pm25,60.0,µg/m³
+Antwerpen,BE,2019-05-19 04:00:00+00:00,BETR801,pm25,56.5,µg/m³
+Antwerpen,BE,2019-05-19 03:00:00+00:00,BETR801,pm25,52.5,µg/m³
+Antwerpen,BE,2019-05-19 02:00:00+00:00,BETR801,pm25,51.5,µg/m³
+Antwerpen,BE,2019-05-19 01:00:00+00:00,BETR801,pm25,52.0,µg/m³
+Antwerpen,BE,2019-05-19 00:00:00+00:00,BETR801,pm25,49.5,µg/m³
+Antwerpen,BE,2019-05-18 23:00:00+00:00,BETR801,pm25,45.5,µg/m³
+Antwerpen,BE,2019-05-18 22:00:00+00:00,BETR801,pm25,42.0,µg/m³
+Antwerpen,BE,2019-05-18 21:00:00+00:00,BETR801,pm25,40.5,µg/m³
+Antwerpen,BE,2019-05-18 20:00:00+00:00,BETR801,pm25,41.0,µg/m³
+Antwerpen,BE,2019-05-18 19:00:00+00:00,BETR801,pm25,36.5,µg/m³
+Antwerpen,BE,2019-05-18 18:00:00+00:00,BETR801,pm25,37.0,µg/m³
+Antwerpen,BE,2019-05-18 01:00:00+00:00,BETR801,pm25,24.0,µg/m³
+Antwerpen,BE,2019-05-17 01:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-16 01:00:00+00:00,BETR801,pm25,11.0,µg/m³
+Antwerpen,BE,2019-05-15 02:00:00+00:00,BETR801,pm25,12.5,µg/m³
+Antwerpen,BE,2019-05-15 01:00:00+00:00,BETR801,pm25,13.0,µg/m³
+Antwerpen,BE,2019-05-14 02:00:00+00:00,BETR801,pm25,4.0,µg/m³
+Antwerpen,BE,2019-05-14 01:00:00+00:00,BETR801,pm25,4.0,µg/m³
+Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,pm25,5.5,µg/m³
+Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,pm25,5.0,µg/m³
+Antwerpen,BE,2019-05-12 02:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-05-12 01:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-05-11 02:00:00+00:00,BETR801,pm25,19.5,µg/m³
+Antwerpen,BE,2019-05-11 01:00:00+00:00,BETR801,pm25,17.0,µg/m³
+Antwerpen,BE,2019-05-10 02:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-10 01:00:00+00:00,BETR801,pm25,11.5,µg/m³
+Antwerpen,BE,2019-05-09 02:00:00+00:00,BETR801,pm25,3.5,µg/m³
+Antwerpen,BE,2019-05-09 01:00:00+00:00,BETR801,pm25,4.5,µg/m³
+Antwerpen,BE,2019-05-08 02:00:00+00:00,BETR801,pm25,14.0,µg/m³
+Antwerpen,BE,2019-05-08 01:00:00+00:00,BETR801,pm25,14.5,µg/m³
+Antwerpen,BE,2019-05-07 02:00:00+00:00,BETR801,pm25,14.0,µg/m³
+Antwerpen,BE,2019-05-07 01:00:00+00:00,BETR801,pm25,12.5,µg/m³
+Antwerpen,BE,2019-05-06 02:00:00+00:00,BETR801,pm25,10.5,µg/m³
+Antwerpen,BE,2019-05-06 01:00:00+00:00,BETR801,pm25,10.0,µg/m³
+Antwerpen,BE,2019-05-05 02:00:00+00:00,BETR801,pm25,3.0,µg/m³
+Antwerpen,BE,2019-05-05 01:00:00+00:00,BETR801,pm25,5.0,µg/m³
+Antwerpen,BE,2019-05-04 02:00:00+00:00,BETR801,pm25,4.5,µg/m³
+Antwerpen,BE,2019-05-04 01:00:00+00:00,BETR801,pm25,4.0,µg/m³
+Antwerpen,BE,2019-05-03 02:00:00+00:00,BETR801,pm25,9.5,µg/m³
+Antwerpen,BE,2019-05-03 01:00:00+00:00,BETR801,pm25,8.5,µg/m³
+Antwerpen,BE,2019-05-02 02:00:00+00:00,BETR801,pm25,45.5,µg/m³
+Antwerpen,BE,2019-05-02 01:00:00+00:00,BETR801,pm25,46.0,µg/m³
+Antwerpen,BE,2019-05-01 02:00:00+00:00,BETR801,pm25,28.5,µg/m³
+Antwerpen,BE,2019-05-01 01:00:00+00:00,BETR801,pm25,34.5,µg/m³
+Antwerpen,BE,2019-04-30 02:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-04-30 01:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-04-29 02:00:00+00:00,BETR801,pm25,14.5,µg/m³
+Antwerpen,BE,2019-04-29 01:00:00+00:00,BETR801,pm25,14.0,µg/m³
+Antwerpen,BE,2019-04-28 02:00:00+00:00,BETR801,pm25,4.5,µg/m³
+Antwerpen,BE,2019-04-28 01:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-04-27 02:00:00+00:00,BETR801,pm25,7.0,µg/m³
+Antwerpen,BE,2019-04-27 01:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-04-26 02:00:00+00:00,BETR801,pm25,4.0,µg/m³
+Antwerpen,BE,2019-04-26 01:00:00+00:00,BETR801,pm25,4.5,µg/m³
+Antwerpen,BE,2019-04-25 02:00:00+00:00,BETR801,pm25,3.0,µg/m³
+Antwerpen,BE,2019-04-25 01:00:00+00:00,BETR801,pm25,3.0,µg/m³
+Antwerpen,BE,2019-04-24 02:00:00+00:00,BETR801,pm25,19.0,µg/m³
+Antwerpen,BE,2019-04-24 01:00:00+00:00,BETR801,pm25,19.0,µg/m³
+Antwerpen,BE,2019-04-23 02:00:00+00:00,BETR801,pm25,9.0,µg/m³
+Antwerpen,BE,2019-04-23 01:00:00+00:00,BETR801,pm25,9.0,µg/m³
+Antwerpen,BE,2019-04-22 02:00:00+00:00,BETR801,pm25,36.5,µg/m³
+Antwerpen,BE,2019-04-22 01:00:00+00:00,BETR801,pm25,32.5,µg/m³
+Antwerpen,BE,2019-04-21 02:00:00+00:00,BETR801,pm25,26.5,µg/m³
+Antwerpen,BE,2019-04-21 01:00:00+00:00,BETR801,pm25,27.5,µg/m³
+Antwerpen,BE,2019-04-20 02:00:00+00:00,BETR801,pm25,20.0,µg/m³
+Antwerpen,BE,2019-04-20 01:00:00+00:00,BETR801,pm25,20.0,µg/m³
+Antwerpen,BE,2019-04-19 01:00:00+00:00,BETR801,pm25,20.0,µg/m³
+Antwerpen,BE,2019-04-18 02:00:00+00:00,BETR801,pm25,26.5,µg/m³
+Antwerpen,BE,2019-04-18 01:00:00+00:00,BETR801,pm25,25.0,µg/m³
+Antwerpen,BE,2019-04-17 03:00:00+00:00,BETR801,pm25,9.0,µg/m³
+Antwerpen,BE,2019-04-17 02:00:00+00:00,BETR801,pm25,8.5,µg/m³
+Antwerpen,BE,2019-04-17 01:00:00+00:00,BETR801,pm25,8.0,µg/m³
+Antwerpen,BE,2019-04-16 02:00:00+00:00,BETR801,pm25,23.0,µg/m³
+Antwerpen,BE,2019-04-16 01:00:00+00:00,BETR801,pm25,24.0,µg/m³
+Antwerpen,BE,2019-04-15 15:00:00+00:00,BETR801,pm25,26.5,µg/m³
+Antwerpen,BE,2019-04-15 14:00:00+00:00,BETR801,pm25,25.5,µg/m³
+Antwerpen,BE,2019-04-15 13:00:00+00:00,BETR801,pm25,26.5,µg/m³
+Antwerpen,BE,2019-04-15 12:00:00+00:00,BETR801,pm25,26.5,µg/m³
+Antwerpen,BE,2019-04-15 11:00:00+00:00,BETR801,pm25,26.0,µg/m³
+Antwerpen,BE,2019-04-15 10:00:00+00:00,BETR801,pm25,26.0,µg/m³
+Antwerpen,BE,2019-04-15 09:00:00+00:00,BETR801,pm25,21.5,µg/m³
+Antwerpen,BE,2019-04-15 08:00:00+00:00,BETR801,pm25,24.0,µg/m³
+Antwerpen,BE,2019-04-15 07:00:00+00:00,BETR801,pm25,24.0,µg/m³
+Antwerpen,BE,2019-04-15 06:00:00+00:00,BETR801,pm25,23.0,µg/m³
+Antwerpen,BE,2019-04-15 05:00:00+00:00,BETR801,pm25,23.0,µg/m³
+Antwerpen,BE,2019-04-15 04:00:00+00:00,BETR801,pm25,23.5,µg/m³
+Antwerpen,BE,2019-04-15 03:00:00+00:00,BETR801,pm25,24.5,µg/m³
+Antwerpen,BE,2019-04-15 02:00:00+00:00,BETR801,pm25,24.5,µg/m³
+Antwerpen,BE,2019-04-15 01:00:00+00:00,BETR801,pm25,25.5,µg/m³
+Antwerpen,BE,2019-04-12 02:00:00+00:00,BETR801,pm25,22.0,µg/m³
+Antwerpen,BE,2019-04-12 01:00:00+00:00,BETR801,pm25,22.0,µg/m³
+Antwerpen,BE,2019-04-11 02:00:00+00:00,BETR801,pm25,10.0,µg/m³
+Antwerpen,BE,2019-04-11 01:00:00+00:00,BETR801,pm25,9.0,µg/m³
+Antwerpen,BE,2019-04-10 02:00:00+00:00,BETR801,pm25,26.0,µg/m³
+Antwerpen,BE,2019-04-10 01:00:00+00:00,BETR801,pm25,24.5,µg/m³
+Antwerpen,BE,2019-04-09 13:00:00+00:00,BETR801,pm25,38.0,µg/m³
+Antwerpen,BE,2019-04-09 12:00:00+00:00,BETR801,pm25,41.5,µg/m³
+Antwerpen,BE,2019-04-09 11:00:00+00:00,BETR801,pm25,45.0,µg/m³
+Antwerpen,BE,2019-04-09 10:00:00+00:00,BETR801,pm25,44.5,µg/m³
+Antwerpen,BE,2019-04-09 09:00:00+00:00,BETR801,pm25,43.0,µg/m³
+Antwerpen,BE,2019-04-09 08:00:00+00:00,BETR801,pm25,44.0,µg/m³
+Antwerpen,BE,2019-04-09 07:00:00+00:00,BETR801,pm25,46.5,µg/m³
+Antwerpen,BE,2019-04-09 06:00:00+00:00,BETR801,pm25,52.5,µg/m³
+Antwerpen,BE,2019-04-09 05:00:00+00:00,BETR801,pm25,68.0,µg/m³
+Antwerpen,BE,2019-04-09 04:00:00+00:00,BETR801,pm25,83.5,µg/m³
+Antwerpen,BE,2019-04-09 03:00:00+00:00,BETR801,pm25,99.0,µg/m³
+Antwerpen,BE,2019-04-09 02:00:00+00:00,BETR801,pm25,91.5,µg/m³
+Antwerpen,BE,2019-04-09 01:00:00+00:00,BETR801,pm25,76.0,µg/m³
+London,GB,2019-06-21 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-20 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-20 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-20 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-19 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-19 12:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-19 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-19 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-06-18 23:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-06-18 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-06-18 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-18 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-15 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-14 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-14 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-13 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-13 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-13 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-08 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-08 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-08 03:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-08 02:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-08 00:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 23:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 21:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 20:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 19:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 18:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 17:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 16:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 15:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 14:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 13:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 12:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 11:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 10:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 09:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 08:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 07:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-06-07 06:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-06-07 05:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-06-07 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-07 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-07 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-07 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-07 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-06 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-06 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-05 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-05 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-05 01:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-01 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 22:00:00+00:00,London Westminster,pm25,5.0,µg/m³
+London,GB,2019-05-31 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-25 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-25 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-25 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-24 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-24 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-24 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-21 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-21 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-21 07:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-21 06:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-21 05:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-21 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-21 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-21 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-21 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-21 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 22:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 21:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 20:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 19:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 18:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 17:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-20 16:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-20 15:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 14:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 13:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 12:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 11:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 10:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-20 09:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 08:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-20 07:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-20 06:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-20 05:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-20 04:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 03:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 02:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 01:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 00:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 23:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 22:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 21:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 20:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 19:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 18:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 17:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 16:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 15:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 14:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 13:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 12:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 11:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 10:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 09:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 08:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 07:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 06:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 05:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 04:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 03:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 02:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 01:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 00:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 23:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 22:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 21:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 20:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 19:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 18:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 17:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 16:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 15:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 14:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 13:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 12:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 11:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 10:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 09:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-18 08:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-18 07:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-18 06:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-18 05:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-18 04:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-18 03:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-18 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-18 01:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-18 00:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-17 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-17 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 20:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 01:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-16 23:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-16 22:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-16 21:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-16 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 16:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 14:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 13:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 12:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 11:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 10:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 09:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 08:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 07:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 06:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 05:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 04:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 03:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 02:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 01:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-15 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-15 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-15 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-15 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-15 19:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 18:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 17:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 16:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 15:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 14:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 13:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 12:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-15 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-15 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-15 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 03:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 02:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-14 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-14 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-13 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-13 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-13 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-12 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-12 22:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-12 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-12 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-12 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 09:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 08:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 07:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-12 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-12 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-12 03:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-12 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-12 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-12 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-11 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-11 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 16:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-11 09:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 08:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 07:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-11 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-11 04:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-11 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-11 02:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-11 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-11 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-10 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-10 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 20:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 18:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 17:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 16:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 15:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 14:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 12:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 10:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-10 09:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 08:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 07:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 02:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 01:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-09 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-09 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-09 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-09 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 03:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 02:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 04:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-08 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-08 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-07 18:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-07 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-07 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-07 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-07 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-07 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-07 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-06 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-06 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-06 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-06 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-06 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-06 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-06 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-06 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-05 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-05 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-05 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-05 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-05 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-05 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-05 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-04 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-04 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-04 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-04 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-04 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-04 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-04 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-04 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-04 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-04 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-04 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-04 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-04 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-04 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-04 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-04 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-04 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-03 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-03 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-03 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-03 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-03 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-03 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-03 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-03 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-03 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-03 03:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-03 02:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-03 01:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-03 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-02 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-02 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-02 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-02 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-02 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-02 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-02 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-02 16:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-02 15:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-02 14:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-02 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-02 12:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-02 11:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-02 10:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 09:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 08:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 07:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 06:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 05:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 04:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 03:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 02:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 01:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-02 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 22:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 21:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 20:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 19:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-01 18:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-01 17:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-01 16:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 15:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 14:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 13:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 12:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 11:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 10:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 09:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 08:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 07:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 06:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-01 05:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 04:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-01 03:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-01 00:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-04-30 23:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-30 22:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 21:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 20:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 19:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 18:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 17:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 16:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 15:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 14:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 13:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 12:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 11:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 10:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 09:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-30 08:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-30 07:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-30 06:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-30 05:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-30 04:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-30 03:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-04-30 02:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-04-30 01:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-04-30 00:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-04-29 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-29 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-29 21:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-29 20:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-29 19:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-29 18:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 17:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 16:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 15:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 14:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 13:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 12:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 11:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-29 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-29 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-29 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-29 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-29 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-29 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-29 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-29 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-29 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-29 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-28 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-28 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-28 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-28 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-04-28 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-04-28 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-28 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-27 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-26 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-04-25 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-25 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-04-25 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-25 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-25 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-25 05:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-25 04:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-04-25 03:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-04-25 02:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-25 00:00:00+00:00,London Westminster,pm25,21.0,µg/m³
+London,GB,2019-04-24 23:00:00+00:00,London Westminster,pm25,22.0,µg/m³
+London,GB,2019-04-24 22:00:00+00:00,London Westminster,pm25,23.0,µg/m³
+London,GB,2019-04-24 21:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-24 20:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-24 19:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-24 18:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-24 17:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-24 16:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-24 15:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-24 14:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-24 13:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-24 12:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-24 11:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-24 10:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-24 09:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-24 08:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-24 07:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-24 06:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-24 05:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-24 04:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-24 03:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-24 02:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-24 00:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-23 23:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-23 22:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-23 21:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-23 20:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-23 19:00:00+00:00,London Westminster,pm25,32.0,µg/m³
+London,GB,2019-04-23 18:00:00+00:00,London Westminster,pm25,33.0,µg/m³
+London,GB,2019-04-23 17:00:00+00:00,London Westminster,pm25,33.0,µg/m³
+London,GB,2019-04-23 16:00:00+00:00,London Westminster,pm25,34.0,µg/m³
+London,GB,2019-04-23 15:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-23 14:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-23 13:00:00+00:00,London Westminster,pm25,34.0,µg/m³
+London,GB,2019-04-23 12:00:00+00:00,London Westminster,pm25,34.0,µg/m³
+London,GB,2019-04-23 11:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-23 10:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-23 09:00:00+00:00,London Westminster,pm25,36.0,µg/m³
+London,GB,2019-04-23 08:00:00+00:00,London Westminster,pm25,37.0,µg/m³
+London,GB,2019-04-23 07:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-23 06:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-23 05:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-23 04:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-23 03:00:00+00:00,London Westminster,pm25,44.0,µg/m³
+London,GB,2019-04-23 02:00:00+00:00,London Westminster,pm25,45.0,µg/m³
+London,GB,2019-04-23 01:00:00+00:00,London Westminster,pm25,45.0,µg/m³
+London,GB,2019-04-23 00:00:00+00:00,London Westminster,pm25,45.0,µg/m³
+London,GB,2019-04-22 23:00:00+00:00,London Westminster,pm25,44.0,µg/m³
+London,GB,2019-04-22 22:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-22 21:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-22 20:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-22 19:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-22 18:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-22 17:00:00+00:00,London Westminster,pm25,39.0,µg/m³
+London,GB,2019-04-22 16:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-22 15:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-22 14:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-22 13:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-22 12:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-22 11:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-22 10:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-22 09:00:00+00:00,London Westminster,pm25,37.0,µg/m³
+London,GB,2019-04-22 08:00:00+00:00,London Westminster,pm25,37.0,µg/m³
+London,GB,2019-04-22 07:00:00+00:00,London Westminster,pm25,36.0,µg/m³
+London,GB,2019-04-22 06:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-22 05:00:00+00:00,London Westminster,pm25,33.0,µg/m³
+London,GB,2019-04-22 04:00:00+00:00,London Westminster,pm25,32.0,µg/m³
+London,GB,2019-04-22 03:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-22 02:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-22 01:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-22 00:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-21 23:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-21 22:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-21 21:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-21 20:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-21 19:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-21 18:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-21 17:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-21 16:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-21 15:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-21 14:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-21 13:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-21 12:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-21 11:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-21 10:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-21 09:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-21 08:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-21 07:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-21 06:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-21 05:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-21 04:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-21 03:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-21 02:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-21 01:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-21 00:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-20 23:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-20 22:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-20 21:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-20 20:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-20 19:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-20 18:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-20 17:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-20 16:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 15:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 14:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 13:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 12:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 11:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 10:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 09:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-20 08:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-20 07:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-20 06:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-20 05:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-20 04:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 03:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 02:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 01:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-20 00:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-19 23:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-19 22:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-19 21:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-19 20:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-19 19:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-19 18:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-19 17:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-19 16:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-19 15:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-19 14:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-19 13:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-19 12:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-19 11:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-19 10:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-19 09:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-19 08:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-19 07:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-19 06:00:00+00:00,London Westminster,pm25,31.0,µg/m³
+London,GB,2019-04-19 05:00:00+00:00,London Westminster,pm25,32.0,µg/m³
+London,GB,2019-04-19 04:00:00+00:00,London Westminster,pm25,34.0,µg/m³
+London,GB,2019-04-19 03:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-19 02:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-19 00:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-18 23:00:00+00:00,London Westminster,pm25,45.0,µg/m³
+London,GB,2019-04-18 22:00:00+00:00,London Westminster,pm25,47.0,µg/m³
+London,GB,2019-04-18 21:00:00+00:00,London Westminster,pm25,49.0,µg/m³
+London,GB,2019-04-18 20:00:00+00:00,London Westminster,pm25,50.0,µg/m³
+London,GB,2019-04-18 19:00:00+00:00,London Westminster,pm25,51.0,µg/m³
+London,GB,2019-04-18 18:00:00+00:00,London Westminster,pm25,51.0,µg/m³
+London,GB,2019-04-18 17:00:00+00:00,London Westminster,pm25,51.0,µg/m³
+London,GB,2019-04-18 16:00:00+00:00,London Westminster,pm25,52.0,µg/m³
+London,GB,2019-04-18 15:00:00+00:00,London Westminster,pm25,53.0,µg/m³
+London,GB,2019-04-18 14:00:00+00:00,London Westminster,pm25,53.0,µg/m³
+London,GB,2019-04-18 13:00:00+00:00,London Westminster,pm25,53.0,µg/m³
+London,GB,2019-04-18 12:00:00+00:00,London Westminster,pm25,54.0,µg/m³
+London,GB,2019-04-18 11:00:00+00:00,London Westminster,pm25,55.0,µg/m³
+London,GB,2019-04-18 10:00:00+00:00,London Westminster,pm25,55.0,µg/m³
+London,GB,2019-04-18 09:00:00+00:00,London Westminster,pm25,55.0,µg/m³
+London,GB,2019-04-18 08:00:00+00:00,London Westminster,pm25,55.0,µg/m³
+London,GB,2019-04-18 07:00:00+00:00,London Westminster,pm25,55.0,µg/m³
+London,GB,2019-04-18 06:00:00+00:00,London Westminster,pm25,54.0,µg/m³
+London,GB,2019-04-18 05:00:00+00:00,London Westminster,pm25,53.0,µg/m³
+London,GB,2019-04-18 04:00:00+00:00,London Westminster,pm25,52.0,µg/m³
+London,GB,2019-04-18 03:00:00+00:00,London Westminster,pm25,50.0,µg/m³
+London,GB,2019-04-18 02:00:00+00:00,London Westminster,pm25,48.0,µg/m³
+London,GB,2019-04-18 01:00:00+00:00,London Westminster,pm25,46.0,µg/m³
+London,GB,2019-04-18 00:00:00+00:00,London Westminster,pm25,44.0,µg/m³
+London,GB,2019-04-17 23:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-17 22:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-17 21:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-17 20:00:00+00:00,London Westminster,pm25,39.0,µg/m³
+London,GB,2019-04-17 19:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 18:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 17:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 16:00:00+00:00,London Westminster,pm25,37.0,µg/m³
+London,GB,2019-04-17 15:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 14:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 13:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 12:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 11:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 10:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-17 09:00:00+00:00,London Westminster,pm25,39.0,µg/m³
+London,GB,2019-04-17 08:00:00+00:00,London Westminster,pm25,39.0,µg/m³
+London,GB,2019-04-17 07:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-17 06:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-17 05:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-17 04:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-17 03:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-17 02:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-17 00:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 23:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 22:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 21:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 20:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 19:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 18:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 17:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-16 15:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-16 14:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-16 13:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-16 12:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-16 11:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-16 10:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-16 09:00:00+00:00,London Westminster,pm25,37.0,µg/m³
+London,GB,2019-04-16 08:00:00+00:00,London Westminster,pm25,36.0,µg/m³
+London,GB,2019-04-16 07:00:00+00:00,London Westminster,pm25,36.0,µg/m³
+London,GB,2019-04-16 06:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-16 05:00:00+00:00,London Westminster,pm25,34.0,µg/m³
+London,GB,2019-04-16 04:00:00+00:00,London Westminster,pm25,32.0,µg/m³
+London,GB,2019-04-16 03:00:00+00:00,London Westminster,pm25,32.0,µg/m³
+London,GB,2019-04-16 02:00:00+00:00,London Westminster,pm25,31.0,µg/m³
+London,GB,2019-04-16 00:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-15 23:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-15 22:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-15 21:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-15 20:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-15 19:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-15 18:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-15 17:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-15 16:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-15 15:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-15 14:00:00+00:00,London Westminster,pm25,28.0,µg/m³
+London,GB,2019-04-15 13:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-15 12:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-15 11:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-15 10:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-15 09:00:00+00:00,London Westminster,pm25,25.0,µg/m³
+London,GB,2019-04-15 08:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-15 07:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-15 06:00:00+00:00,London Westminster,pm25,23.0,µg/m³
+London,GB,2019-04-15 05:00:00+00:00,London Westminster,pm25,22.0,µg/m³
+London,GB,2019-04-15 04:00:00+00:00,London Westminster,pm25,22.0,µg/m³
+London,GB,2019-04-15 03:00:00+00:00,London Westminster,pm25,21.0,µg/m³
+London,GB,2019-04-15 02:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-04-15 01:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-15 00:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-14 23:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-04-14 22:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-04-14 21:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-04-14 20:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-14 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-14 18:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 17:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 16:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 15:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 14:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 13:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 12:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 11:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 09:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-14 08:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 07:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-14 01:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-14 00:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-13 23:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 16:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 14:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 13:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 12:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 11:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 10:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 09:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-13 08:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 07:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 06:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 05:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 04:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 03:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-13 00:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 23:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 16:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 15:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 14:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-12 12:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-12 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-12 10:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-12 09:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-12 08:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-12 07:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-12 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-12 05:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-12 04:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-12 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-12 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-11 23:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-11 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-11 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-11 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-11 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-11 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-04-11 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-04-11 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-11 02:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-11 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-04-10 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-10 22:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-04-10 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-10 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-10 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-10 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-10 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-04-10 16:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-10 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-04-10 14:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-04-10 13:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-04-10 12:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-04-10 11:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-04-10 10:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-04-10 09:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-04-10 08:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-04-10 07:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-04-10 06:00:00+00:00,London Westminster,pm25,21.0,µg/m³
+London,GB,2019-04-10 05:00:00+00:00,London Westminster,pm25,22.0,µg/m³
+London,GB,2019-04-10 04:00:00+00:00,London Westminster,pm25,24.0,µg/m³
+London,GB,2019-04-10 03:00:00+00:00,London Westminster,pm25,26.0,µg/m³
+London,GB,2019-04-10 02:00:00+00:00,London Westminster,pm25,27.0,µg/m³
+London,GB,2019-04-10 01:00:00+00:00,London Westminster,pm25,29.0,µg/m³
+London,GB,2019-04-10 00:00:00+00:00,London Westminster,pm25,30.0,µg/m³
+London,GB,2019-04-09 23:00:00+00:00,London Westminster,pm25,32.0,µg/m³
+London,GB,2019-04-09 22:00:00+00:00,London Westminster,pm25,34.0,µg/m³
+London,GB,2019-04-09 21:00:00+00:00,London Westminster,pm25,35.0,µg/m³
+London,GB,2019-04-09 20:00:00+00:00,London Westminster,pm25,36.0,µg/m³
+London,GB,2019-04-09 19:00:00+00:00,London Westminster,pm25,37.0,µg/m³
+London,GB,2019-04-09 18:00:00+00:00,London Westminster,pm25,38.0,µg/m³
+London,GB,2019-04-09 17:00:00+00:00,London Westminster,pm25,39.0,µg/m³
+London,GB,2019-04-09 16:00:00+00:00,London Westminster,pm25,39.0,µg/m³
+London,GB,2019-04-09 15:00:00+00:00,London Westminster,pm25,40.0,µg/m³
+London,GB,2019-04-09 14:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-09 13:00:00+00:00,London Westminster,pm25,41.0,µg/m³
+London,GB,2019-04-09 12:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-09 11:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-09 10:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-09 09:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-09 08:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-09 07:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-09 06:00:00+00:00,London Westminster,pm25,44.0,µg/m³
+London,GB,2019-04-09 05:00:00+00:00,London Westminster,pm25,44.0,µg/m³
+London,GB,2019-04-09 04:00:00+00:00,London Westminster,pm25,43.0,µg/m³
+London,GB,2019-04-09 03:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+London,GB,2019-04-09 02:00:00+00:00,London Westminster,pm25,42.0,µg/m³
+Paris,FR,2019-06-21 00:00:00+00:00,FR04014,no2,20.0,µg/m³
+Paris,FR,2019-06-20 23:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-06-20 22:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-06-20 21:00:00+00:00,FR04014,no2,24.9,µg/m³
+Paris,FR,2019-06-20 20:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-06-20 19:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-06-20 18:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-06-20 17:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-06-20 16:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-06-20 15:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-06-20 14:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-06-20 13:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-06-19 10:00:00+00:00,FR04014,no2,26.6,µg/m³
+Paris,FR,2019-06-19 09:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-06-18 22:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-06-18 21:00:00+00:00,FR04014,no2,23.1,µg/m³
+Paris,FR,2019-06-18 20:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-06-18 19:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-06-18 08:00:00+00:00,FR04014,no2,49.6,µg/m³
+Paris,FR,2019-06-18 07:00:00+00:00,FR04014,no2,52.6,µg/m³
+Paris,FR,2019-06-18 06:00:00+00:00,FR04014,no2,51.4,µg/m³
+Paris,FR,2019-06-18 05:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-06-18 04:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-06-18 03:00:00+00:00,FR04014,no2,45.5,µg/m³
+Paris,FR,2019-06-18 02:00:00+00:00,FR04014,no2,39.8,µg/m³
+Paris,FR,2019-06-18 01:00:00+00:00,FR04014,no2,60.1,µg/m³
+Paris,FR,2019-06-18 00:00:00+00:00,FR04014,no2,66.2,µg/m³
+Paris,FR,2019-06-17 23:00:00+00:00,FR04014,no2,73.3,µg/m³
+Paris,FR,2019-06-17 22:00:00+00:00,FR04014,no2,51.0,µg/m³
+Paris,FR,2019-06-17 21:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-06-17 20:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-06-17 19:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-17 18:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-06-17 17:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-06-17 16:00:00+00:00,FR04014,no2,11.9,µg/m³
+Paris,FR,2019-06-17 15:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-17 14:00:00+00:00,FR04014,no2,11.5,µg/m³
+Paris,FR,2019-06-17 13:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-17 12:00:00+00:00,FR04014,no2,10.1,µg/m³
+Paris,FR,2019-06-17 11:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-17 10:00:00+00:00,FR04014,no2,16.0,µg/m³
+Paris,FR,2019-06-17 09:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-06-17 08:00:00+00:00,FR04014,no2,51.6,µg/m³
+Paris,FR,2019-06-17 07:00:00+00:00,FR04014,no2,54.4,µg/m³
+Paris,FR,2019-06-17 06:00:00+00:00,FR04014,no2,52.3,µg/m³
+Paris,FR,2019-06-17 05:00:00+00:00,FR04014,no2,44.8,µg/m³
+Paris,FR,2019-06-17 04:00:00+00:00,FR04014,no2,45.7,µg/m³
+Paris,FR,2019-06-17 03:00:00+00:00,FR04014,no2,49.1,µg/m³
+Paris,FR,2019-06-17 02:00:00+00:00,FR04014,no2,53.1,µg/m³
+Paris,FR,2019-06-17 01:00:00+00:00,FR04014,no2,58.8,µg/m³
+Paris,FR,2019-06-17 00:00:00+00:00,FR04014,no2,69.3,µg/m³
+Paris,FR,2019-06-16 23:00:00+00:00,FR04014,no2,67.3,µg/m³
+Paris,FR,2019-06-16 22:00:00+00:00,FR04014,no2,56.6,µg/m³
+Paris,FR,2019-06-16 21:00:00+00:00,FR04014,no2,42.7,µg/m³
+Paris,FR,2019-06-16 20:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-06-16 19:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-16 18:00:00+00:00,FR04014,no2,12.3,µg/m³
+Paris,FR,2019-06-16 17:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-16 16:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-06-16 15:00:00+00:00,FR04014,no2,8.4,µg/m³
+Paris,FR,2019-06-16 14:00:00+00:00,FR04014,no2,8.1,µg/m³
+Paris,FR,2019-06-16 13:00:00+00:00,FR04014,no2,8.7,µg/m³
+Paris,FR,2019-06-16 12:00:00+00:00,FR04014,no2,11.2,µg/m³
+Paris,FR,2019-06-16 11:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-06-16 10:00:00+00:00,FR04014,no2,8.7,µg/m³
+Paris,FR,2019-06-16 09:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-06-16 08:00:00+00:00,FR04014,no2,9.9,µg/m³
+Paris,FR,2019-06-16 07:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-16 06:00:00+00:00,FR04014,no2,11.6,µg/m³
+Paris,FR,2019-06-16 05:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-16 04:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-16 03:00:00+00:00,FR04014,no2,11.2,µg/m³
+Paris,FR,2019-06-16 02:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-06-16 01:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-06-16 00:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-06-15 23:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-06-15 22:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-06-15 21:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-06-15 20:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-15 19:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-06-15 18:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-15 17:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-06-15 16:00:00+00:00,FR04014,no2,10.7,µg/m³
+Paris,FR,2019-06-15 15:00:00+00:00,FR04014,no2,10.5,µg/m³
+Paris,FR,2019-06-15 14:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-15 13:00:00+00:00,FR04014,no2,9.0,µg/m³
+Paris,FR,2019-06-15 12:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-06-15 11:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-06-15 10:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-06-15 09:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-15 08:00:00+00:00,FR04014,no2,17.6,µg/m³
+Paris,FR,2019-06-15 07:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-06-15 06:00:00+00:00,FR04014,no2,35.8,µg/m³
+Paris,FR,2019-06-15 02:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-06-15 01:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-06-15 00:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-06-14 23:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-06-14 22:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-06-14 21:00:00+00:00,FR04014,no2,55.0,µg/m³
+Paris,FR,2019-06-14 20:00:00+00:00,FR04014,no2,41.9,µg/m³
+Paris,FR,2019-06-14 19:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-06-14 18:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-06-14 17:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-06-14 16:00:00+00:00,FR04014,no2,18.9,µg/m³
+Paris,FR,2019-06-14 15:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-14 14:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-06-14 13:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-06-14 12:00:00+00:00,FR04014,no2,17.1,µg/m³
+Paris,FR,2019-06-14 11:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-06-14 10:00:00+00:00,FR04014,no2,25.1,µg/m³
+Paris,FR,2019-06-14 09:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-06-14 08:00:00+00:00,FR04014,no2,34.3,µg/m³
+Paris,FR,2019-06-14 07:00:00+00:00,FR04014,no2,51.5,µg/m³
+Paris,FR,2019-06-14 06:00:00+00:00,FR04014,no2,64.3,µg/m³
+Paris,FR,2019-06-14 05:00:00+00:00,FR04014,no2,49.3,µg/m³
+Paris,FR,2019-06-14 04:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-06-14 03:00:00+00:00,FR04014,no2,48.5,µg/m³
+Paris,FR,2019-06-14 02:00:00+00:00,FR04014,no2,66.6,µg/m³
+Paris,FR,2019-06-14 01:00:00+00:00,FR04014,no2,68.1,µg/m³
+Paris,FR,2019-06-14 00:00:00+00:00,FR04014,no2,74.2,µg/m³
+Paris,FR,2019-06-13 23:00:00+00:00,FR04014,no2,78.3,µg/m³
+Paris,FR,2019-06-13 22:00:00+00:00,FR04014,no2,77.9,µg/m³
+Paris,FR,2019-06-13 21:00:00+00:00,FR04014,no2,58.8,µg/m³
+Paris,FR,2019-06-13 20:00:00+00:00,FR04014,no2,31.5,µg/m³
+Paris,FR,2019-06-13 19:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-06-13 18:00:00+00:00,FR04014,no2,24.0,µg/m³
+Paris,FR,2019-06-13 17:00:00+00:00,FR04014,no2,38.2,µg/m³
+Paris,FR,2019-06-13 16:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-06-13 15:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-06-13 14:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-06-13 13:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-06-13 12:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-06-13 11:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-06-13 10:00:00+00:00,FR04014,no2,24.5,µg/m³
+Paris,FR,2019-06-13 09:00:00+00:00,FR04014,no2,30.2,µg/m³
+Paris,FR,2019-06-13 08:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-06-13 07:00:00+00:00,FR04014,no2,40.9,µg/m³
+Paris,FR,2019-06-13 06:00:00+00:00,FR04014,no2,39.8,µg/m³
+Paris,FR,2019-06-13 05:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-06-13 04:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-06-13 03:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-06-13 02:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-06-13 01:00:00+00:00,FR04014,no2,18.7,µg/m³
+Paris,FR,2019-06-13 00:00:00+00:00,FR04014,no2,20.0,µg/m³
+Paris,FR,2019-06-12 23:00:00+00:00,FR04014,no2,26.9,µg/m³
+Paris,FR,2019-06-12 22:00:00+00:00,FR04014,no2,25.6,µg/m³
+Paris,FR,2019-06-12 21:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-06-12 20:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-06-12 19:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-06-12 18:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-06-12 17:00:00+00:00,FR04014,no2,24.2,µg/m³
+Paris,FR,2019-06-12 16:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-06-12 15:00:00+00:00,FR04014,no2,16.8,µg/m³
+Paris,FR,2019-06-12 14:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-06-12 13:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-06-12 12:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-06-12 11:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-06-12 10:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-06-12 09:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-06-12 08:00:00+00:00,FR04014,no2,35.5,µg/m³
+Paris,FR,2019-06-12 07:00:00+00:00,FR04014,no2,44.4,µg/m³
+Paris,FR,2019-06-12 06:00:00+00:00,FR04014,no2,38.4,µg/m³
+Paris,FR,2019-06-12 05:00:00+00:00,FR04014,no2,42.7,µg/m³
+Paris,FR,2019-06-12 04:00:00+00:00,FR04014,no2,44.9,µg/m³
+Paris,FR,2019-06-12 03:00:00+00:00,FR04014,no2,36.3,µg/m³
+Paris,FR,2019-06-12 02:00:00+00:00,FR04014,no2,34.7,µg/m³
+Paris,FR,2019-06-12 01:00:00+00:00,FR04014,no2,41.9,µg/m³
+Paris,FR,2019-06-12 00:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-06-11 23:00:00+00:00,FR04014,no2,41.5,µg/m³
+Paris,FR,2019-06-11 22:00:00+00:00,FR04014,no2,59.4,µg/m³
+Paris,FR,2019-06-11 21:00:00+00:00,FR04014,no2,54.1,µg/m³
+Paris,FR,2019-06-11 20:00:00+00:00,FR04014,no2,42.7,µg/m³
+Paris,FR,2019-06-11 19:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-06-11 18:00:00+00:00,FR04014,no2,44.6,µg/m³
+Paris,FR,2019-06-11 17:00:00+00:00,FR04014,no2,35.5,µg/m³
+Paris,FR,2019-06-11 16:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-06-11 15:00:00+00:00,FR04014,no2,19.8,µg/m³
+Paris,FR,2019-06-11 14:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-06-11 13:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-11 12:00:00+00:00,FR04014,no2,12.6,µg/m³
+Paris,FR,2019-06-11 11:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-06-11 10:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-06-11 09:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-06-11 08:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-06-11 07:00:00+00:00,FR04014,no2,58.0,µg/m³
+Paris,FR,2019-06-11 06:00:00+00:00,FR04014,no2,55.4,µg/m³
+Paris,FR,2019-06-11 05:00:00+00:00,FR04014,no2,58.7,µg/m³
+Paris,FR,2019-06-11 04:00:00+00:00,FR04014,no2,52.7,µg/m³
+Paris,FR,2019-06-11 03:00:00+00:00,FR04014,no2,32.3,µg/m³
+Paris,FR,2019-06-11 02:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-06-11 01:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-06-11 00:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-06-10 23:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-06-10 22:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-06-10 21:00:00+00:00,FR04014,no2,23.5,µg/m³
+Paris,FR,2019-06-10 20:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-06-10 19:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-06-10 18:00:00+00:00,FR04014,no2,18.4,µg/m³
+Paris,FR,2019-06-10 17:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-06-10 16:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-06-10 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-10 14:00:00+00:00,FR04014,no2,9.5,µg/m³
+Paris,FR,2019-06-10 13:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-10 12:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-10 11:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-06-10 10:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-06-10 09:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-06-10 08:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-06-10 07:00:00+00:00,FR04014,no2,23.0,µg/m³
+Paris,FR,2019-06-10 06:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-06-10 05:00:00+00:00,FR04014,no2,21.3,µg/m³
+Paris,FR,2019-06-10 04:00:00+00:00,FR04014,no2,13.7,µg/m³
+Paris,FR,2019-06-10 03:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-06-10 02:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-06-10 01:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-06-10 00:00:00+00:00,FR04014,no2,28.1,µg/m³
+Paris,FR,2019-06-09 23:00:00+00:00,FR04014,no2,39.9,µg/m³
+Paris,FR,2019-06-09 22:00:00+00:00,FR04014,no2,37.1,µg/m³
+Paris,FR,2019-06-09 21:00:00+00:00,FR04014,no2,30.9,µg/m³
+Paris,FR,2019-06-09 20:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-06-09 19:00:00+00:00,FR04014,no2,30.6,µg/m³
+Paris,FR,2019-06-09 18:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-09 17:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-06-09 16:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-06-09 15:00:00+00:00,FR04014,no2,7.2,µg/m³
+Paris,FR,2019-06-09 14:00:00+00:00,FR04014,no2,7.9,µg/m³
+Paris,FR,2019-06-09 13:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-09 12:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-06-09 11:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-06-09 10:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-06-09 09:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-06-09 08:00:00+00:00,FR04014,no2,30.2,µg/m³
+Paris,FR,2019-06-09 07:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-06-09 06:00:00+00:00,FR04014,no2,36.7,µg/m³
+Paris,FR,2019-06-09 05:00:00+00:00,FR04014,no2,42.2,µg/m³
+Paris,FR,2019-06-09 04:00:00+00:00,FR04014,no2,43.0,µg/m³
+Paris,FR,2019-06-09 03:00:00+00:00,FR04014,no2,51.5,µg/m³
+Paris,FR,2019-06-09 02:00:00+00:00,FR04014,no2,51.2,µg/m³
+Paris,FR,2019-06-09 01:00:00+00:00,FR04014,no2,41.0,µg/m³
+Paris,FR,2019-06-09 00:00:00+00:00,FR04014,no2,55.9,µg/m³
+Paris,FR,2019-06-08 23:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-06-08 22:00:00+00:00,FR04014,no2,34.8,µg/m³
+Paris,FR,2019-06-08 21:00:00+00:00,FR04014,no2,36.7,µg/m³
+Paris,FR,2019-06-08 18:00:00+00:00,FR04014,no2,22.0,µg/m³
+Paris,FR,2019-06-08 17:00:00+00:00,FR04014,no2,14.8,µg/m³
+Paris,FR,2019-06-08 16:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-06-08 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-08 14:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-06-08 13:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-06-08 12:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-06-08 11:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-06-08 10:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-06-08 09:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-08 08:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-06-08 07:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-08 06:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-06-08 05:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-06-08 04:00:00+00:00,FR04014,no2,10.7,µg/m³
+Paris,FR,2019-06-08 03:00:00+00:00,FR04014,no2,9.8,µg/m³
+Paris,FR,2019-06-08 02:00:00+00:00,FR04014,no2,8.4,µg/m³
+Paris,FR,2019-06-08 01:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-08 00:00:00+00:00,FR04014,no2,11.3,µg/m³
+Paris,FR,2019-06-07 23:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-07 22:00:00+00:00,FR04014,no2,14.7,µg/m³
+Paris,FR,2019-06-07 21:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-06-07 20:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-06-07 19:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-06-07 18:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-06-07 17:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-07 16:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-07 15:00:00+00:00,FR04014,no2,15.6,µg/m³
+Paris,FR,2019-06-07 14:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-07 13:00:00+00:00,FR04014,no2,15.0,µg/m³
+Paris,FR,2019-06-07 12:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-06-07 11:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-06-07 10:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-06-07 09:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-06-07 08:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-06-07 07:00:00+00:00,FR04014,no2,23.0,µg/m³
+Paris,FR,2019-06-07 06:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-06-06 14:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-06-06 13:00:00+00:00,FR04014,no2,16.0,µg/m³
+Paris,FR,2019-06-06 12:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-06-06 11:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-06-06 10:00:00+00:00,FR04014,no2,21.2,µg/m³
+Paris,FR,2019-06-06 09:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-06-06 08:00:00+00:00,FR04014,no2,36.0,µg/m³
+Paris,FR,2019-06-06 07:00:00+00:00,FR04014,no2,43.1,µg/m³
+Paris,FR,2019-06-06 06:00:00+00:00,FR04014,no2,40.5,µg/m³
+Paris,FR,2019-06-06 05:00:00+00:00,FR04014,no2,40.3,µg/m³
+Paris,FR,2019-06-06 04:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-06-06 03:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-06-06 02:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-06 01:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-06-06 00:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-06-05 23:00:00+00:00,FR04014,no2,31.8,µg/m³
+Paris,FR,2019-06-05 22:00:00+00:00,FR04014,no2,30.3,µg/m³
+Paris,FR,2019-06-05 21:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-06-05 20:00:00+00:00,FR04014,no2,37.5,µg/m³
+Paris,FR,2019-06-05 19:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-06-05 18:00:00+00:00,FR04014,no2,40.8,µg/m³
+Paris,FR,2019-06-05 17:00:00+00:00,FR04014,no2,48.8,µg/m³
+Paris,FR,2019-06-05 16:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-06-05 15:00:00+00:00,FR04014,no2,53.5,µg/m³
+Paris,FR,2019-06-05 14:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-06-05 13:00:00+00:00,FR04014,no2,33.6,µg/m³
+Paris,FR,2019-06-05 12:00:00+00:00,FR04014,no2,47.2,µg/m³
+Paris,FR,2019-06-05 11:00:00+00:00,FR04014,no2,59.0,µg/m³
+Paris,FR,2019-06-05 10:00:00+00:00,FR04014,no2,42.1,µg/m³
+Paris,FR,2019-06-05 09:00:00+00:00,FR04014,no2,36.8,µg/m³
+Paris,FR,2019-06-05 08:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-06-05 07:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-06-05 06:00:00+00:00,FR04014,no2,35.8,µg/m³
+Paris,FR,2019-06-05 05:00:00+00:00,FR04014,no2,39.2,µg/m³
+Paris,FR,2019-06-05 04:00:00+00:00,FR04014,no2,24.5,µg/m³
+Paris,FR,2019-06-05 03:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-06-05 02:00:00+00:00,FR04014,no2,12.4,µg/m³
+Paris,FR,2019-06-05 01:00:00+00:00,FR04014,no2,10.8,µg/m³
+Paris,FR,2019-06-05 00:00:00+00:00,FR04014,no2,15.7,µg/m³
+Paris,FR,2019-06-04 23:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-06-04 22:00:00+00:00,FR04014,no2,33.5,µg/m³
+Paris,FR,2019-06-04 21:00:00+00:00,FR04014,no2,26.3,µg/m³
+Paris,FR,2019-06-04 20:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-06-04 19:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-06-04 18:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-06-04 17:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-06-04 16:00:00+00:00,FR04014,no2,26.3,µg/m³
+Paris,FR,2019-06-04 15:00:00+00:00,FR04014,no2,21.5,µg/m³
+Paris,FR,2019-06-04 14:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-06-04 13:00:00+00:00,FR04014,no2,17.4,µg/m³
+Paris,FR,2019-06-04 12:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-06-04 11:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-06-04 10:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-06-04 09:00:00+00:00,FR04014,no2,38.5,µg/m³
+Paris,FR,2019-06-04 08:00:00+00:00,FR04014,no2,50.8,µg/m³
+Paris,FR,2019-06-04 07:00:00+00:00,FR04014,no2,53.5,µg/m³
+Paris,FR,2019-06-04 06:00:00+00:00,FR04014,no2,47.7,µg/m³
+Paris,FR,2019-06-04 05:00:00+00:00,FR04014,no2,36.5,µg/m³
+Paris,FR,2019-06-04 04:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-06-04 03:00:00+00:00,FR04014,no2,41.6,µg/m³
+Paris,FR,2019-06-04 02:00:00+00:00,FR04014,no2,35.0,µg/m³
+Paris,FR,2019-06-04 01:00:00+00:00,FR04014,no2,43.9,µg/m³
+Paris,FR,2019-06-04 00:00:00+00:00,FR04014,no2,52.4,µg/m³
+Paris,FR,2019-06-03 23:00:00+00:00,FR04014,no2,44.6,µg/m³
+Paris,FR,2019-06-03 22:00:00+00:00,FR04014,no2,30.5,µg/m³
+Paris,FR,2019-06-03 21:00:00+00:00,FR04014,no2,31.1,µg/m³
+Paris,FR,2019-06-03 20:00:00+00:00,FR04014,no2,33.0,µg/m³
+Paris,FR,2019-06-03 19:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-06-03 18:00:00+00:00,FR04014,no2,23.1,µg/m³
+Paris,FR,2019-06-03 17:00:00+00:00,FR04014,no2,24.4,µg/m³
+Paris,FR,2019-06-03 16:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-06-03 15:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-06-03 14:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-06-03 13:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-06-03 12:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-06-03 11:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-06-03 10:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-06-03 09:00:00+00:00,FR04014,no2,46.0,µg/m³
+Paris,FR,2019-06-03 08:00:00+00:00,FR04014,no2,43.9,µg/m³
+Paris,FR,2019-06-03 07:00:00+00:00,FR04014,no2,50.0,µg/m³
+Paris,FR,2019-06-03 06:00:00+00:00,FR04014,no2,44.1,µg/m³
+Paris,FR,2019-06-03 05:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-06-03 04:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-06-03 03:00:00+00:00,FR04014,no2,9.8,µg/m³
+Paris,FR,2019-06-03 02:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-03 01:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-03 00:00:00+00:00,FR04014,no2,15.7,µg/m³
+Paris,FR,2019-06-02 23:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-06-02 22:00:00+00:00,FR04014,no2,27.6,µg/m³
+Paris,FR,2019-06-02 21:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-06-02 20:00:00+00:00,FR04014,no2,40.9,µg/m³
+Paris,FR,2019-06-02 19:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-06-02 18:00:00+00:00,FR04014,no2,15.6,µg/m³
+Paris,FR,2019-06-02 17:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-02 16:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-02 15:00:00+00:00,FR04014,no2,13.9,µg/m³
+Paris,FR,2019-06-02 14:00:00+00:00,FR04014,no2,15.0,µg/m³
+Paris,FR,2019-06-02 13:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-06-02 12:00:00+00:00,FR04014,no2,11.5,µg/m³
+Paris,FR,2019-06-02 11:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-02 10:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-06-02 09:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-06-02 08:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-02 07:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-06-02 06:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-06-02 05:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-06-02 04:00:00+00:00,FR04014,no2,24.5,µg/m³
+Paris,FR,2019-06-02 03:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-06-02 02:00:00+00:00,FR04014,no2,39.2,µg/m³
+Paris,FR,2019-06-02 01:00:00+00:00,FR04014,no2,38.2,µg/m³
+Paris,FR,2019-06-02 00:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-06-01 23:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-06-01 22:00:00+00:00,FR04014,no2,48.1,µg/m³
+Paris,FR,2019-06-01 21:00:00+00:00,FR04014,no2,49.4,µg/m³
+Paris,FR,2019-06-01 20:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-06-01 19:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-06-01 18:00:00+00:00,FR04014,no2,14.5,µg/m³
+Paris,FR,2019-06-01 17:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-01 16:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-01 15:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-01 14:00:00+00:00,FR04014,no2,10.0,µg/m³
+Paris,FR,2019-06-01 13:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-01 12:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-06-01 11:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-06-01 10:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-06-01 09:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-06-01 08:00:00+00:00,FR04014,no2,33.3,µg/m³
+Paris,FR,2019-06-01 07:00:00+00:00,FR04014,no2,46.4,µg/m³
+Paris,FR,2019-06-01 06:00:00+00:00,FR04014,no2,44.6,µg/m³
+Paris,FR,2019-06-01 02:00:00+00:00,FR04014,no2,68.1,µg/m³
+Paris,FR,2019-06-01 01:00:00+00:00,FR04014,no2,74.8,µg/m³
+Paris,FR,2019-06-01 00:00:00+00:00,FR04014,no2,84.7,µg/m³
+Paris,FR,2019-05-31 23:00:00+00:00,FR04014,no2,81.7,µg/m³
+Paris,FR,2019-05-31 22:00:00+00:00,FR04014,no2,68.0,µg/m³
+Paris,FR,2019-05-31 21:00:00+00:00,FR04014,no2,60.2,µg/m³
+Paris,FR,2019-05-31 20:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-05-31 19:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-31 18:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-31 17:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-31 16:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-05-31 15:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-05-31 14:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-05-31 13:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-05-31 12:00:00+00:00,FR04014,no2,13.3,µg/m³
+Paris,FR,2019-05-31 11:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-31 10:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-31 09:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-05-31 08:00:00+00:00,FR04014,no2,36.6,µg/m³
+Paris,FR,2019-05-31 07:00:00+00:00,FR04014,no2,47.4,µg/m³
+Paris,FR,2019-05-31 06:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-05-31 05:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-05-31 04:00:00+00:00,FR04014,no2,31.1,µg/m³
+Paris,FR,2019-05-31 03:00:00+00:00,FR04014,no2,40.1,µg/m³
+Paris,FR,2019-05-31 02:00:00+00:00,FR04014,no2,44.1,µg/m³
+Paris,FR,2019-05-31 01:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-05-31 00:00:00+00:00,FR04014,no2,27.2,µg/m³
+Paris,FR,2019-05-30 23:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-05-30 22:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-05-30 21:00:00+00:00,FR04014,no2,26.9,µg/m³
+Paris,FR,2019-05-30 20:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-05-30 19:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-30 18:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-30 17:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-30 16:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-30 15:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-30 14:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-30 13:00:00+00:00,FR04014,no2,16.1,µg/m³
+Paris,FR,2019-05-30 12:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-05-30 11:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-30 10:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-05-30 09:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-30 08:00:00+00:00,FR04014,no2,16.7,µg/m³
+Paris,FR,2019-05-30 07:00:00+00:00,FR04014,no2,18.3,µg/m³
+Paris,FR,2019-05-30 06:00:00+00:00,FR04014,no2,13.3,µg/m³
+Paris,FR,2019-05-30 05:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-05-30 04:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-05-30 03:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-30 02:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-05-30 01:00:00+00:00,FR04014,no2,12.4,µg/m³
+Paris,FR,2019-05-30 00:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-05-29 23:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-05-29 22:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-29 21:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-05-29 20:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-05-29 19:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-29 18:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-29 17:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-29 16:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-29 15:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-29 14:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-29 13:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-29 12:00:00+00:00,FR04014,no2,13.2,µg/m³
+Paris,FR,2019-05-29 11:00:00+00:00,FR04014,no2,22.0,µg/m³
+Paris,FR,2019-05-29 10:00:00+00:00,FR04014,no2,30.7,µg/m³
+Paris,FR,2019-05-29 09:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-29 08:00:00+00:00,FR04014,no2,45.7,µg/m³
+Paris,FR,2019-05-29 07:00:00+00:00,FR04014,no2,50.5,µg/m³
+Paris,FR,2019-05-29 06:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-29 05:00:00+00:00,FR04014,no2,36.7,µg/m³
+Paris,FR,2019-05-29 04:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-29 03:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-29 02:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-29 01:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-05-29 00:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-05-28 23:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-28 22:00:00+00:00,FR04014,no2,20.2,µg/m³
+Paris,FR,2019-05-28 21:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-28 20:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-28 19:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-28 18:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-05-28 17:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-05-28 16:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-05-28 15:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-05-28 14:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-28 13:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-28 12:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-05-28 11:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-28 10:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-05-28 09:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-28 08:00:00+00:00,FR04014,no2,31.2,µg/m³
+Paris,FR,2019-05-28 07:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-05-28 06:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-05-28 05:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-05-28 04:00:00+00:00,FR04014,no2,8.9,µg/m³
+Paris,FR,2019-05-28 03:00:00+00:00,FR04014,no2,6.1,µg/m³
+Paris,FR,2019-05-28 02:00:00+00:00,FR04014,no2,6.4,µg/m³
+Paris,FR,2019-05-28 01:00:00+00:00,FR04014,no2,8.2,µg/m³
+Paris,FR,2019-05-28 00:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-05-27 23:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-05-27 22:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-05-27 21:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-27 20:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-27 19:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-27 18:00:00+00:00,FR04014,no2,25.6,µg/m³
+Paris,FR,2019-05-27 17:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-27 16:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-05-27 15:00:00+00:00,FR04014,no2,25.6,µg/m³
+Paris,FR,2019-05-27 14:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-05-27 13:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-27 12:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-05-27 11:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-05-27 10:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-27 09:00:00+00:00,FR04014,no2,31.4,µg/m³
+Paris,FR,2019-05-27 08:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-05-27 07:00:00+00:00,FR04014,no2,29.5,µg/m³
+Paris,FR,2019-05-27 06:00:00+00:00,FR04014,no2,29.1,µg/m³
+Paris,FR,2019-05-27 05:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-27 04:00:00+00:00,FR04014,no2,6.5,µg/m³
+Paris,FR,2019-05-27 03:00:00+00:00,FR04014,no2,4.8,µg/m³
+Paris,FR,2019-05-27 02:00:00+00:00,FR04014,no2,5.9,µg/m³
+Paris,FR,2019-05-27 01:00:00+00:00,FR04014,no2,7.1,µg/m³
+Paris,FR,2019-05-27 00:00:00+00:00,FR04014,no2,9.5,µg/m³
+Paris,FR,2019-05-26 23:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-26 22:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-05-26 21:00:00+00:00,FR04014,no2,16.1,µg/m³
+Paris,FR,2019-05-26 20:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-05-26 19:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-26 18:00:00+00:00,FR04014,no2,22.8,µg/m³
+Paris,FR,2019-05-26 17:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-05-26 16:00:00+00:00,FR04014,no2,17.1,µg/m³
+Paris,FR,2019-05-26 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-26 14:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-26 13:00:00+00:00,FR04014,no2,12.5,µg/m³
+Paris,FR,2019-05-26 12:00:00+00:00,FR04014,no2,11.5,µg/m³
+Paris,FR,2019-05-26 11:00:00+00:00,FR04014,no2,13.3,µg/m³
+Paris,FR,2019-05-26 10:00:00+00:00,FR04014,no2,11.3,µg/m³
+Paris,FR,2019-05-26 09:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-26 08:00:00+00:00,FR04014,no2,11.0,µg/m³
+Paris,FR,2019-05-26 07:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-05-26 06:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-26 05:00:00+00:00,FR04014,no2,16.8,µg/m³
+Paris,FR,2019-05-26 04:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-26 03:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-26 02:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-05-26 01:00:00+00:00,FR04014,no2,49.8,µg/m³
+Paris,FR,2019-05-26 00:00:00+00:00,FR04014,no2,67.0,µg/m³
+Paris,FR,2019-05-25 23:00:00+00:00,FR04014,no2,70.2,µg/m³
+Paris,FR,2019-05-25 22:00:00+00:00,FR04014,no2,63.9,µg/m³
+Paris,FR,2019-05-25 21:00:00+00:00,FR04014,no2,39.5,µg/m³
+Paris,FR,2019-05-25 20:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-05-25 19:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-25 18:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-05-25 17:00:00+00:00,FR04014,no2,20.6,µg/m³
+Paris,FR,2019-05-25 16:00:00+00:00,FR04014,no2,31.9,µg/m³
+Paris,FR,2019-05-25 15:00:00+00:00,FR04014,no2,30.0,µg/m³
+Paris,FR,2019-05-25 14:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-05-25 13:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-05-25 12:00:00+00:00,FR04014,no2,18.6,µg/m³
+Paris,FR,2019-05-25 11:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-25 10:00:00+00:00,FR04014,no2,26.3,µg/m³
+Paris,FR,2019-05-25 09:00:00+00:00,FR04014,no2,33.6,µg/m³
+Paris,FR,2019-05-25 08:00:00+00:00,FR04014,no2,44.5,µg/m³
+Paris,FR,2019-05-25 07:00:00+00:00,FR04014,no2,42.1,µg/m³
+Paris,FR,2019-05-25 06:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-05-25 02:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-25 01:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-25 00:00:00+00:00,FR04014,no2,17.4,µg/m³
+Paris,FR,2019-05-24 23:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-05-24 22:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-05-24 21:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-05-24 20:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-05-24 19:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-05-24 18:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-24 17:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-24 16:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-05-24 15:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-24 14:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-24 13:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-24 12:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-05-24 11:00:00+00:00,FR04014,no2,40.6,µg/m³
+Paris,FR,2019-05-24 10:00:00+00:00,FR04014,no2,28.6,µg/m³
+Paris,FR,2019-05-24 09:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-05-24 08:00:00+00:00,FR04014,no2,45.9,µg/m³
+Paris,FR,2019-05-24 07:00:00+00:00,FR04014,no2,54.8,µg/m³
+Paris,FR,2019-05-24 06:00:00+00:00,FR04014,no2,40.7,µg/m³
+Paris,FR,2019-05-24 05:00:00+00:00,FR04014,no2,35.9,µg/m³
+Paris,FR,2019-05-24 04:00:00+00:00,FR04014,no2,28.1,µg/m³
+Paris,FR,2019-05-24 03:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-05-24 02:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-24 01:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-05-24 00:00:00+00:00,FR04014,no2,32.8,µg/m³
+Paris,FR,2019-05-23 23:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-05-23 22:00:00+00:00,FR04014,no2,61.9,µg/m³
+Paris,FR,2019-05-23 21:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-05-23 20:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-05-23 19:00:00+00:00,FR04014,no2,28.0,µg/m³
+Paris,FR,2019-05-23 18:00:00+00:00,FR04014,no2,23.5,µg/m³
+Paris,FR,2019-05-23 17:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-23 16:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-23 15:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-23 14:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-23 13:00:00+00:00,FR04014,no2,21.2,µg/m³
+Paris,FR,2019-05-23 12:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-05-23 11:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-05-23 10:00:00+00:00,FR04014,no2,28.3,µg/m³
+Paris,FR,2019-05-23 09:00:00+00:00,FR04014,no2,79.4,µg/m³
+Paris,FR,2019-05-23 08:00:00+00:00,FR04014,no2,97.0,µg/m³
+Paris,FR,2019-05-23 07:00:00+00:00,FR04014,no2,91.8,µg/m³
+Paris,FR,2019-05-23 06:00:00+00:00,FR04014,no2,79.6,µg/m³
+Paris,FR,2019-05-23 05:00:00+00:00,FR04014,no2,68.7,µg/m³
+Paris,FR,2019-05-23 04:00:00+00:00,FR04014,no2,71.9,µg/m³
+Paris,FR,2019-05-23 03:00:00+00:00,FR04014,no2,76.8,µg/m³
+Paris,FR,2019-05-23 02:00:00+00:00,FR04014,no2,66.6,µg/m³
+Paris,FR,2019-05-23 01:00:00+00:00,FR04014,no2,53.1,µg/m³
+Paris,FR,2019-05-23 00:00:00+00:00,FR04014,no2,53.3,µg/m³
+Paris,FR,2019-05-22 23:00:00+00:00,FR04014,no2,62.1,µg/m³
+Paris,FR,2019-05-22 22:00:00+00:00,FR04014,no2,29.8,µg/m³
+Paris,FR,2019-05-22 21:00:00+00:00,FR04014,no2,37.7,µg/m³
+Paris,FR,2019-05-22 20:00:00+00:00,FR04014,no2,44.9,µg/m³
+Paris,FR,2019-05-22 19:00:00+00:00,FR04014,no2,36.2,µg/m³
+Paris,FR,2019-05-22 18:00:00+00:00,FR04014,no2,34.1,µg/m³
+Paris,FR,2019-05-22 17:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-05-22 16:00:00+00:00,FR04014,no2,34.9,µg/m³
+Paris,FR,2019-05-22 15:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-05-22 14:00:00+00:00,FR04014,no2,40.0,µg/m³
+Paris,FR,2019-05-22 13:00:00+00:00,FR04014,no2,38.5,µg/m³
+Paris,FR,2019-05-22 12:00:00+00:00,FR04014,no2,42.2,µg/m³
+Paris,FR,2019-05-22 11:00:00+00:00,FR04014,no2,42.6,µg/m³
+Paris,FR,2019-05-22 10:00:00+00:00,FR04014,no2,57.8,µg/m³
+Paris,FR,2019-05-22 09:00:00+00:00,FR04014,no2,63.1,µg/m³
+Paris,FR,2019-05-22 08:00:00+00:00,FR04014,no2,70.8,µg/m³
+Paris,FR,2019-05-22 07:00:00+00:00,FR04014,no2,75.4,µg/m³
+Paris,FR,2019-05-22 06:00:00+00:00,FR04014,no2,75.7,µg/m³
+Paris,FR,2019-05-22 05:00:00+00:00,FR04014,no2,45.1,µg/m³
+Paris,FR,2019-05-22 04:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-05-22 03:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-22 02:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-05-22 01:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-22 00:00:00+00:00,FR04014,no2,27.1,µg/m³
+Paris,FR,2019-05-21 23:00:00+00:00,FR04014,no2,29.5,µg/m³
+Paris,FR,2019-05-21 22:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-05-21 21:00:00+00:00,FR04014,no2,43.0,µg/m³
+Paris,FR,2019-05-21 20:00:00+00:00,FR04014,no2,40.8,µg/m³
+Paris,FR,2019-05-21 19:00:00+00:00,FR04014,no2,50.0,µg/m³
+Paris,FR,2019-05-21 18:00:00+00:00,FR04014,no2,54.3,µg/m³
+Paris,FR,2019-05-21 17:00:00+00:00,FR04014,no2,75.0,µg/m³
+Paris,FR,2019-05-21 16:00:00+00:00,FR04014,no2,42.3,µg/m³
+Paris,FR,2019-05-21 15:00:00+00:00,FR04014,no2,36.6,µg/m³
+Paris,FR,2019-05-21 14:00:00+00:00,FR04014,no2,47.8,µg/m³
+Paris,FR,2019-05-21 13:00:00+00:00,FR04014,no2,49.7,µg/m³
+Paris,FR,2019-05-21 12:00:00+00:00,FR04014,no2,30.5,µg/m³
+Paris,FR,2019-05-21 11:00:00+00:00,FR04014,no2,25.5,µg/m³
+Paris,FR,2019-05-21 10:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-05-21 09:00:00+00:00,FR04014,no2,48.1,µg/m³
+Paris,FR,2019-05-21 08:00:00+00:00,FR04014,no2,54.2,µg/m³
+Paris,FR,2019-05-21 07:00:00+00:00,FR04014,no2,56.0,µg/m³
+Paris,FR,2019-05-21 06:00:00+00:00,FR04014,no2,62.6,µg/m³
+Paris,FR,2019-05-21 05:00:00+00:00,FR04014,no2,38.0,µg/m³
+Paris,FR,2019-05-21 04:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-21 03:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-05-21 02:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-05-21 01:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-05-21 00:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-05-20 23:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-05-20 22:00:00+00:00,FR04014,no2,20.7,µg/m³
+Paris,FR,2019-05-20 21:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-20 20:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-05-20 19:00:00+00:00,FR04014,no2,21.3,µg/m³
+Paris,FR,2019-05-20 18:00:00+00:00,FR04014,no2,32.2,µg/m³
+Paris,FR,2019-05-20 17:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-05-20 16:00:00+00:00,FR04014,no2,32.4,µg/m³
+Paris,FR,2019-05-20 15:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-05-20 14:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-05-20 13:00:00+00:00,FR04014,no2,23.7,µg/m³
+Paris,FR,2019-05-20 12:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-05-20 11:00:00+00:00,FR04014,no2,35.4,µg/m³
+Paris,FR,2019-05-20 10:00:00+00:00,FR04014,no2,43.9,µg/m³
+Paris,FR,2019-05-20 09:00:00+00:00,FR04014,no2,45.5,µg/m³
+Paris,FR,2019-05-20 08:00:00+00:00,FR04014,no2,46.1,µg/m³
+Paris,FR,2019-05-20 07:00:00+00:00,FR04014,no2,46.9,µg/m³
+Paris,FR,2019-05-20 06:00:00+00:00,FR04014,no2,40.1,µg/m³
+Paris,FR,2019-05-20 05:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-20 04:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-20 03:00:00+00:00,FR04014,no2,12.6,µg/m³
+Paris,FR,2019-05-20 02:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-05-20 01:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-20 00:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-05-19 23:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-19 22:00:00+00:00,FR04014,no2,22.2,µg/m³
+Paris,FR,2019-05-19 21:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-05-19 20:00:00+00:00,FR04014,no2,35.6,µg/m³
+Paris,FR,2019-05-19 19:00:00+00:00,FR04014,no2,51.2,µg/m³
+Paris,FR,2019-05-19 18:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-05-19 17:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-05-19 16:00:00+00:00,FR04014,no2,32.5,µg/m³
+Paris,FR,2019-05-19 15:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-05-19 14:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-05-19 13:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-05-19 12:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-19 11:00:00+00:00,FR04014,no2,32.6,µg/m³
+Paris,FR,2019-05-19 10:00:00+00:00,FR04014,no2,31.0,µg/m³
+Paris,FR,2019-05-19 09:00:00+00:00,FR04014,no2,33.0,µg/m³
+Paris,FR,2019-05-19 08:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-05-19 07:00:00+00:00,FR04014,no2,32.4,µg/m³
+Paris,FR,2019-05-19 06:00:00+00:00,FR04014,no2,31.1,µg/m³
+Paris,FR,2019-05-19 05:00:00+00:00,FR04014,no2,40.9,µg/m³
+Paris,FR,2019-05-19 04:00:00+00:00,FR04014,no2,39.4,µg/m³
+Paris,FR,2019-05-19 03:00:00+00:00,FR04014,no2,36.4,µg/m³
+Paris,FR,2019-05-19 02:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-05-19 01:00:00+00:00,FR04014,no2,34.9,µg/m³
+Paris,FR,2019-05-19 00:00:00+00:00,FR04014,no2,49.6,µg/m³
+Paris,FR,2019-05-18 23:00:00+00:00,FR04014,no2,50.2,µg/m³
+Paris,FR,2019-05-18 22:00:00+00:00,FR04014,no2,62.5,µg/m³
+Paris,FR,2019-05-18 21:00:00+00:00,FR04014,no2,59.3,µg/m³
+Paris,FR,2019-05-18 20:00:00+00:00,FR04014,no2,36.2,µg/m³
+Paris,FR,2019-05-18 19:00:00+00:00,FR04014,no2,67.5,µg/m³
+Paris,FR,2019-05-18 18:00:00+00:00,FR04014,no2,14.5,µg/m³
+Paris,FR,2019-05-18 17:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-18 16:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-05-18 15:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-18 14:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-05-18 13:00:00+00:00,FR04014,no2,10.5,µg/m³
+Paris,FR,2019-05-18 12:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-18 11:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-18 10:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-18 09:00:00+00:00,FR04014,no2,21.1,µg/m³
+Paris,FR,2019-05-18 08:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-18 07:00:00+00:00,FR04014,no2,27.4,µg/m³
+Paris,FR,2019-05-18 06:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-18 05:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-18 04:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-05-18 03:00:00+00:00,FR04014,no2,16.1,µg/m³
+Paris,FR,2019-05-18 02:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-05-18 01:00:00+00:00,FR04014,no2,37.4,µg/m³
+Paris,FR,2019-05-18 00:00:00+00:00,FR04014,no2,31.5,µg/m³
+Paris,FR,2019-05-17 23:00:00+00:00,FR04014,no2,34.1,µg/m³
+Paris,FR,2019-05-17 22:00:00+00:00,FR04014,no2,28.2,µg/m³
+Paris,FR,2019-05-17 21:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-17 20:00:00+00:00,FR04014,no2,23.5,µg/m³
+Paris,FR,2019-05-17 19:00:00+00:00,FR04014,no2,24.7,µg/m³
+Paris,FR,2019-05-17 18:00:00+00:00,FR04014,no2,33.6,µg/m³
+Paris,FR,2019-05-17 17:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-17 16:00:00+00:00,FR04014,no2,20.7,µg/m³
+Paris,FR,2019-05-17 15:00:00+00:00,FR04014,no2,22.2,µg/m³
+Paris,FR,2019-05-17 14:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-05-17 13:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-05-17 12:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-17 11:00:00+00:00,FR04014,no2,43.1,µg/m³
+Paris,FR,2019-05-17 10:00:00+00:00,FR04014,no2,51.5,µg/m³
+Paris,FR,2019-05-17 09:00:00+00:00,FR04014,no2,60.5,µg/m³
+Paris,FR,2019-05-17 08:00:00+00:00,FR04014,no2,57.5,µg/m³
+Paris,FR,2019-05-17 07:00:00+00:00,FR04014,no2,55.0,µg/m³
+Paris,FR,2019-05-17 06:00:00+00:00,FR04014,no2,46.3,µg/m³
+Paris,FR,2019-05-17 05:00:00+00:00,FR04014,no2,34.0,µg/m³
+Paris,FR,2019-05-17 04:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-17 03:00:00+00:00,FR04014,no2,26.6,µg/m³
+Paris,FR,2019-05-17 02:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-05-17 01:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-05-17 00:00:00+00:00,FR04014,no2,46.3,µg/m³
+Paris,FR,2019-05-16 23:00:00+00:00,FR04014,no2,43.7,µg/m³
+Paris,FR,2019-05-16 22:00:00+00:00,FR04014,no2,37.1,µg/m³
+Paris,FR,2019-05-16 21:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-16 20:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-05-16 19:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-05-16 18:00:00+00:00,FR04014,no2,15.9,µg/m³
+Paris,FR,2019-05-16 17:00:00+00:00,FR04014,no2,13.5,µg/m³
+Paris,FR,2019-05-16 16:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-16 15:00:00+00:00,FR04014,no2,10.1,µg/m³
+Paris,FR,2019-05-16 14:00:00+00:00,FR04014,no2,8.1,µg/m³
+Paris,FR,2019-05-16 13:00:00+00:00,FR04014,no2,8.5,µg/m³
+Paris,FR,2019-05-16 12:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-05-16 11:00:00+00:00,FR04014,no2,10.5,µg/m³
+Paris,FR,2019-05-16 10:00:00+00:00,FR04014,no2,13.5,µg/m³
+Paris,FR,2019-05-16 09:00:00+00:00,FR04014,no2,29.5,µg/m³
+Paris,FR,2019-05-16 08:00:00+00:00,FR04014,no2,39.4,µg/m³
+Paris,FR,2019-05-16 07:00:00+00:00,FR04014,no2,40.0,µg/m³
+Paris,FR,2019-05-16 05:00:00+00:00,FR04014,no2,52.6,µg/m³
+Paris,FR,2019-05-16 04:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-05-16 03:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-16 02:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-05-16 01:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-05-16 00:00:00+00:00,FR04014,no2,27.4,µg/m³
+Paris,FR,2019-05-15 23:00:00+00:00,FR04014,no2,30.9,µg/m³
+Paris,FR,2019-05-15 22:00:00+00:00,FR04014,no2,44.1,µg/m³
+Paris,FR,2019-05-15 21:00:00+00:00,FR04014,no2,36.0,µg/m³
+Paris,FR,2019-05-15 20:00:00+00:00,FR04014,no2,30.1,µg/m³
+Paris,FR,2019-05-15 19:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-15 18:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-05-15 17:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-15 16:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-05-15 15:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-15 14:00:00+00:00,FR04014,no2,11.9,µg/m³
+Paris,FR,2019-05-15 13:00:00+00:00,FR04014,no2,10.0,µg/m³
+Paris,FR,2019-05-15 12:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-05-15 11:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-15 10:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-15 09:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-15 08:00:00+00:00,FR04014,no2,25.7,µg/m³
+Paris,FR,2019-05-15 07:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-05-15 06:00:00+00:00,FR04014,no2,48.1,µg/m³
+Paris,FR,2019-05-15 05:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-15 04:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-05-15 03:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-05-15 02:00:00+00:00,FR04014,no2,16.8,µg/m³
+Paris,FR,2019-05-15 01:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-15 00:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-14 23:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-14 22:00:00+00:00,FR04014,no2,30.9,µg/m³
+Paris,FR,2019-05-14 21:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-05-14 20:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-14 19:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-14 18:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-05-14 17:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-05-14 16:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-14 15:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-05-14 14:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-05-14 13:00:00+00:00,FR04014,no2,11.0,µg/m³
+Paris,FR,2019-05-14 12:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-05-14 11:00:00+00:00,FR04014,no2,11.3,µg/m³
+Paris,FR,2019-05-14 10:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-14 09:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-14 08:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-05-14 07:00:00+00:00,FR04014,no2,41.3,µg/m³
+Paris,FR,2019-05-14 06:00:00+00:00,FR04014,no2,46.1,µg/m³
+Paris,FR,2019-05-14 05:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-05-14 04:00:00+00:00,FR04014,no2,31.6,µg/m³
+Paris,FR,2019-05-14 03:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-14 02:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-14 01:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-05-14 00:00:00+00:00,FR04014,no2,20.9,µg/m³
+Paris,FR,2019-05-13 23:00:00+00:00,FR04014,no2,22.8,µg/m³
+Paris,FR,2019-05-13 22:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-05-13 21:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-05-13 20:00:00+00:00,FR04014,no2,28.3,µg/m³
+Paris,FR,2019-05-13 19:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-05-13 18:00:00+00:00,FR04014,no2,15.5,µg/m³
+Paris,FR,2019-05-13 17:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-13 16:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-05-13 15:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-13 14:00:00+00:00,FR04014,no2,10.7,µg/m³
+Paris,FR,2019-05-13 13:00:00+00:00,FR04014,no2,10.1,µg/m³
+Paris,FR,2019-05-13 12:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-05-13 11:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-05-13 10:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-13 09:00:00+00:00,FR04014,no2,20.6,µg/m³
+Paris,FR,2019-05-13 08:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-05-13 07:00:00+00:00,FR04014,no2,41.0,µg/m³
+Paris,FR,2019-05-13 06:00:00+00:00,FR04014,no2,45.2,µg/m³
+Paris,FR,2019-05-13 05:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-05-13 04:00:00+00:00,FR04014,no2,25.1,µg/m³
+Paris,FR,2019-05-13 03:00:00+00:00,FR04014,no2,18.9,µg/m³
+Paris,FR,2019-05-13 02:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-13 01:00:00+00:00,FR04014,no2,18.9,µg/m³
+Paris,FR,2019-05-13 00:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-05-12 23:00:00+00:00,FR04014,no2,32.5,µg/m³
+Paris,FR,2019-05-12 22:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-12 21:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-05-12 20:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-12 19:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-12 18:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-05-12 17:00:00+00:00,FR04014,no2,13.9,µg/m³
+Paris,FR,2019-05-12 16:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-12 15:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-05-12 14:00:00+00:00,FR04014,no2,9.1,µg/m³
+Paris,FR,2019-05-12 13:00:00+00:00,FR04014,no2,8.7,µg/m³
+Paris,FR,2019-05-12 12:00:00+00:00,FR04014,no2,10.9,µg/m³
+Paris,FR,2019-05-12 11:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-05-12 10:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-05-12 09:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-12 08:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-05-12 07:00:00+00:00,FR04014,no2,15.9,µg/m³
+Paris,FR,2019-05-12 06:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-12 05:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-05-12 04:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-05-12 03:00:00+00:00,FR04014,no2,16.0,µg/m³
+Paris,FR,2019-05-12 02:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-12 01:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-05-12 00:00:00+00:00,FR04014,no2,22.8,µg/m³
+Paris,FR,2019-05-11 23:00:00+00:00,FR04014,no2,26.4,µg/m³
+Paris,FR,2019-05-11 22:00:00+00:00,FR04014,no2,27.7,µg/m³
+Paris,FR,2019-05-11 21:00:00+00:00,FR04014,no2,21.1,µg/m³
+Paris,FR,2019-05-11 20:00:00+00:00,FR04014,no2,24.2,µg/m³
+Paris,FR,2019-05-11 19:00:00+00:00,FR04014,no2,31.2,µg/m³
+Paris,FR,2019-05-11 18:00:00+00:00,FR04014,no2,33.1,µg/m³
+Paris,FR,2019-05-11 17:00:00+00:00,FR04014,no2,32.0,µg/m³
+Paris,FR,2019-05-11 16:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-11 15:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-05-11 14:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-11 13:00:00+00:00,FR04014,no2,30.8,µg/m³
+Paris,FR,2019-05-11 12:00:00+00:00,FR04014,no2,30.2,µg/m³
+Paris,FR,2019-05-11 11:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-05-11 10:00:00+00:00,FR04014,no2,36.8,µg/m³
+Paris,FR,2019-05-11 09:00:00+00:00,FR04014,no2,35.7,µg/m³
+Paris,FR,2019-05-11 08:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-05-11 07:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-05-11 06:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-05-11 02:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-11 01:00:00+00:00,FR04014,no2,15.5,µg/m³
+Paris,FR,2019-05-11 00:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-05-10 23:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-05-10 22:00:00+00:00,FR04014,no2,28.1,µg/m³
+Paris,FR,2019-05-10 21:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-05-10 20:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-05-10 19:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-05-10 18:00:00+00:00,FR04014,no2,33.4,µg/m³
+Paris,FR,2019-05-10 17:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-05-10 16:00:00+00:00,FR04014,no2,30.8,µg/m³
+Paris,FR,2019-05-10 15:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-05-10 14:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-05-10 13:00:00+00:00,FR04014,no2,22.0,µg/m³
+Paris,FR,2019-05-10 12:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-10 11:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-05-10 10:00:00+00:00,FR04014,no2,35.1,µg/m³
+Paris,FR,2019-05-10 09:00:00+00:00,FR04014,no2,53.4,µg/m³
+Paris,FR,2019-05-10 08:00:00+00:00,FR04014,no2,60.7,µg/m³
+Paris,FR,2019-05-10 07:00:00+00:00,FR04014,no2,57.3,µg/m³
+Paris,FR,2019-05-10 06:00:00+00:00,FR04014,no2,47.4,µg/m³
+Paris,FR,2019-05-10 05:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-05-10 04:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-10 03:00:00+00:00,FR04014,no2,15.0,µg/m³
+Paris,FR,2019-05-10 02:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-05-10 01:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-05-10 00:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-09 23:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-05-09 22:00:00+00:00,FR04014,no2,29.7,µg/m³
+Paris,FR,2019-05-09 21:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-09 20:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-05-09 19:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-05-09 18:00:00+00:00,FR04014,no2,24.4,µg/m³
+Paris,FR,2019-05-09 17:00:00+00:00,FR04014,no2,29.9,µg/m³
+Paris,FR,2019-05-09 16:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-05-09 15:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-05-09 14:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-05-09 13:00:00+00:00,FR04014,no2,21.3,µg/m³
+Paris,FR,2019-05-09 12:00:00+00:00,FR04014,no2,35.1,µg/m³
+Paris,FR,2019-05-09 11:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-05-09 10:00:00+00:00,FR04014,no2,43.1,µg/m³
+Paris,FR,2019-05-09 09:00:00+00:00,FR04014,no2,32.3,µg/m³
+Paris,FR,2019-05-09 08:00:00+00:00,FR04014,no2,32.2,µg/m³
+Paris,FR,2019-05-09 07:00:00+00:00,FR04014,no2,49.0,µg/m³
+Paris,FR,2019-05-09 06:00:00+00:00,FR04014,no2,50.7,µg/m³
+Paris,FR,2019-05-09 05:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-09 04:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-09 03:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-05-09 02:00:00+00:00,FR04014,no2,10.0,µg/m³
+Paris,FR,2019-05-09 01:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-09 00:00:00+00:00,FR04014,no2,14.7,µg/m³
+Paris,FR,2019-05-08 23:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-08 22:00:00+00:00,FR04014,no2,32.2,µg/m³
+Paris,FR,2019-05-08 21:00:00+00:00,FR04014,no2,48.9,µg/m³
+Paris,FR,2019-05-08 20:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-05-08 19:00:00+00:00,FR04014,no2,41.3,µg/m³
+Paris,FR,2019-05-08 18:00:00+00:00,FR04014,no2,27.8,µg/m³
+Paris,FR,2019-05-08 17:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-05-08 16:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-05-08 15:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-05-08 14:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-08 13:00:00+00:00,FR04014,no2,14.3,µg/m³
+Paris,FR,2019-05-08 12:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-08 11:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-05-08 10:00:00+00:00,FR04014,no2,33.4,µg/m³
+Paris,FR,2019-05-08 09:00:00+00:00,FR04014,no2,19.7,µg/m³
+Paris,FR,2019-05-08 08:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-05-08 07:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-08 06:00:00+00:00,FR04014,no2,21.7,µg/m³
+Paris,FR,2019-05-08 05:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-05-08 04:00:00+00:00,FR04014,no2,15.5,µg/m³
+Paris,FR,2019-05-08 03:00:00+00:00,FR04014,no2,13.5,µg/m³
+Paris,FR,2019-05-08 02:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-08 01:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-05-08 00:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-07 23:00:00+00:00,FR04014,no2,34.0,µg/m³
+Paris,FR,2019-05-07 22:00:00+00:00,FR04014,no2,35.8,µg/m³
+Paris,FR,2019-05-07 21:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-05-07 20:00:00+00:00,FR04014,no2,36.2,µg/m³
+Paris,FR,2019-05-07 19:00:00+00:00,FR04014,no2,26.8,µg/m³
+Paris,FR,2019-05-07 18:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-05-07 17:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-07 16:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-05-07 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-07 14:00:00+00:00,FR04014,no2,11.0,µg/m³
+Paris,FR,2019-05-07 13:00:00+00:00,FR04014,no2,13.2,µg/m³
+Paris,FR,2019-05-07 12:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-07 11:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-07 10:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-07 09:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-07 08:00:00+00:00,FR04014,no2,56.0,µg/m³
+Paris,FR,2019-05-07 07:00:00+00:00,FR04014,no2,67.9,µg/m³
+Paris,FR,2019-05-07 06:00:00+00:00,FR04014,no2,77.7,µg/m³
+Paris,FR,2019-05-07 05:00:00+00:00,FR04014,no2,72.4,µg/m³
+Paris,FR,2019-05-07 04:00:00+00:00,FR04014,no2,61.9,µg/m³
+Paris,FR,2019-05-07 03:00:00+00:00,FR04014,no2,50.4,µg/m³
+Paris,FR,2019-05-07 02:00:00+00:00,FR04014,no2,27.7,µg/m³
+Paris,FR,2019-05-07 01:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-05-07 00:00:00+00:00,FR04014,no2,47.2,µg/m³
+Paris,FR,2019-05-06 23:00:00+00:00,FR04014,no2,53.1,µg/m³
+Paris,FR,2019-05-06 22:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-06 21:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-05-06 20:00:00+00:00,FR04014,no2,35.9,µg/m³
+Paris,FR,2019-05-06 19:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-05-06 18:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-06 17:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-05-06 16:00:00+00:00,FR04014,no2,38.4,µg/m³
+Paris,FR,2019-05-06 15:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-05-06 14:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-05-06 13:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-05-06 12:00:00+00:00,FR04014,no2,42.1,µg/m³
+Paris,FR,2019-05-06 11:00:00+00:00,FR04014,no2,44.3,µg/m³
+Paris,FR,2019-05-06 10:00:00+00:00,FR04014,no2,42.4,µg/m³
+Paris,FR,2019-05-06 09:00:00+00:00,FR04014,no2,44.2,µg/m³
+Paris,FR,2019-05-06 08:00:00+00:00,FR04014,no2,52.5,µg/m³
+Paris,FR,2019-05-06 07:00:00+00:00,FR04014,no2,68.9,µg/m³
+Paris,FR,2019-05-06 06:00:00+00:00,FR04014,no2,62.4,µg/m³
+Paris,FR,2019-05-06 05:00:00+00:00,FR04014,no2,56.7,µg/m³
+Paris,FR,2019-05-06 04:00:00+00:00,FR04014,no2,36.0,µg/m³
+Paris,FR,2019-05-06 03:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-05-06 02:00:00+00:00,FR04014,no2,25.1,µg/m³
+Paris,FR,2019-05-06 01:00:00+00:00,FR04014,no2,26.6,µg/m³
+Paris,FR,2019-05-06 00:00:00+00:00,FR04014,no2,26.8,µg/m³
+Paris,FR,2019-05-05 23:00:00+00:00,FR04014,no2,26.4,µg/m³
+Paris,FR,2019-05-05 22:00:00+00:00,FR04014,no2,28.6,µg/m³
+Paris,FR,2019-05-05 21:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-05-05 20:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-05-05 19:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-05 18:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-05 17:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-05 16:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-05-05 15:00:00+00:00,FR04014,no2,16.8,µg/m³
+Paris,FR,2019-05-05 14:00:00+00:00,FR04014,no2,17.6,µg/m³
+Paris,FR,2019-05-05 13:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-05 12:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-05 11:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-05-05 10:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-05 09:00:00+00:00,FR04014,no2,11.6,µg/m³
+Paris,FR,2019-05-05 08:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-05-05 07:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-05-05 06:00:00+00:00,FR04014,no2,26.4,µg/m³
+Paris,FR,2019-05-05 05:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-05-05 04:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-05-05 03:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-05 02:00:00+00:00,FR04014,no2,27.2,µg/m³
+Paris,FR,2019-05-05 01:00:00+00:00,FR04014,no2,25.7,µg/m³
+Paris,FR,2019-05-05 00:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-04 23:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-05-04 22:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-05-04 21:00:00+00:00,FR04014,no2,27.1,µg/m³
+Paris,FR,2019-05-04 20:00:00+00:00,FR04014,no2,33.1,µg/m³
+Paris,FR,2019-05-04 19:00:00+00:00,FR04014,no2,26.8,µg/m³
+Paris,FR,2019-05-04 18:00:00+00:00,FR04014,no2,16.7,µg/m³
+Paris,FR,2019-05-04 17:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-05-04 16:00:00+00:00,FR04014,no2,13.2,µg/m³
+Paris,FR,2019-05-04 15:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-05-04 14:00:00+00:00,FR04014,no2,17.1,µg/m³
+Paris,FR,2019-05-04 13:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-05-04 12:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-05-04 11:00:00+00:00,FR04014,no2,24.4,µg/m³
+Paris,FR,2019-05-04 10:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-05-04 09:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-05-04 08:00:00+00:00,FR04014,no2,22.5,µg/m³
+Paris,FR,2019-05-04 07:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-05-04 06:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-04 05:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-05-04 04:00:00+00:00,FR04014,no2,20.0,µg/m³
+Paris,FR,2019-05-04 03:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-04 02:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-04 01:00:00+00:00,FR04014,no2,22.2,µg/m³
+Paris,FR,2019-05-04 00:00:00+00:00,FR04014,no2,29.7,µg/m³
+Paris,FR,2019-05-03 23:00:00+00:00,FR04014,no2,31.3,µg/m³
+Paris,FR,2019-05-03 22:00:00+00:00,FR04014,no2,43.2,µg/m³
+Paris,FR,2019-05-03 21:00:00+00:00,FR04014,no2,31.8,µg/m³
+Paris,FR,2019-05-03 20:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-05-03 19:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-05-03 18:00:00+00:00,FR04014,no2,59.6,µg/m³
+Paris,FR,2019-05-03 17:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-03 16:00:00+00:00,FR04014,no2,33.0,µg/m³
+Paris,FR,2019-05-03 15:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-05-03 14:00:00+00:00,FR04014,no2,36.0,µg/m³
+Paris,FR,2019-05-03 13:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-05-03 12:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-05-03 11:00:00+00:00,FR04014,no2,38.2,µg/m³
+Paris,FR,2019-05-03 10:00:00+00:00,FR04014,no2,46.3,µg/m³
+Paris,FR,2019-05-03 09:00:00+00:00,FR04014,no2,39.8,µg/m³
+Paris,FR,2019-05-03 08:00:00+00:00,FR04014,no2,46.4,µg/m³
+Paris,FR,2019-05-03 07:00:00+00:00,FR04014,no2,48.1,µg/m³
+Paris,FR,2019-05-03 06:00:00+00:00,FR04014,no2,45.1,µg/m³
+Paris,FR,2019-05-03 05:00:00+00:00,FR04014,no2,32.8,µg/m³
+Paris,FR,2019-05-03 04:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-03 03:00:00+00:00,FR04014,no2,17.6,µg/m³
+Paris,FR,2019-05-03 02:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-03 01:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-03 00:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-05-02 23:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-05-02 22:00:00+00:00,FR04014,no2,31.1,µg/m³
+Paris,FR,2019-05-02 21:00:00+00:00,FR04014,no2,31.0,µg/m³
+Paris,FR,2019-05-02 20:00:00+00:00,FR04014,no2,28.6,µg/m³
+Paris,FR,2019-05-02 19:00:00+00:00,FR04014,no2,30.7,µg/m³
+Paris,FR,2019-05-02 18:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-02 17:00:00+00:00,FR04014,no2,29.9,µg/m³
+Paris,FR,2019-05-02 16:00:00+00:00,FR04014,no2,36.7,µg/m³
+Paris,FR,2019-05-02 15:00:00+00:00,FR04014,no2,41.4,µg/m³
+Paris,FR,2019-05-02 14:00:00+00:00,FR04014,no2,36.3,µg/m³
+Paris,FR,2019-05-02 13:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-05-02 12:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-05-02 11:00:00+00:00,FR04014,no2,32.6,µg/m³
+Paris,FR,2019-05-02 10:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-05-02 09:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-05-02 08:00:00+00:00,FR04014,no2,55.5,µg/m³
+Paris,FR,2019-05-02 07:00:00+00:00,FR04014,no2,51.0,µg/m³
+Paris,FR,2019-05-02 06:00:00+00:00,FR04014,no2,49.4,µg/m³
+Paris,FR,2019-05-02 05:00:00+00:00,FR04014,no2,35.8,µg/m³
+Paris,FR,2019-05-02 04:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-02 03:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-05-02 02:00:00+00:00,FR04014,no2,13.2,µg/m³
+Paris,FR,2019-05-02 01:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-05-02 00:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-05-01 23:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-01 22:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-05-01 21:00:00+00:00,FR04014,no2,24.4,µg/m³
+Paris,FR,2019-05-01 20:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-01 19:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-01 18:00:00+00:00,FR04014,no2,23.0,µg/m³
+Paris,FR,2019-05-01 17:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-01 16:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-05-01 15:00:00+00:00,FR04014,no2,24.4,µg/m³
+Paris,FR,2019-05-01 14:00:00+00:00,FR04014,no2,20.6,µg/m³
+Paris,FR,2019-05-01 13:00:00+00:00,FR04014,no2,22.5,µg/m³
+Paris,FR,2019-05-01 12:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-05-01 11:00:00+00:00,FR04014,no2,28.2,µg/m³
+Paris,FR,2019-05-01 10:00:00+00:00,FR04014,no2,33.3,µg/m³
+Paris,FR,2019-05-01 09:00:00+00:00,FR04014,no2,33.5,µg/m³
+Paris,FR,2019-05-01 08:00:00+00:00,FR04014,no2,33.5,µg/m³
+Paris,FR,2019-05-01 07:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-05-01 06:00:00+00:00,FR04014,no2,33.4,µg/m³
+Paris,FR,2019-05-01 05:00:00+00:00,FR04014,no2,28.5,µg/m³
+Paris,FR,2019-05-01 04:00:00+00:00,FR04014,no2,24.9,µg/m³
+Paris,FR,2019-05-01 03:00:00+00:00,FR04014,no2,23.1,µg/m³
+Paris,FR,2019-05-01 02:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-05-01 01:00:00+00:00,FR04014,no2,31.2,µg/m³
+Paris,FR,2019-05-01 00:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-04-30 23:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-04-30 22:00:00+00:00,FR04014,no2,41.3,µg/m³
+Paris,FR,2019-04-30 21:00:00+00:00,FR04014,no2,42.8,µg/m³
+Paris,FR,2019-04-30 20:00:00+00:00,FR04014,no2,39.6,µg/m³
+Paris,FR,2019-04-30 19:00:00+00:00,FR04014,no2,36.8,µg/m³
+Paris,FR,2019-04-30 18:00:00+00:00,FR04014,no2,27.2,µg/m³
+Paris,FR,2019-04-30 17:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-04-30 16:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-04-30 15:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-04-30 14:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-04-30 13:00:00+00:00,FR04014,no2,24.2,µg/m³
+Paris,FR,2019-04-30 12:00:00+00:00,FR04014,no2,21.5,µg/m³
+Paris,FR,2019-04-30 11:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-04-30 10:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-04-30 09:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-04-30 08:00:00+00:00,FR04014,no2,45.1,µg/m³
+Paris,FR,2019-04-30 07:00:00+00:00,FR04014,no2,44.1,µg/m³
+Paris,FR,2019-04-30 06:00:00+00:00,FR04014,no2,51.5,µg/m³
+Paris,FR,2019-04-30 05:00:00+00:00,FR04014,no2,37.3,µg/m³
+Paris,FR,2019-04-30 04:00:00+00:00,FR04014,no2,30.8,µg/m³
+Paris,FR,2019-04-30 03:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-04-30 02:00:00+00:00,FR04014,no2,22.8,µg/m³
+Paris,FR,2019-04-30 01:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-04-30 00:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-04-29 23:00:00+00:00,FR04014,no2,34.3,µg/m³
+Paris,FR,2019-04-29 22:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-04-29 21:00:00+00:00,FR04014,no2,31.6,µg/m³
+Paris,FR,2019-04-29 20:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-04-29 19:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-04-29 18:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-04-29 17:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-04-29 16:00:00+00:00,FR04014,no2,15.9,µg/m³
+Paris,FR,2019-04-29 15:00:00+00:00,FR04014,no2,15.0,µg/m³
+Paris,FR,2019-04-29 14:00:00+00:00,FR04014,no2,15.7,µg/m³
+Paris,FR,2019-04-29 13:00:00+00:00,FR04014,no2,14.3,µg/m³
+Paris,FR,2019-04-29 12:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-04-29 11:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-04-29 10:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-04-29 09:00:00+00:00,FR04014,no2,28.5,µg/m³
+Paris,FR,2019-04-29 08:00:00+00:00,FR04014,no2,39.1,µg/m³
+Paris,FR,2019-04-29 07:00:00+00:00,FR04014,no2,45.4,µg/m³
+Paris,FR,2019-04-29 06:00:00+00:00,FR04014,no2,52.6,µg/m³
+Paris,FR,2019-04-29 05:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-04-29 04:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-04-29 03:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-04-29 02:00:00+00:00,FR04014,no2,34.9,µg/m³
+Paris,FR,2019-04-29 01:00:00+00:00,FR04014,no2,25.5,µg/m³
+Paris,FR,2019-04-29 00:00:00+00:00,FR04014,no2,26.2,µg/m³
+Paris,FR,2019-04-28 23:00:00+00:00,FR04014,no2,29.8,µg/m³
+Paris,FR,2019-04-28 22:00:00+00:00,FR04014,no2,27.1,µg/m³
+Paris,FR,2019-04-28 21:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-04-28 20:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-04-28 19:00:00+00:00,FR04014,no2,32.3,µg/m³
+Paris,FR,2019-04-28 18:00:00+00:00,FR04014,no2,31.2,µg/m³
+Paris,FR,2019-04-28 17:00:00+00:00,FR04014,no2,23.7,µg/m³
+Paris,FR,2019-04-28 16:00:00+00:00,FR04014,no2,22.0,µg/m³
+Paris,FR,2019-04-28 15:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-04-28 14:00:00+00:00,FR04014,no2,18.4,µg/m³
+Paris,FR,2019-04-28 13:00:00+00:00,FR04014,no2,19.8,µg/m³
+Paris,FR,2019-04-28 12:00:00+00:00,FR04014,no2,20.7,µg/m³
+Paris,FR,2019-04-28 11:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-04-28 10:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-04-28 09:00:00+00:00,FR04014,no2,13.5,µg/m³
+Paris,FR,2019-04-28 08:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-04-28 07:00:00+00:00,FR04014,no2,15.9,µg/m³
+Paris,FR,2019-04-28 06:00:00+00:00,FR04014,no2,13.6,µg/m³
+Paris,FR,2019-04-28 05:00:00+00:00,FR04014,no2,12.7,µg/m³
+Paris,FR,2019-04-28 04:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-04-28 03:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-04-28 02:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-04-28 01:00:00+00:00,FR04014,no2,12.3,µg/m³
+Paris,FR,2019-04-28 00:00:00+00:00,FR04014,no2,14.8,µg/m³
+Paris,FR,2019-04-27 23:00:00+00:00,FR04014,no2,18.7,µg/m³
+Paris,FR,2019-04-27 22:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-04-27 21:00:00+00:00,FR04014,no2,16.7,µg/m³
+Paris,FR,2019-04-27 20:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-04-27 19:00:00+00:00,FR04014,no2,17.1,µg/m³
+Paris,FR,2019-04-27 18:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-04-27 17:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-04-27 16:00:00+00:00,FR04014,no2,18.6,µg/m³
+Paris,FR,2019-04-27 15:00:00+00:00,FR04014,no2,13.7,µg/m³
+Paris,FR,2019-04-27 14:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-04-27 13:00:00+00:00,FR04014,no2,13.9,µg/m³
+Paris,FR,2019-04-27 12:00:00+00:00,FR04014,no2,11.0,µg/m³
+Paris,FR,2019-04-27 11:00:00+00:00,FR04014,no2,12.3,µg/m³
+Paris,FR,2019-04-27 10:00:00+00:00,FR04014,no2,10.9,µg/m³
+Paris,FR,2019-04-27 09:00:00+00:00,FR04014,no2,11.9,µg/m³
+Paris,FR,2019-04-27 08:00:00+00:00,FR04014,no2,14.5,µg/m³
+Paris,FR,2019-04-27 07:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-04-27 06:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-04-27 05:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-04-27 04:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-04-27 03:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-04-27 02:00:00+00:00,FR04014,no2,8.6,µg/m³
+Paris,FR,2019-04-27 01:00:00+00:00,FR04014,no2,9.3,µg/m³
+Paris,FR,2019-04-27 00:00:00+00:00,FR04014,no2,10.8,µg/m³
+Paris,FR,2019-04-26 23:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-04-26 22:00:00+00:00,FR04014,no2,20.7,µg/m³
+Paris,FR,2019-04-26 21:00:00+00:00,FR04014,no2,34.8,µg/m³
+Paris,FR,2019-04-26 20:00:00+00:00,FR04014,no2,38.7,µg/m³
+Paris,FR,2019-04-26 19:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-04-26 18:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-04-26 17:00:00+00:00,FR04014,no2,20.2,µg/m³
+Paris,FR,2019-04-26 16:00:00+00:00,FR04014,no2,18.6,µg/m³
+Paris,FR,2019-04-26 15:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-04-26 14:00:00+00:00,FR04014,no2,18.6,µg/m³
+Paris,FR,2019-04-26 13:00:00+00:00,FR04014,no2,20.7,µg/m³
+Paris,FR,2019-04-26 12:00:00+00:00,FR04014,no2,27.2,µg/m³
+Paris,FR,2019-04-26 11:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-04-26 10:00:00+00:00,FR04014,no2,22.2,µg/m³
+Paris,FR,2019-04-26 09:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-04-26 08:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-04-26 07:00:00+00:00,FR04014,no2,47.2,µg/m³
+Paris,FR,2019-04-26 06:00:00+00:00,FR04014,no2,61.8,µg/m³
+Paris,FR,2019-04-26 05:00:00+00:00,FR04014,no2,70.9,µg/m³
+Paris,FR,2019-04-26 04:00:00+00:00,FR04014,no2,58.3,µg/m³
+Paris,FR,2019-04-26 03:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-04-26 02:00:00+00:00,FR04014,no2,27.8,µg/m³
+Paris,FR,2019-04-26 01:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-04-26 00:00:00+00:00,FR04014,no2,25.1,µg/m³
+Paris,FR,2019-04-25 23:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-04-25 22:00:00+00:00,FR04014,no2,31.0,µg/m³
+Paris,FR,2019-04-25 21:00:00+00:00,FR04014,no2,26.4,µg/m³
+Paris,FR,2019-04-25 20:00:00+00:00,FR04014,no2,26.8,µg/m³
+Paris,FR,2019-04-25 19:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-04-25 18:00:00+00:00,FR04014,no2,26.3,µg/m³
+Paris,FR,2019-04-25 17:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-04-25 16:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-04-25 15:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-04-25 14:00:00+00:00,FR04014,no2,21.2,µg/m³
+Paris,FR,2019-04-25 13:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-04-25 12:00:00+00:00,FR04014,no2,29.1,µg/m³
+Paris,FR,2019-04-25 11:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-04-25 10:00:00+00:00,FR04014,no2,45.1,µg/m³
+Paris,FR,2019-04-25 09:00:00+00:00,FR04014,no2,41.6,µg/m³
+Paris,FR,2019-04-25 08:00:00+00:00,FR04014,no2,37.6,µg/m³
+Paris,FR,2019-04-25 07:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-04-25 06:00:00+00:00,FR04014,no2,26.6,µg/m³
+Paris,FR,2019-04-25 05:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-04-25 04:00:00+00:00,FR04014,no2,16.7,µg/m³
+Paris,FR,2019-04-25 03:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-04-25 02:00:00+00:00,FR04014,no2,14.8,µg/m³
+Paris,FR,2019-04-25 01:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-04-25 00:00:00+00:00,FR04014,no2,23.1,µg/m³
+Paris,FR,2019-04-24 23:00:00+00:00,FR04014,no2,27.4,µg/m³
+Paris,FR,2019-04-24 22:00:00+00:00,FR04014,no2,36.0,µg/m³
+Paris,FR,2019-04-24 21:00:00+00:00,FR04014,no2,40.3,µg/m³
+Paris,FR,2019-04-24 20:00:00+00:00,FR04014,no2,41.0,µg/m³
+Paris,FR,2019-04-24 19:00:00+00:00,FR04014,no2,30.7,µg/m³
+Paris,FR,2019-04-24 18:00:00+00:00,FR04014,no2,22.5,µg/m³
+Paris,FR,2019-04-24 17:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-04-24 16:00:00+00:00,FR04014,no2,31.3,µg/m³
+Paris,FR,2019-04-24 15:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-04-24 14:00:00+00:00,FR04014,no2,26.6,µg/m³
+Paris,FR,2019-04-24 13:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-04-24 12:00:00+00:00,FR04014,no2,26.4,µg/m³
+Paris,FR,2019-04-24 11:00:00+00:00,FR04014,no2,22.4,µg/m³
+Paris,FR,2019-04-24 10:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-04-24 09:00:00+00:00,FR04014,no2,24.2,µg/m³
+Paris,FR,2019-04-24 08:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-04-24 07:00:00+00:00,FR04014,no2,33.0,µg/m³
+Paris,FR,2019-04-24 06:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-04-24 05:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-04-24 04:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-04-24 03:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-04-24 02:00:00+00:00,FR04014,no2,21.2,µg/m³
+Paris,FR,2019-04-24 01:00:00+00:00,FR04014,no2,26.4,µg/m³
+Paris,FR,2019-04-24 00:00:00+00:00,FR04014,no2,43.8,µg/m³
+Paris,FR,2019-04-23 23:00:00+00:00,FR04014,no2,48.8,µg/m³
+Paris,FR,2019-04-23 22:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-04-23 21:00:00+00:00,FR04014,no2,41.2,µg/m³
+Paris,FR,2019-04-23 20:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-04-23 19:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-04-23 18:00:00+00:00,FR04014,no2,33.0,µg/m³
+Paris,FR,2019-04-23 17:00:00+00:00,FR04014,no2,35.7,µg/m³
+Paris,FR,2019-04-23 16:00:00+00:00,FR04014,no2,52.9,µg/m³
+Paris,FR,2019-04-23 15:00:00+00:00,FR04014,no2,44.5,µg/m³
+Paris,FR,2019-04-23 14:00:00+00:00,FR04014,no2,48.8,µg/m³
+Paris,FR,2019-04-23 13:00:00+00:00,FR04014,no2,53.2,µg/m³
+Paris,FR,2019-04-23 12:00:00+00:00,FR04014,no2,54.1,µg/m³
+Paris,FR,2019-04-23 11:00:00+00:00,FR04014,no2,51.8,µg/m³
+Paris,FR,2019-04-23 10:00:00+00:00,FR04014,no2,47.9,µg/m³
+Paris,FR,2019-04-23 09:00:00+00:00,FR04014,no2,51.9,µg/m³
+Paris,FR,2019-04-23 08:00:00+00:00,FR04014,no2,60.7,µg/m³
+Paris,FR,2019-04-23 07:00:00+00:00,FR04014,no2,86.0,µg/m³
+Paris,FR,2019-04-23 06:00:00+00:00,FR04014,no2,74.7,µg/m³
+Paris,FR,2019-04-23 05:00:00+00:00,FR04014,no2,49.2,µg/m³
+Paris,FR,2019-04-23 04:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-04-23 03:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-04-23 02:00:00+00:00,FR04014,no2,32.4,µg/m³
+Paris,FR,2019-04-23 01:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-04-23 00:00:00+00:00,FR04014,no2,35.7,µg/m³
+Paris,FR,2019-04-22 23:00:00+00:00,FR04014,no2,45.6,µg/m³
+Paris,FR,2019-04-22 22:00:00+00:00,FR04014,no2,44.5,µg/m³
+Paris,FR,2019-04-22 21:00:00+00:00,FR04014,no2,38.4,µg/m³
+Paris,FR,2019-04-22 20:00:00+00:00,FR04014,no2,31.4,µg/m³
+Paris,FR,2019-04-22 19:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-04-22 18:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-04-22 17:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-04-22 16:00:00+00:00,FR04014,no2,13.9,µg/m³
+Paris,FR,2019-04-22 15:00:00+00:00,FR04014,no2,11.9,µg/m³
+Paris,FR,2019-04-22 14:00:00+00:00,FR04014,no2,8.9,µg/m³
+Paris,FR,2019-04-22 13:00:00+00:00,FR04014,no2,15.9,µg/m³
+Paris,FR,2019-04-22 12:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-04-22 11:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-04-22 10:00:00+00:00,FR04014,no2,43.5,µg/m³
+Paris,FR,2019-04-22 09:00:00+00:00,FR04014,no2,44.4,µg/m³
+Paris,FR,2019-04-22 08:00:00+00:00,FR04014,no2,63.7,µg/m³
+Paris,FR,2019-04-22 07:00:00+00:00,FR04014,no2,51.4,µg/m³
+Paris,FR,2019-04-22 06:00:00+00:00,FR04014,no2,65.7,µg/m³
+Paris,FR,2019-04-22 05:00:00+00:00,FR04014,no2,69.8,µg/m³
+Paris,FR,2019-04-22 04:00:00+00:00,FR04014,no2,80.2,µg/m³
+Paris,FR,2019-04-22 03:00:00+00:00,FR04014,no2,87.9,µg/m³
+Paris,FR,2019-04-22 02:00:00+00:00,FR04014,no2,88.7,µg/m³
+Paris,FR,2019-04-22 01:00:00+00:00,FR04014,no2,99.0,µg/m³
+Paris,FR,2019-04-22 00:00:00+00:00,FR04014,no2,116.4,µg/m³
+Paris,FR,2019-04-21 23:00:00+00:00,FR04014,no2,105.2,µg/m³
+Paris,FR,2019-04-21 22:00:00+00:00,FR04014,no2,117.2,µg/m³
+Paris,FR,2019-04-21 21:00:00+00:00,FR04014,no2,101.1,µg/m³
+Paris,FR,2019-04-21 20:00:00+00:00,FR04014,no2,75.6,µg/m³
+Paris,FR,2019-04-21 19:00:00+00:00,FR04014,no2,45.6,µg/m³
+Paris,FR,2019-04-21 18:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-04-21 17:00:00+00:00,FR04014,no2,15.6,µg/m³
+Paris,FR,2019-04-21 16:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-04-21 15:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-04-21 14:00:00+00:00,FR04014,no2,9.3,µg/m³
+Paris,FR,2019-04-21 13:00:00+00:00,FR04014,no2,9.8,µg/m³
+Paris,FR,2019-04-21 12:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-04-21 11:00:00+00:00,FR04014,no2,15.7,µg/m³
+Paris,FR,2019-04-21 10:00:00+00:00,FR04014,no2,15.6,µg/m³
+Paris,FR,2019-04-21 09:00:00+00:00,FR04014,no2,21.5,µg/m³
+Paris,FR,2019-04-21 08:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-04-21 07:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-04-21 06:00:00+00:00,FR04014,no2,34.0,µg/m³
+Paris,FR,2019-04-21 05:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-04-21 04:00:00+00:00,FR04014,no2,24.9,µg/m³
+Paris,FR,2019-04-21 03:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-04-21 02:00:00+00:00,FR04014,no2,28.7,µg/m³
+Paris,FR,2019-04-21 01:00:00+00:00,FR04014,no2,38.2,µg/m³
+Paris,FR,2019-04-21 00:00:00+00:00,FR04014,no2,40.5,µg/m³
+Paris,FR,2019-04-20 23:00:00+00:00,FR04014,no2,49.2,µg/m³
+Paris,FR,2019-04-20 22:00:00+00:00,FR04014,no2,52.8,µg/m³
+Paris,FR,2019-04-20 21:00:00+00:00,FR04014,no2,52.9,µg/m³
+Paris,FR,2019-04-20 20:00:00+00:00,FR04014,no2,39.2,µg/m³
+Paris,FR,2019-04-20 19:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-04-20 18:00:00+00:00,FR04014,no2,14.8,µg/m³
+Paris,FR,2019-04-20 17:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-04-20 16:00:00+00:00,FR04014,no2,12.7,µg/m³
+Paris,FR,2019-04-20 15:00:00+00:00,FR04014,no2,10.0,µg/m³
+Paris,FR,2019-04-20 14:00:00+00:00,FR04014,no2,9.8,µg/m³
+Paris,FR,2019-04-20 13:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-04-20 12:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-04-20 11:00:00+00:00,FR04014,no2,28.6,µg/m³
+Paris,FR,2019-04-20 10:00:00+00:00,FR04014,no2,39.8,µg/m³
+Paris,FR,2019-04-20 09:00:00+00:00,FR04014,no2,44.0,µg/m³
+Paris,FR,2019-04-20 08:00:00+00:00,FR04014,no2,46.3,µg/m³
+Paris,FR,2019-04-20 07:00:00+00:00,FR04014,no2,64.5,µg/m³
+Paris,FR,2019-04-20 06:00:00+00:00,FR04014,no2,67.1,µg/m³
+Paris,FR,2019-04-20 05:00:00+00:00,FR04014,no2,45.9,µg/m³
+Paris,FR,2019-04-20 04:00:00+00:00,FR04014,no2,31.5,µg/m³
+Paris,FR,2019-04-20 03:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-04-20 02:00:00+00:00,FR04014,no2,12.7,µg/m³
+Paris,FR,2019-04-20 01:00:00+00:00,FR04014,no2,14.5,µg/m³
+Paris,FR,2019-04-20 00:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-04-19 23:00:00+00:00,FR04014,no2,70.2,µg/m³
+Paris,FR,2019-04-19 22:00:00+00:00,FR04014,no2,90.4,µg/m³
+Paris,FR,2019-04-19 21:00:00+00:00,FR04014,no2,96.9,µg/m³
+Paris,FR,2019-04-19 20:00:00+00:00,FR04014,no2,78.4,µg/m³
+Paris,FR,2019-04-19 19:00:00+00:00,FR04014,no2,34.1,µg/m³
+Paris,FR,2019-04-19 18:00:00+00:00,FR04014,no2,20.2,µg/m³
+Paris,FR,2019-04-19 17:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-04-19 16:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-04-19 15:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-04-19 14:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-04-19 13:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-04-19 12:00:00+00:00,FR04014,no2,19.8,µg/m³
+Paris,FR,2019-04-19 11:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-04-19 10:00:00+00:00,FR04014,no2,51.3,µg/m³
+Paris,FR,2019-04-19 09:00:00+00:00,FR04014,no2,56.3,µg/m³
+Paris,FR,2019-04-19 08:00:00+00:00,FR04014,no2,61.4,µg/m³
+Paris,FR,2019-04-19 07:00:00+00:00,FR04014,no2,86.5,µg/m³
+Paris,FR,2019-04-19 06:00:00+00:00,FR04014,no2,89.3,µg/m³
+Paris,FR,2019-04-19 05:00:00+00:00,FR04014,no2,58.1,µg/m³
+Paris,FR,2019-04-19 04:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-04-19 03:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-04-19 02:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-04-19 01:00:00+00:00,FR04014,no2,17.1,µg/m³
+Paris,FR,2019-04-19 00:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-04-18 23:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-04-18 22:00:00+00:00,FR04014,no2,41.2,µg/m³
+Paris,FR,2019-04-18 21:00:00+00:00,FR04014,no2,52.7,µg/m³
+Paris,FR,2019-04-18 20:00:00+00:00,FR04014,no2,43.8,µg/m³
+Paris,FR,2019-04-18 19:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-04-18 18:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-04-18 17:00:00+00:00,FR04014,no2,16.0,µg/m³
+Paris,FR,2019-04-18 16:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-04-18 15:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-04-18 14:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-04-18 13:00:00+00:00,FR04014,no2,11.3,µg/m³
+Paris,FR,2019-04-18 12:00:00+00:00,FR04014,no2,12.7,µg/m³
+Paris,FR,2019-04-18 11:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-04-18 10:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-04-18 09:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-04-18 08:00:00+00:00,FR04014,no2,41.9,µg/m³
+Paris,FR,2019-04-18 07:00:00+00:00,FR04014,no2,43.8,µg/m³
+Paris,FR,2019-04-18 06:00:00+00:00,FR04014,no2,47.2,µg/m³
+Paris,FR,2019-04-18 05:00:00+00:00,FR04014,no2,39.8,µg/m³
+Paris,FR,2019-04-18 04:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-04-18 03:00:00+00:00,FR04014,no2,17.6,µg/m³
+Paris,FR,2019-04-18 02:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-04-18 01:00:00+00:00,FR04014,no2,18.9,µg/m³
+Paris,FR,2019-04-18 00:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-04-17 23:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-04-17 22:00:00+00:00,FR04014,no2,24.7,µg/m³
+Paris,FR,2019-04-17 21:00:00+00:00,FR04014,no2,37.3,µg/m³
+Paris,FR,2019-04-17 20:00:00+00:00,FR04014,no2,41.2,µg/m³
+Paris,FR,2019-04-17 19:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-04-17 18:00:00+00:00,FR04014,no2,17.4,µg/m³
+Paris,FR,2019-04-17 17:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-04-17 16:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-04-17 15:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-04-17 14:00:00+00:00,FR04014,no2,13.2,µg/m³
+Paris,FR,2019-04-17 13:00:00+00:00,FR04014,no2,11.9,µg/m³
+Paris,FR,2019-04-17 12:00:00+00:00,FR04014,no2,15.8,µg/m³
+Paris,FR,2019-04-17 11:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-04-17 10:00:00+00:00,FR04014,no2,46.9,µg/m³
+Paris,FR,2019-04-17 09:00:00+00:00,FR04014,no2,69.3,µg/m³
+Paris,FR,2019-04-17 08:00:00+00:00,FR04014,no2,72.7,µg/m³
+Paris,FR,2019-04-17 07:00:00+00:00,FR04014,no2,70.4,µg/m³
+Paris,FR,2019-04-17 06:00:00+00:00,FR04014,no2,72.9,µg/m³
+Paris,FR,2019-04-17 05:00:00+00:00,FR04014,no2,67.3,µg/m³
+Paris,FR,2019-04-17 04:00:00+00:00,FR04014,no2,65.5,µg/m³
+Paris,FR,2019-04-17 03:00:00+00:00,FR04014,no2,62.5,µg/m³
+Paris,FR,2019-04-17 02:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-04-17 01:00:00+00:00,FR04014,no2,30.7,µg/m³
+Paris,FR,2019-04-17 00:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-04-16 23:00:00+00:00,FR04014,no2,34.4,µg/m³
+Paris,FR,2019-04-16 22:00:00+00:00,FR04014,no2,30.9,µg/m³
+Paris,FR,2019-04-16 21:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-04-16 20:00:00+00:00,FR04014,no2,28.3,µg/m³
+Paris,FR,2019-04-16 19:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-04-16 18:00:00+00:00,FR04014,no2,39.4,µg/m³
+Paris,FR,2019-04-16 17:00:00+00:00,FR04014,no2,44.0,µg/m³
+Paris,FR,2019-04-16 16:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-04-16 15:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-04-16 14:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-04-16 13:00:00+00:00,FR04014,no2,36.3,µg/m³
+Paris,FR,2019-04-16 12:00:00+00:00,FR04014,no2,40.8,µg/m³
+Paris,FR,2019-04-16 11:00:00+00:00,FR04014,no2,38.8,µg/m³
+Paris,FR,2019-04-16 10:00:00+00:00,FR04014,no2,47.1,µg/m³
+Paris,FR,2019-04-16 09:00:00+00:00,FR04014,no2,57.5,µg/m³
+Paris,FR,2019-04-16 08:00:00+00:00,FR04014,no2,58.8,µg/m³
+Paris,FR,2019-04-16 07:00:00+00:00,FR04014,no2,72.0,µg/m³
+Paris,FR,2019-04-16 06:00:00+00:00,FR04014,no2,79.0,µg/m³
+Paris,FR,2019-04-16 05:00:00+00:00,FR04014,no2,76.9,µg/m³
+Paris,FR,2019-04-16 04:00:00+00:00,FR04014,no2,60.1,µg/m³
+Paris,FR,2019-04-16 03:00:00+00:00,FR04014,no2,34.6,µg/m³
+Paris,FR,2019-04-16 02:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-04-16 01:00:00+00:00,FR04014,no2,36.8,µg/m³
+Paris,FR,2019-04-16 00:00:00+00:00,FR04014,no2,29.7,µg/m³
+Paris,FR,2019-04-15 23:00:00+00:00,FR04014,no2,26.9,µg/m³
+Paris,FR,2019-04-15 22:00:00+00:00,FR04014,no2,29.9,µg/m³
+Paris,FR,2019-04-15 21:00:00+00:00,FR04014,no2,33.5,µg/m³
+Paris,FR,2019-04-15 20:00:00+00:00,FR04014,no2,40.9,µg/m³
+Paris,FR,2019-04-15 19:00:00+00:00,FR04014,no2,32.4,µg/m³
+Paris,FR,2019-04-15 18:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-04-15 17:00:00+00:00,FR04014,no2,15.5,µg/m³
+Paris,FR,2019-04-15 16:00:00+00:00,FR04014,no2,14.3,µg/m³
+Paris,FR,2019-04-15 15:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-04-15 14:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-04-15 13:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-04-15 12:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-04-15 11:00:00+00:00,FR04014,no2,13.6,µg/m³
+Paris,FR,2019-04-15 10:00:00+00:00,FR04014,no2,17.4,µg/m³
+Paris,FR,2019-04-15 09:00:00+00:00,FR04014,no2,28.0,µg/m³
+Paris,FR,2019-04-15 08:00:00+00:00,FR04014,no2,53.9,µg/m³
+Paris,FR,2019-04-15 07:00:00+00:00,FR04014,no2,61.2,µg/m³
+Paris,FR,2019-04-15 06:00:00+00:00,FR04014,no2,67.3,µg/m³
+Paris,FR,2019-04-15 05:00:00+00:00,FR04014,no2,52.9,µg/m³
+Paris,FR,2019-04-15 04:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-04-15 03:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-04-15 02:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-04-15 01:00:00+00:00,FR04014,no2,28.1,µg/m³
+Paris,FR,2019-04-15 00:00:00+00:00,FR04014,no2,29.5,µg/m³
+Paris,FR,2019-04-14 23:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-04-14 22:00:00+00:00,FR04014,no2,35.1,µg/m³
+Paris,FR,2019-04-14 21:00:00+00:00,FR04014,no2,34.4,µg/m³
+Paris,FR,2019-04-14 20:00:00+00:00,FR04014,no2,29.7,µg/m³
+Paris,FR,2019-04-14 19:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-04-14 18:00:00+00:00,FR04014,no2,21.5,µg/m³
+Paris,FR,2019-04-14 17:00:00+00:00,FR04014,no2,16.1,µg/m³
+Paris,FR,2019-04-14 16:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-04-14 15:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-04-14 14:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-04-14 13:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-04-14 12:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-04-14 11:00:00+00:00,FR04014,no2,19.7,µg/m³
+Paris,FR,2019-04-14 10:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-04-14 09:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-04-14 08:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-04-14 07:00:00+00:00,FR04014,no2,34.1,µg/m³
+Paris,FR,2019-04-14 06:00:00+00:00,FR04014,no2,33.6,µg/m³
+Paris,FR,2019-04-14 05:00:00+00:00,FR04014,no2,30.6,µg/m³
+Paris,FR,2019-04-14 04:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-04-14 03:00:00+00:00,FR04014,no2,33.3,µg/m³
+Paris,FR,2019-04-14 02:00:00+00:00,FR04014,no2,36.8,µg/m³
+Paris,FR,2019-04-14 01:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-04-14 00:00:00+00:00,FR04014,no2,41.1,µg/m³
+Paris,FR,2019-04-13 23:00:00+00:00,FR04014,no2,47.8,µg/m³
+Paris,FR,2019-04-13 22:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-04-13 21:00:00+00:00,FR04014,no2,43.8,µg/m³
+Paris,FR,2019-04-13 20:00:00+00:00,FR04014,no2,38.4,µg/m³
+Paris,FR,2019-04-13 19:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-04-13 18:00:00+00:00,FR04014,no2,21.1,µg/m³
+Paris,FR,2019-04-13 17:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-04-13 16:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-04-13 15:00:00+00:00,FR04014,no2,17.4,µg/m³
+Paris,FR,2019-04-13 14:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-04-13 13:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-04-13 12:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-04-13 11:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-04-13 10:00:00+00:00,FR04014,no2,18.3,µg/m³
+Paris,FR,2019-04-13 09:00:00+00:00,FR04014,no2,24.9,µg/m³
+Paris,FR,2019-04-13 08:00:00+00:00,FR04014,no2,35.2,µg/m³
+Paris,FR,2019-04-13 07:00:00+00:00,FR04014,no2,38.2,µg/m³
+Paris,FR,2019-04-13 06:00:00+00:00,FR04014,no2,44.3,µg/m³
+Paris,FR,2019-04-13 05:00:00+00:00,FR04014,no2,38.7,µg/m³
+Paris,FR,2019-04-13 04:00:00+00:00,FR04014,no2,31.9,µg/m³
+Paris,FR,2019-04-13 03:00:00+00:00,FR04014,no2,35.2,µg/m³
+Paris,FR,2019-04-13 02:00:00+00:00,FR04014,no2,38.9,µg/m³
+Paris,FR,2019-04-13 01:00:00+00:00,FR04014,no2,38.9,µg/m³
+Paris,FR,2019-04-13 00:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-04-12 23:00:00+00:00,FR04014,no2,40.0,µg/m³
+Paris,FR,2019-04-12 22:00:00+00:00,FR04014,no2,42.4,µg/m³
+Paris,FR,2019-04-12 21:00:00+00:00,FR04014,no2,41.6,µg/m³
+Paris,FR,2019-04-12 20:00:00+00:00,FR04014,no2,32.8,µg/m³
+Paris,FR,2019-04-12 19:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-04-12 18:00:00+00:00,FR04014,no2,26.2,µg/m³
+Paris,FR,2019-04-12 17:00:00+00:00,FR04014,no2,25.9,µg/m³
+Paris,FR,2019-04-12 16:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-04-12 15:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-04-12 14:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-04-12 13:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-04-12 12:00:00+00:00,FR04014,no2,18.6,µg/m³
+Paris,FR,2019-04-12 11:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-04-12 10:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-04-12 09:00:00+00:00,FR04014,no2,36.5,µg/m³
+Paris,FR,2019-04-12 08:00:00+00:00,FR04014,no2,44.3,µg/m³
+Paris,FR,2019-04-12 07:00:00+00:00,FR04014,no2,48.3,µg/m³
+Paris,FR,2019-04-12 06:00:00+00:00,FR04014,no2,52.6,µg/m³
+Paris,FR,2019-04-12 05:00:00+00:00,FR04014,no2,39.0,µg/m³
+Paris,FR,2019-04-12 04:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-04-12 03:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-04-12 02:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-04-12 01:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-04-12 00:00:00+00:00,FR04014,no2,25.7,µg/m³
+Paris,FR,2019-04-11 23:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-04-11 22:00:00+00:00,FR04014,no2,42.6,µg/m³
+Paris,FR,2019-04-11 21:00:00+00:00,FR04014,no2,40.7,µg/m³
+Paris,FR,2019-04-11 20:00:00+00:00,FR04014,no2,36.3,µg/m³
+Paris,FR,2019-04-11 19:00:00+00:00,FR04014,no2,31.4,µg/m³
+Paris,FR,2019-04-11 18:00:00+00:00,FR04014,no2,26.8,µg/m³
+Paris,FR,2019-04-11 17:00:00+00:00,FR04014,no2,20.9,µg/m³
+Paris,FR,2019-04-11 16:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-04-11 15:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-04-11 14:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-04-11 13:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-04-11 12:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-04-11 11:00:00+00:00,FR04014,no2,25.4,µg/m³
+Paris,FR,2019-04-11 10:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-04-11 09:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-04-11 08:00:00+00:00,FR04014,no2,43.2,µg/m³
+Paris,FR,2019-04-11 07:00:00+00:00,FR04014,no2,44.3,µg/m³
+Paris,FR,2019-04-11 06:00:00+00:00,FR04014,no2,45.7,µg/m³
+Paris,FR,2019-04-11 05:00:00+00:00,FR04014,no2,35.1,µg/m³
+Paris,FR,2019-04-11 04:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-04-11 03:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-04-11 02:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-04-11 01:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-04-11 00:00:00+00:00,FR04014,no2,27.4,µg/m³
+Paris,FR,2019-04-10 23:00:00+00:00,FR04014,no2,31.3,µg/m³
+Paris,FR,2019-04-10 22:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-04-10 21:00:00+00:00,FR04014,no2,35.1,µg/m³
+Paris,FR,2019-04-10 20:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-04-10 19:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-04-10 18:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-04-10 17:00:00+00:00,FR04014,no2,46.0,µg/m³
+Paris,FR,2019-04-10 16:00:00+00:00,FR04014,no2,36.2,µg/m³
+Paris,FR,2019-04-10 15:00:00+00:00,FR04014,no2,32.3,µg/m³
+Paris,FR,2019-04-10 14:00:00+00:00,FR04014,no2,26.2,µg/m³
+Paris,FR,2019-04-10 13:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-04-10 12:00:00+00:00,FR04014,no2,31.8,µg/m³
+Paris,FR,2019-04-10 11:00:00+00:00,FR04014,no2,34.4,µg/m³
+Paris,FR,2019-04-10 10:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-04-10 09:00:00+00:00,FR04014,no2,41.1,µg/m³
+Paris,FR,2019-04-10 08:00:00+00:00,FR04014,no2,45.2,µg/m³
+Paris,FR,2019-04-10 07:00:00+00:00,FR04014,no2,48.5,µg/m³
+Paris,FR,2019-04-10 06:00:00+00:00,FR04014,no2,40.6,µg/m³
+Paris,FR,2019-04-10 05:00:00+00:00,FR04014,no2,26.2,µg/m³
+Paris,FR,2019-04-10 04:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-04-10 03:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-04-10 02:00:00+00:00,FR04014,no2,18.6,µg/m³
+Paris,FR,2019-04-10 01:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-04-10 00:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-04-09 23:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-04-09 22:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-04-09 21:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-04-09 20:00:00+00:00,FR04014,no2,39.9,µg/m³
+Paris,FR,2019-04-09 19:00:00+00:00,FR04014,no2,48.7,µg/m³
+Paris,FR,2019-04-09 18:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-04-09 17:00:00+00:00,FR04014,no2,31.2,µg/m³
+Paris,FR,2019-04-09 16:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-04-09 15:00:00+00:00,FR04014,no2,24.2,µg/m³
+Paris,FR,2019-04-09 14:00:00+00:00,FR04014,no2,25.6,µg/m³
+Paris,FR,2019-04-09 13:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-04-09 12:00:00+00:00,FR04014,no2,30.6,µg/m³
+Paris,FR,2019-04-09 11:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-04-09 10:00:00+00:00,FR04014,no2,67.1,µg/m³
+Paris,FR,2019-04-09 09:00:00+00:00,FR04014,no2,66.5,µg/m³
+Paris,FR,2019-04-09 08:00:00+00:00,FR04014,no2,69.5,µg/m³
+Paris,FR,2019-04-09 07:00:00+00:00,FR04014,no2,68.0,µg/m³
+Paris,FR,2019-04-09 06:00:00+00:00,FR04014,no2,66.9,µg/m³
+Paris,FR,2019-04-09 05:00:00+00:00,FR04014,no2,59.5,µg/m³
+Paris,FR,2019-04-09 04:00:00+00:00,FR04014,no2,48.5,µg/m³
+Paris,FR,2019-04-09 03:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-04-09 02:00:00+00:00,FR04014,no2,27.4,µg/m³
+Paris,FR,2019-04-09 01:00:00+00:00,FR04014,no2,24.4,µg/m³
+Antwerpen,BE,2019-06-17 08:00:00+00:00,BETR801,no2,41.0,µg/m³
+Antwerpen,BE,2019-06-17 07:00:00+00:00,BETR801,no2,45.0,µg/m³
+Antwerpen,BE,2019-06-17 06:00:00+00:00,BETR801,no2,43.5,µg/m³
+Antwerpen,BE,2019-06-17 05:00:00+00:00,BETR801,no2,42.5,µg/m³
+Antwerpen,BE,2019-06-17 04:00:00+00:00,BETR801,no2,39.5,µg/m³
+Antwerpen,BE,2019-06-17 03:00:00+00:00,BETR801,no2,36.0,µg/m³
+Antwerpen,BE,2019-06-17 02:00:00+00:00,BETR801,no2,35.5,µg/m³
+Antwerpen,BE,2019-06-17 01:00:00+00:00,BETR801,no2,42.0,µg/m³
+Antwerpen,BE,2019-06-16 01:00:00+00:00,BETR801,no2,42.5,µg/m³
+Antwerpen,BE,2019-06-15 01:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-06-14 09:00:00+00:00,BETR801,no2,36.5,µg/m³
+Antwerpen,BE,2019-06-13 01:00:00+00:00,BETR801,no2,28.5,µg/m³
+Antwerpen,BE,2019-06-12 01:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-06-11 01:00:00+00:00,BETR801,no2,7.5,µg/m³
+Antwerpen,BE,2019-06-10 01:00:00+00:00,BETR801,no2,18.5,µg/m³
+Antwerpen,BE,2019-06-09 01:00:00+00:00,BETR801,no2,10.0,µg/m³
+Antwerpen,BE,2019-06-05 01:00:00+00:00,BETR801,no2,15.0,µg/m³
+Antwerpen,BE,2019-06-01 01:00:00+00:00,BETR801,no2,52.5,µg/m³
+Antwerpen,BE,2019-05-31 01:00:00+00:00,BETR801,no2,9.0,µg/m³
+Antwerpen,BE,2019-05-30 01:00:00+00:00,BETR801,no2,7.5,µg/m³
+Antwerpen,BE,2019-05-29 01:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-05-28 01:00:00+00:00,BETR801,no2,11.0,µg/m³
+Antwerpen,BE,2019-05-27 01:00:00+00:00,BETR801,no2,10.5,µg/m³
+Antwerpen,BE,2019-05-26 01:00:00+00:00,BETR801,no2,53.0,µg/m³
+Antwerpen,BE,2019-05-25 01:00:00+00:00,BETR801,no2,29.0,µg/m³
+Antwerpen,BE,2019-05-24 01:00:00+00:00,BETR801,no2,74.5,µg/m³
+Antwerpen,BE,2019-05-23 01:00:00+00:00,BETR801,no2,60.5,µg/m³
+Antwerpen,BE,2019-05-22 01:00:00+00:00,BETR801,no2,20.5,µg/m³
+Antwerpen,BE,2019-05-21 01:00:00+00:00,BETR801,no2,15.5,µg/m³
+Antwerpen,BE,2019-05-20 15:00:00+00:00,BETR801,no2,25.5,µg/m³
+Antwerpen,BE,2019-05-20 14:00:00+00:00,BETR801,no2,24.5,µg/m³
+Antwerpen,BE,2019-05-20 13:00:00+00:00,BETR801,no2,32.0,µg/m³
+Antwerpen,BE,2019-05-20 12:00:00+00:00,BETR801,no2,34.5,µg/m³
+Antwerpen,BE,2019-05-20 11:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-05-20 10:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-05-20 09:00:00+00:00,BETR801,no2,30.5,µg/m³
+Antwerpen,BE,2019-05-20 08:00:00+00:00,BETR801,no2,40.0,µg/m³
+Antwerpen,BE,2019-05-20 07:00:00+00:00,BETR801,no2,38.0,µg/m³
+Antwerpen,BE,2019-05-20 06:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-20 05:00:00+00:00,BETR801,no2,20.0,µg/m³
+Antwerpen,BE,2019-05-20 04:00:00+00:00,BETR801,no2,14.0,µg/m³
+Antwerpen,BE,2019-05-20 03:00:00+00:00,BETR801,no2,9.0,µg/m³
+Antwerpen,BE,2019-05-20 02:00:00+00:00,BETR801,no2,10.5,µg/m³
+Antwerpen,BE,2019-05-20 01:00:00+00:00,BETR801,no2,17.0,µg/m³
+Antwerpen,BE,2019-05-20 00:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-19 23:00:00+00:00,BETR801,no2,16.5,µg/m³
+Antwerpen,BE,2019-05-19 22:00:00+00:00,BETR801,no2,18.5,µg/m³
+Antwerpen,BE,2019-05-19 21:00:00+00:00,BETR801,no2,12.5,µg/m³
+Antwerpen,BE,2019-05-19 20:00:00+00:00,BETR801,no2,15.0,µg/m³
+Antwerpen,BE,2019-05-19 19:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-19 18:00:00+00:00,BETR801,no2,15.5,µg/m³
+Antwerpen,BE,2019-05-19 17:00:00+00:00,BETR801,no2,18.5,µg/m³
+Antwerpen,BE,2019-05-19 16:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-05-19 15:00:00+00:00,BETR801,no2,33.0,µg/m³
+Antwerpen,BE,2019-05-19 14:00:00+00:00,BETR801,no2,23.0,µg/m³
+Antwerpen,BE,2019-05-19 13:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-19 12:00:00+00:00,BETR801,no2,16.0,µg/m³
+Antwerpen,BE,2019-05-19 11:00:00+00:00,BETR801,no2,17.0,µg/m³
+Antwerpen,BE,2019-05-19 10:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-05-19 09:00:00+00:00,BETR801,no2,16.0,µg/m³
+Antwerpen,BE,2019-05-19 08:00:00+00:00,BETR801,no2,23.5,µg/m³
+Antwerpen,BE,2019-05-19 07:00:00+00:00,BETR801,no2,30.0,µg/m³
+Antwerpen,BE,2019-05-19 06:00:00+00:00,BETR801,no2,30.5,µg/m³
+Antwerpen,BE,2019-05-19 05:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-19 04:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-05-19 03:00:00+00:00,BETR801,no2,19.0,µg/m³
+Antwerpen,BE,2019-05-19 02:00:00+00:00,BETR801,no2,19.0,µg/m³
+Antwerpen,BE,2019-05-19 01:00:00+00:00,BETR801,no2,22.5,µg/m³
+Antwerpen,BE,2019-05-19 00:00:00+00:00,BETR801,no2,23.5,µg/m³
+Antwerpen,BE,2019-05-18 23:00:00+00:00,BETR801,no2,29.5,µg/m³
+Antwerpen,BE,2019-05-18 22:00:00+00:00,BETR801,no2,34.5,µg/m³
+Antwerpen,BE,2019-05-18 21:00:00+00:00,BETR801,no2,39.0,µg/m³
+Antwerpen,BE,2019-05-18 20:00:00+00:00,BETR801,no2,40.0,µg/m³
+Antwerpen,BE,2019-05-18 19:00:00+00:00,BETR801,no2,35.5,µg/m³
+Antwerpen,BE,2019-05-18 18:00:00+00:00,BETR801,no2,35.5,µg/m³
+Antwerpen,BE,2019-05-18 01:00:00+00:00,BETR801,no2,41.5,µg/m³
+Antwerpen,BE,2019-05-16 01:00:00+00:00,BETR801,no2,28.0,µg/m³
+Antwerpen,BE,2019-05-15 02:00:00+00:00,BETR801,no2,22.5,µg/m³
+Antwerpen,BE,2019-05-15 01:00:00+00:00,BETR801,no2,25.5,µg/m³
+Antwerpen,BE,2019-05-14 02:00:00+00:00,BETR801,no2,11.5,µg/m³
+Antwerpen,BE,2019-05-14 01:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-12 02:00:00+00:00,BETR801,no2,20.0,µg/m³
+Antwerpen,BE,2019-05-12 01:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-05-11 02:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-05-11 01:00:00+00:00,BETR801,no2,26.5,µg/m³
+Antwerpen,BE,2019-05-10 02:00:00+00:00,BETR801,no2,11.5,µg/m³
+Antwerpen,BE,2019-05-10 01:00:00+00:00,BETR801,no2,10.5,µg/m³
+Antwerpen,BE,2019-05-09 02:00:00+00:00,BETR801,no2,20.5,µg/m³
+Antwerpen,BE,2019-05-09 01:00:00+00:00,BETR801,no2,20.0,µg/m³
+Antwerpen,BE,2019-05-08 02:00:00+00:00,BETR801,no2,20.5,µg/m³
+Antwerpen,BE,2019-05-08 01:00:00+00:00,BETR801,no2,23.0,µg/m³
+Antwerpen,BE,2019-05-07 02:00:00+00:00,BETR801,no2,45.0,µg/m³
+Antwerpen,BE,2019-05-07 01:00:00+00:00,BETR801,no2,50.5,µg/m³
+Antwerpen,BE,2019-05-06 02:00:00+00:00,BETR801,no2,27.0,µg/m³
+Antwerpen,BE,2019-05-06 01:00:00+00:00,BETR801,no2,30.0,µg/m³
+Antwerpen,BE,2019-05-05 02:00:00+00:00,BETR801,no2,13.0,µg/m³
+Antwerpen,BE,2019-05-05 01:00:00+00:00,BETR801,no2,18.0,µg/m³
+Antwerpen,BE,2019-05-04 02:00:00+00:00,BETR801,no2,9.5,µg/m³
+Antwerpen,BE,2019-05-04 01:00:00+00:00,BETR801,no2,8.5,µg/m³
+Antwerpen,BE,2019-05-03 02:00:00+00:00,BETR801,no2,25.5,µg/m³
+Antwerpen,BE,2019-05-03 01:00:00+00:00,BETR801,no2,14.0,µg/m³
+Antwerpen,BE,2019-05-02 02:00:00+00:00,BETR801,no2,36.5,µg/m³
+Antwerpen,BE,2019-05-02 01:00:00+00:00,BETR801,no2,31.0,µg/m³
+Antwerpen,BE,2019-05-01 02:00:00+00:00,BETR801,no2,12.0,µg/m³
+Antwerpen,BE,2019-05-01 01:00:00+00:00,BETR801,no2,12.5,µg/m³
+Antwerpen,BE,2019-04-30 02:00:00+00:00,BETR801,no2,9.0,µg/m³
+Antwerpen,BE,2019-04-30 01:00:00+00:00,BETR801,no2,15.0,µg/m³
+Antwerpen,BE,2019-04-29 02:00:00+00:00,BETR801,no2,52.5,µg/m³
+Antwerpen,BE,2019-04-29 01:00:00+00:00,BETR801,no2,72.5,µg/m³
+Antwerpen,BE,2019-04-28 02:00:00+00:00,BETR801,no2,10.5,µg/m³
+Antwerpen,BE,2019-04-28 01:00:00+00:00,BETR801,no2,8.5,µg/m³
+Antwerpen,BE,2019-04-27 02:00:00+00:00,BETR801,no2,14.0,µg/m³
+Antwerpen,BE,2019-04-27 01:00:00+00:00,BETR801,no2,22.0,µg/m³
+Antwerpen,BE,2019-04-26 02:00:00+00:00,BETR801,no2,15.0,µg/m³
+Antwerpen,BE,2019-04-26 01:00:00+00:00,BETR801,no2,25.5,µg/m³
+Antwerpen,BE,2019-04-25 02:00:00+00:00,BETR801,no2,12.0,µg/m³
+Antwerpen,BE,2019-04-25 01:00:00+00:00,BETR801,no2,13.0,µg/m³
+Antwerpen,BE,2019-04-22 01:00:00+00:00,BETR801,no2,24.5,µg/m³
+Antwerpen,BE,2019-04-21 02:00:00+00:00,BETR801,no2,15.0,µg/m³
+Antwerpen,BE,2019-04-21 01:00:00+00:00,BETR801,no2,18.0,µg/m³
+Antwerpen,BE,2019-04-19 01:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-04-18 02:00:00+00:00,BETR801,no2,35.0,µg/m³
+Antwerpen,BE,2019-04-17 03:00:00+00:00,BETR801,no2,38.5,µg/m³
+Antwerpen,BE,2019-04-17 02:00:00+00:00,BETR801,no2,33.0,µg/m³
+Antwerpen,BE,2019-04-17 01:00:00+00:00,BETR801,no2,33.0,µg/m³
+Antwerpen,BE,2019-04-16 02:00:00+00:00,BETR801,no2,21.5,µg/m³
+Antwerpen,BE,2019-04-16 01:00:00+00:00,BETR801,no2,27.5,µg/m³
+Antwerpen,BE,2019-04-15 15:00:00+00:00,BETR801,no2,32.0,µg/m³
+Antwerpen,BE,2019-04-15 14:00:00+00:00,BETR801,no2,28.0,µg/m³
+Antwerpen,BE,2019-04-15 13:00:00+00:00,BETR801,no2,31.0,µg/m³
+Antwerpen,BE,2019-04-15 12:00:00+00:00,BETR801,no2,29.5,µg/m³
+Antwerpen,BE,2019-04-15 11:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-04-15 10:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-04-15 09:00:00+00:00,BETR801,no2,29.5,µg/m³
+Antwerpen,BE,2019-04-15 08:00:00+00:00,BETR801,no2,43.5,µg/m³
+Antwerpen,BE,2019-04-15 07:00:00+00:00,BETR801,no2,54.0,µg/m³
+Antwerpen,BE,2019-04-15 06:00:00+00:00,BETR801,no2,64.0,µg/m³
+Antwerpen,BE,2019-04-15 05:00:00+00:00,BETR801,no2,63.0,µg/m³
+Antwerpen,BE,2019-04-15 04:00:00+00:00,BETR801,no2,49.0,µg/m³
+Antwerpen,BE,2019-04-15 03:00:00+00:00,BETR801,no2,36.5,µg/m³
+Antwerpen,BE,2019-04-15 02:00:00+00:00,BETR801,no2,32.0,µg/m³
+Antwerpen,BE,2019-04-15 01:00:00+00:00,BETR801,no2,30.5,µg/m³
+Antwerpen,BE,2019-04-12 02:00:00+00:00,BETR801,no2,22.5,µg/m³
+Antwerpen,BE,2019-04-12 01:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-04-11 02:00:00+00:00,BETR801,no2,14.0,µg/m³
+Antwerpen,BE,2019-04-11 01:00:00+00:00,BETR801,no2,13.5,µg/m³
+Antwerpen,BE,2019-04-10 02:00:00+00:00,BETR801,no2,11.5,µg/m³
+Antwerpen,BE,2019-04-10 01:00:00+00:00,BETR801,no2,13.5,µg/m³
+Antwerpen,BE,2019-04-09 13:00:00+00:00,BETR801,no2,27.5,µg/m³
+Antwerpen,BE,2019-04-09 12:00:00+00:00,BETR801,no2,30.0,µg/m³
+Antwerpen,BE,2019-04-09 11:00:00+00:00,BETR801,no2,28.5,µg/m³
+Antwerpen,BE,2019-04-09 10:00:00+00:00,BETR801,no2,33.5,µg/m³
+Antwerpen,BE,2019-04-09 09:00:00+00:00,BETR801,no2,35.0,µg/m³
+Antwerpen,BE,2019-04-09 08:00:00+00:00,BETR801,no2,39.0,µg/m³
+Antwerpen,BE,2019-04-09 07:00:00+00:00,BETR801,no2,38.5,µg/m³
+Antwerpen,BE,2019-04-09 06:00:00+00:00,BETR801,no2,50.0,µg/m³
+Antwerpen,BE,2019-04-09 05:00:00+00:00,BETR801,no2,46.5,µg/m³
+Antwerpen,BE,2019-04-09 04:00:00+00:00,BETR801,no2,34.5,µg/m³
+Antwerpen,BE,2019-04-09 03:00:00+00:00,BETR801,no2,54.5,µg/m³
+Antwerpen,BE,2019-04-09 02:00:00+00:00,BETR801,no2,53.5,µg/m³
+Antwerpen,BE,2019-04-09 01:00:00+00:00,BETR801,no2,22.5,µg/m³
+London,GB,2019-06-17 11:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 10:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 09:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 08:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-17 07:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-17 06:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-17 05:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 04:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 03:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-17 02:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-17 01:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-17 00:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-16 23:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-16 20:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-16 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-16 18:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-16 17:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-16 14:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-16 13:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-16 12:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-16 11:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-16 10:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-16 09:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-16 08:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-16 07:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-16 06:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-16 05:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-16 04:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-16 03:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-16 02:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-16 01:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-16 00:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-15 23:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-15 22:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-15 21:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-15 20:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-15 19:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-15 18:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-15 17:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-15 16:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-15 15:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-15 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-15 13:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-15 12:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-15 11:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-15 10:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-15 09:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-15 08:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-15 07:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-15 06:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-15 05:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-15 04:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-15 00:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-14 20:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 19:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 18:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-14 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 16:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-14 15:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-14 14:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-14 13:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-14 12:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-14 11:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-14 10:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-14 09:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-14 08:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-14 07:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-14 06:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-14 05:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-14 04:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-14 03:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-14 02:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-14 00:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-13 23:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 22:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 21:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 20:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-13 19:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-13 18:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-13 17:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 16:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-13 15:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-13 14:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 13:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 12:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 11:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 10:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-13 09:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 08:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 07:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-13 06:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 05:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 04:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-13 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-13 00:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-12 23:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-12 21:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-12 20:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-06-12 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-12 18:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-12 17:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-12 16:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-12 15:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-06-12 14:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-12 13:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-12 12:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-12 11:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-12 10:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-12 09:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-12 08:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-12 07:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-12 06:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-12 05:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-12 04:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-12 03:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-12 00:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-11 23:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-11 22:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-11 21:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 20:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 19:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-06-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-11 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-06-11 16:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-11 15:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-11 14:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-11 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-11 12:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-11 11:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 10:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 09:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-11 08:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-11 07:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-11 06:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-11 05:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-11 04:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-11 03:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-11 02:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-11 01:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-11 00:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-10 23:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-10 22:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-10 21:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 20:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-10 19:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-10 17:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-10 16:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-10 15:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-06-10 14:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-06-10 13:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-06-10 12:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-10 11:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-06-10 10:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-10 08:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-10 07:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-10 06:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 05:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 04:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 03:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-10 02:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-10 01:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-10 00:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-09 23:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-09 21:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-09 20:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-09 19:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-09 18:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-09 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-09 16:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-09 15:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-09 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-09 13:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-09 12:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-09 11:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-09 10:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-09 09:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-09 08:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-09 07:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-09 06:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-09 05:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-09 04:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-09 03:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-09 02:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-09 01:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-09 00:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-08 23:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-08 21:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-08 20:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-08 19:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-08 18:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-08 17:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-08 16:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-08 15:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-08 14:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-08 13:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-08 12:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-08 11:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-08 10:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 09:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-08 08:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-08 07:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 06:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-08 05:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 04:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 03:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-08 02:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-08 00:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-07 23:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-07 21:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-07 20:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-07 19:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-07 18:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-07 16:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-07 15:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-07 14:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-07 12:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 11:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-07 10:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 09:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-07 08:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-07 07:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 06:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-07 05:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 04:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 03:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 02:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-06 23:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-06 22:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-06 21:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 20:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-06 19:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 18:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 16:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-06 15:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-06 14:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-06 13:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-06 12:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-06 11:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-06 10:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-06 09:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-06 08:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-06 07:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-06 06:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-06 05:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-06 04:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-06 03:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-06 02:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-06 00:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-05 23:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-05 22:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-05 21:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 20:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 19:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 18:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 17:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 16:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 15:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-05 14:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-05 13:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-05 12:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-05 11:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-05 10:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-05 09:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-05 08:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-05 07:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-05 06:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-05 05:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-05 04:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-05 03:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-05 02:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-05 01:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-05 00:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-04 23:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-04 22:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-04 21:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-04 20:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-04 19:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-04 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-04 17:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-06-04 16:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-06-04 15:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-06-04 14:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-04 13:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-06-04 12:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-04 11:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-04 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-04 09:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-04 08:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-04 07:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-04 06:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-04 05:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-04 04:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-04 03:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-04 02:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-04 01:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-04 00:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-03 23:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-03 22:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-03 21:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-03 20:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-03 19:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-03 18:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-03 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-03 16:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-03 15:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-03 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-03 13:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-03 12:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-03 11:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-03 10:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-03 09:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-03 08:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-03 07:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-03 06:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-03 05:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-03 04:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-03 03:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-03 02:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-03 01:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-03 00:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-02 23:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-02 22:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-02 21:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-02 20:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 19:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-02 18:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-02 17:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 15:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-02 14:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-02 13:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-02 12:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-02 11:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-02 10:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-02 09:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 08:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-06-02 07:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 06:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 05:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-06-02 04:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-06-02 03:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-02 02:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-02 01:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-06-02 00:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-06-01 23:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-06-01 22:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-06-01 21:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-06-01 20:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-06-01 19:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-06-01 18:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-01 17:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-01 16:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-01 15:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-01 14:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-01 13:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-01 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-01 11:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-01 10:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-01 09:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-01 08:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-01 07:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-01 06:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-01 05:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-01 04:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-01 03:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-01 02:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-01 01:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-01 00:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-31 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-31 20:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-31 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-31 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-31 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 16:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 15:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-31 14:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-31 13:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-31 12:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-31 11:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-31 10:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-31 09:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-31 08:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-05-31 07:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-31 06:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-05-31 05:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-31 04:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-31 03:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-05-31 02:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-05-31 01:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-31 00:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-30 23:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-30 22:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-30 21:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-30 20:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-30 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-30 18:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-30 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-30 16:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-30 15:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-30 14:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-30 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-30 12:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-30 11:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-05-30 10:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-30 09:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-30 08:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-05-30 07:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-05-30 06:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 05:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 04:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 03:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 02:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 01:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-05-30 00:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-05-29 23:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 22:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 21:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-05-29 20:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-05-29 19:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 18:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 17:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 16:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-05-29 15:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 14:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-29 13:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-05-29 12:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-29 11:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-29 10:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-29 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-29 08:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-29 07:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-29 06:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-29 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-29 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-29 03:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-29 02:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-29 01:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-29 00:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-28 23:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-28 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-28 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 19:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 17:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-28 16:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-28 15:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-28 14:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-28 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-28 11:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-28 10:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-28 09:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-28 08:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-28 07:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-28 06:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-28 05:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-28 04:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-28 03:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-28 02:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-28 01:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-28 00:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-27 23:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 22:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 21:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-27 20:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-27 19:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 18:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 17:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 16:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 15:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 14:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-27 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-27 12:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-27 11:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-27 10:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-27 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 08:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 07:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 06:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 03:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-27 02:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-27 01:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-27 00:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 21:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-26 20:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-26 19:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 18:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-26 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 16:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-26 15:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 14:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-26 13:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-26 12:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-26 11:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-26 10:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-26 09:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-26 08:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-26 07:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-26 06:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-26 05:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-26 04:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-26 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 01:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-26 00:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-25 23:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-25 22:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-25 21:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-25 20:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-25 19:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-25 18:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-25 17:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-25 16:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-25 15:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-25 14:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-25 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-25 12:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-25 11:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-25 10:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-25 09:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-25 08:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-25 07:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-25 06:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-25 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-25 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-25 03:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-25 02:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-25 01:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-25 00:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-24 23:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-24 22:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-24 21:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-24 20:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-24 19:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-24 18:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-24 17:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-24 16:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-05-24 15:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-24 14:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-24 13:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-24 12:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-24 11:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-24 10:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-24 09:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-24 08:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-24 07:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-24 06:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-24 05:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-24 04:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-24 03:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-24 02:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-24 00:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-23 23:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-23 22:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-23 21:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-23 20:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-05-23 19:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-05-23 18:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-05-23 17:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-05-23 16:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-05-23 15:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-23 14:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-23 13:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-23 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-23 11:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-23 10:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-23 09:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-23 08:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-23 07:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-23 06:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-23 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-23 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-23 03:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-23 02:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-23 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-23 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-22 23:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-22 22:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-22 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 20:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-22 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-22 18:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-22 17:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-22 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-22 15:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-22 14:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-22 13:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-22 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-22 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-22 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 09:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 08:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 07:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-22 06:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-22 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-22 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-22 03:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-22 02:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-22 01:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-22 00:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-21 23:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 22:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 21:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 20:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-21 19:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-21 18:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-21 17:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-21 16:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-21 15:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-21 14:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 13:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-21 12:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 11:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 10:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-21 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-21 08:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-21 07:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-21 06:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-21 05:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-21 04:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-21 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 01:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-21 00:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-20 23:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-20 22:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-20 21:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-20 20:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 19:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 18:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-20 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-20 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 15:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 14:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 13:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 10:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 08:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 07:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-20 06:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-20 05:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-20 04:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-20 03:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 02:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 01:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 00:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-19 23:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 22:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 21:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 20:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 19:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-19 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-19 17:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-19 16:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-19 15:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-19 14:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-19 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-19 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-19 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-19 10:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-19 09:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-19 08:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-19 07:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-19 06:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-19 05:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 04:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 03:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 02:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 01:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 00:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-18 23:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-18 22:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-18 21:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-18 20:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-18 19:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-18 18:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-18 17:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-18 16:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-18 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-18 14:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-18 13:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 12:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-18 11:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-18 10:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-18 09:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-18 08:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-18 07:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 06:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-18 05:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 04:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 03:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-18 02:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-18 01:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-18 00:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-17 23:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-17 22:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-17 21:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-17 20:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-17 19:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-17 18:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-17 17:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-17 16:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 15:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-17 14:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-17 13:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-17 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 11:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 10:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-17 08:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-17 07:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-17 06:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-17 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 03:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 02:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-17 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-16 23:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 22:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 20:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 19:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-16 18:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 14:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-16 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-16 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-16 11:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-16 09:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-16 08:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 07:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-16 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 05:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 04:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 03:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-16 02:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-16 01:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 00:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 23:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 22:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 21:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-15 20:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-15 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 18:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-15 17:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 16:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-15 15:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-15 14:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-15 13:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-15 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-15 11:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-15 10:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-15 09:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-15 08:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-15 07:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 05:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-15 04:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-15 03:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-15 02:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-15 00:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 20:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-14 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-14 18:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 17:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-14 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-14 15:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-14 14:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 13:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 11:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-14 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-14 09:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 08:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-14 07:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-14 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-14 05:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 04:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-14 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-13 23:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 22:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 20:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 19:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 18:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-13 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-13 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-13 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-13 14:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-13 13:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-13 12:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-13 11:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-13 10:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-13 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-13 08:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 07:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-13 06:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-13 05:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-13 04:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-13 03:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-13 02:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-13 01:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-13 00:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 23:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 22:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 21:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-12 20:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 19:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 18:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-12 16:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-12 15:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 14:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-12 13:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-12 12:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-12 11:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-12 10:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-12 09:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-12 08:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-12 07:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-12 06:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-12 05:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-12 04:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-12 03:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-12 02:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-12 01:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-12 00:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-11 23:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-11 22:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-11 21:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-11 20:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-11 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-11 17:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-11 16:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-11 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-11 09:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-11 08:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-11 07:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-11 06:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-11 05:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-11 04:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-11 03:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-11 02:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-11 01:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-11 00:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-10 23:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-10 22:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-10 21:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 19:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-10 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-10 16:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-10 15:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-10 14:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-10 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-10 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-10 11:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-10 08:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-10 07:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-10 06:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-10 05:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-10 04:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-10 03:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-10 02:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-10 01:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-05-10 00:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-05-09 23:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-05-09 22:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-05-09 21:00:00+00:00,London Westminster,no2,65.0,µg/m³
+London,GB,2019-05-09 20:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-05-09 19:00:00+00:00,London Westminster,no2,62.0,µg/m³
+London,GB,2019-05-09 18:00:00+00:00,London Westminster,no2,58.0,µg/m³
+London,GB,2019-05-09 17:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-05-09 16:00:00+00:00,London Westminster,no2,67.0,µg/m³
+London,GB,2019-05-09 15:00:00+00:00,London Westminster,no2,97.0,µg/m³
+London,GB,2019-05-09 14:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-09 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-09 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-09 11:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-09 10:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-09 09:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-09 08:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-09 07:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 05:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 04:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 03:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-09 02:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-09 00:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-08 23:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-08 21:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-08 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-08 19:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-08 18:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-08 17:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-08 16:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-08 15:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-08 14:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-08 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-08 12:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-08 11:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-08 10:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-08 09:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-08 08:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-08 07:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-08 06:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-08 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-08 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-08 03:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-08 02:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-08 01:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-08 00:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-07 23:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-07 21:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-07 20:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-07 19:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 18:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 17:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-07 16:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 15:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 14:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 12:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-07 11:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-07 10:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-07 09:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-07 08:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-07 07:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-07 06:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-07 04:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-07 03:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-07 02:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-06 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-06 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-06 21:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-06 20:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-06 19:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-06 18:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-06 17:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-06 16:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-06 15:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-06 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-06 13:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-06 12:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-06 11:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-06 10:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-06 09:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-06 08:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-06 07:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-06 06:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-06 05:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-06 04:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-06 03:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-06 02:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-06 01:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-06 00:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-05 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-05 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-05 21:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-05 20:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-05 19:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-05 18:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-05 17:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-05 16:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-05 15:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-05 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-05 13:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-05 12:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-05 11:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-05 10:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-05 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-05 08:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-05 07:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-05 06:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-05 05:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-05 04:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-05 03:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-05 02:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-05 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-05 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-04 23:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-04 22:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-04 21:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-04 20:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-04 19:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-04 18:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-04 17:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-04 16:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-04 15:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-04 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-04 13:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-04 12:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-04 11:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-04 10:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-04 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-04 08:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-04 07:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-04 06:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-04 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-04 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-04 03:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-04 02:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-04 01:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-04 00:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-03 23:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-03 22:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-03 21:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-03 20:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-03 19:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-03 18:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-03 17:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-03 16:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-03 15:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-03 14:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-03 13:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-03 12:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-03 11:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-05-03 10:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-03 09:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-03 08:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-03 07:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-03 06:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-03 05:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-03 04:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-03 03:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-03 02:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-03 01:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-03 00:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-02 23:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-02 22:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-02 21:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-05-02 20:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-05-02 19:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-02 18:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-02 17:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-02 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-02 15:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-02 14:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-02 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-02 12:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-02 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-02 10:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-02 09:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-02 08:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-02 07:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-02 06:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-02 05:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-02 04:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-02 03:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-02 02:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-02 01:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-02 00:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-01 23:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-01 22:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-01 21:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-01 20:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-01 19:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-01 18:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-01 17:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-01 16:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-01 15:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-01 14:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-01 13:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-01 12:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-01 11:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-01 10:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-01 09:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-01 08:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-01 07:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-01 06:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-01 05:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-01 04:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-01 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-01 00:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-04-30 23:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-30 22:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-30 21:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-30 20:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-04-30 19:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-30 18:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-30 17:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-30 16:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-30 15:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-30 14:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-30 13:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-30 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-30 11:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-30 10:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-30 09:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-30 08:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-30 07:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-30 06:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-30 05:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-30 04:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-30 03:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-30 02:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-30 01:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-30 00:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-29 23:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-29 22:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-29 21:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-29 20:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-29 19:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-29 18:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-29 17:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-29 16:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-29 15:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-29 14:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-29 13:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-29 12:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-29 11:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-04-29 10:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-29 09:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-29 08:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-29 07:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-29 06:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-29 05:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-29 04:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-29 03:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-29 02:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-29 01:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-29 00:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-28 23:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-28 22:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-28 21:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-28 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-28 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-28 18:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-28 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-28 16:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-28 15:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-28 14:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-28 13:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-28 12:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-28 11:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-28 10:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-28 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-04-27 13:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-04-27 12:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-27 11:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-04-27 10:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-04-27 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-04-27 08:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-04-27 07:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-04-27 06:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-04-27 05:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-04-27 04:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-04-27 03:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-04-27 02:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-04-27 00:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-04-26 23:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-04-26 22:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-04-26 21:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-04-26 20:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-26 19:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-26 18:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-04-26 17:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-04-26 16:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-04-26 15:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-26 14:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-26 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-04-26 12:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-26 11:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-04-26 10:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-26 09:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-26 08:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-26 07:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-26 06:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-26 05:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-26 04:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-26 03:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-26 02:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-26 01:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-26 00:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-25 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-25 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-25 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-04-25 20:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-25 19:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-04-25 18:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-25 17:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-25 16:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-25 15:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-25 14:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-25 13:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-04-25 12:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-25 11:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-25 10:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-25 09:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-25 08:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-25 07:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-25 06:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-25 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-25 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-25 03:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-25 02:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-25 00:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-24 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-24 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-24 21:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-24 20:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-24 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-24 18:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-24 17:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-24 16:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-24 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-24 14:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-24 13:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-24 12:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-24 11:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-24 10:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-24 09:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-04-24 08:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-04-24 07:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-24 06:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-24 05:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-24 04:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-24 03:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-04-24 02:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-04-24 00:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-23 23:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-23 22:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-23 21:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-23 20:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-23 19:00:00+00:00,London Westminster,no2,48.0,µg/m³
+London,GB,2019-04-23 18:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-23 17:00:00+00:00,London Westminster,no2,62.0,µg/m³
+London,GB,2019-04-23 16:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-23 15:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-23 14:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-23 13:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-04-23 12:00:00+00:00,London Westminster,no2,67.0,µg/m³
+London,GB,2019-04-23 11:00:00+00:00,London Westminster,no2,67.0,µg/m³
+London,GB,2019-04-23 10:00:00+00:00,London Westminster,no2,63.0,µg/m³
+London,GB,2019-04-23 09:00:00+00:00,London Westminster,no2,61.0,µg/m³
+London,GB,2019-04-23 08:00:00+00:00,London Westminster,no2,63.0,µg/m³
+London,GB,2019-04-23 07:00:00+00:00,London Westminster,no2,62.0,µg/m³
+London,GB,2019-04-23 06:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-23 05:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-23 04:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-23 03:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-23 02:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-23 01:00:00+00:00,London Westminster,no2,75.0,µg/m³
+London,GB,2019-04-23 00:00:00+00:00,London Westminster,no2,75.0,µg/m³
+London,GB,2019-04-22 23:00:00+00:00,London Westminster,no2,84.0,µg/m³
+London,GB,2019-04-22 22:00:00+00:00,London Westminster,no2,84.0,µg/m³
+London,GB,2019-04-22 21:00:00+00:00,London Westminster,no2,73.0,µg/m³
+London,GB,2019-04-22 20:00:00+00:00,London Westminster,no2,66.0,µg/m³
+London,GB,2019-04-22 19:00:00+00:00,London Westminster,no2,66.0,µg/m³
+London,GB,2019-04-22 18:00:00+00:00,London Westminster,no2,64.0,µg/m³
+London,GB,2019-04-22 17:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-22 16:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-22 15:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-22 14:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-22 13:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-22 12:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-22 11:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-22 10:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-22 09:00:00+00:00,London Westminster,no2,48.0,µg/m³
+London,GB,2019-04-22 08:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-22 07:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-22 06:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-04-22 05:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-22 04:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-22 03:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-22 02:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-22 01:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-22 00:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-21 23:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-21 22:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-21 21:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-21 20:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-21 19:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-21 18:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-21 17:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-21 16:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-21 15:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-21 14:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-21 13:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-21 12:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-21 11:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-21 10:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-21 09:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-04-21 08:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-21 07:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-21 06:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-21 05:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-21 04:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-21 03:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-21 02:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-21 01:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-04-21 00:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-04-20 23:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-20 22:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-20 21:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-20 20:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-20 19:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-20 18:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-20 17:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-20 16:00:00+00:00,London Westminster,no2,48.0,µg/m³
+London,GB,2019-04-20 15:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-20 14:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-20 13:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-20 12:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-20 11:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-20 10:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-20 09:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-20 08:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-20 07:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-20 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-20 05:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-20 04:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-20 03:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-04-20 02:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-04-20 01:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-04-20 00:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-04-19 23:00:00+00:00,London Westminster,no2,77.0,µg/m³
+London,GB,2019-04-19 22:00:00+00:00,London Westminster,no2,77.0,µg/m³
+London,GB,2019-04-19 21:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-19 20:00:00+00:00,London Westminster,no2,58.0,µg/m³
+London,GB,2019-04-19 19:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-19 18:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-19 17:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-04-19 16:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-19 15:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-19 14:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-19 13:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-19 12:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-19 11:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-19 10:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-04-19 09:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-19 08:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-19 07:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-19 06:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-04-19 05:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-04-19 04:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-04-19 03:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-19 02:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-19 00:00:00+00:00,London Westminster,no2,58.0,µg/m³
+London,GB,2019-04-18 23:00:00+00:00,London Westminster,no2,61.0,µg/m³
+London,GB,2019-04-18 22:00:00+00:00,London Westminster,no2,61.0,µg/m³
+London,GB,2019-04-18 21:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-04-18 20:00:00+00:00,London Westminster,no2,69.0,µg/m³
+London,GB,2019-04-18 19:00:00+00:00,London Westminster,no2,63.0,µg/m³
+London,GB,2019-04-18 18:00:00+00:00,London Westminster,no2,63.0,µg/m³
+London,GB,2019-04-18 17:00:00+00:00,London Westminster,no2,56.0,µg/m³
+London,GB,2019-04-18 16:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-18 15:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-18 14:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-18 13:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-18 12:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-18 11:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-18 10:00:00+00:00,London Westminster,no2,56.0,µg/m³
+London,GB,2019-04-18 09:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-18 08:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-18 07:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-18 06:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-18 05:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-18 04:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-18 03:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-18 02:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-18 01:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-18 00:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-17 23:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-17 22:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-17 21:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-04-17 20:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-04-17 19:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-17 18:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-17 17:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-04-17 16:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-17 15:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-17 14:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-04-17 13:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-17 12:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-04-17 11:00:00+00:00,London Westminster,no2,67.0,µg/m³
+London,GB,2019-04-17 10:00:00+00:00,London Westminster,no2,56.0,µg/m³
+London,GB,2019-04-17 09:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-17 08:00:00+00:00,London Westminster,no2,48.0,µg/m³
+London,GB,2019-04-17 07:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-17 06:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-17 05:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-17 04:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-17 03:00:00+00:00,London Westminster,no2,72.0,µg/m³
+London,GB,2019-04-17 02:00:00+00:00,London Westminster,no2,72.0,µg/m³
+London,GB,2019-04-17 00:00:00+00:00,London Westminster,no2,71.0,µg/m³
+London,GB,2019-04-16 23:00:00+00:00,London Westminster,no2,81.0,µg/m³
+London,GB,2019-04-16 22:00:00+00:00,London Westminster,no2,81.0,µg/m³
+London,GB,2019-04-16 21:00:00+00:00,London Westminster,no2,84.0,µg/m³
+London,GB,2019-04-16 20:00:00+00:00,London Westminster,no2,83.0,µg/m³
+London,GB,2019-04-16 19:00:00+00:00,London Westminster,no2,76.0,µg/m³
+London,GB,2019-04-16 18:00:00+00:00,London Westminster,no2,70.0,µg/m³
+London,GB,2019-04-16 17:00:00+00:00,London Westminster,no2,65.0,µg/m³
+London,GB,2019-04-16 15:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-16 14:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-16 13:00:00+00:00,London Westminster,no2,63.0,µg/m³
+London,GB,2019-04-16 12:00:00+00:00,London Westminster,no2,75.0,µg/m³
+London,GB,2019-04-16 11:00:00+00:00,London Westminster,no2,79.0,µg/m³
+London,GB,2019-04-16 10:00:00+00:00,London Westminster,no2,70.0,µg/m³
+London,GB,2019-04-16 09:00:00+00:00,London Westminster,no2,66.0,µg/m³
+London,GB,2019-04-16 08:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-04-16 07:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-16 06:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-04-16 05:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-16 04:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-16 03:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-16 02:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-16 00:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-15 23:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-04-15 22:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-04-15 21:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-15 20:00:00+00:00,London Westminster,no2,48.0,µg/m³
+London,GB,2019-04-15 19:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-15 18:00:00+00:00,London Westminster,no2,48.0,µg/m³
+London,GB,2019-04-15 17:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-04-15 16:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-15 15:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-15 14:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-15 13:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-15 12:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-15 11:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-15 10:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-15 09:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-15 08:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-15 07:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-15 06:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-15 05:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-15 04:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-15 03:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-15 02:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-15 01:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-15 00:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-14 23:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-14 22:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-14 21:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-14 20:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-14 19:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-14 18:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-14 17:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-14 16:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-04-14 15:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-04-14 14:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-04-14 13:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-14 12:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-14 11:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-14 10:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-04-14 09:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-14 08:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-14 07:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-14 06:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-04-14 05:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-04-14 04:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-04-14 03:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-14 02:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-14 01:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-14 00:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-13 23:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-13 22:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-13 21:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-13 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-13 19:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-13 18:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-04-13 17:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-13 16:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-13 15:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-13 14:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-13 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-13 12:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-13 11:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-13 10:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-13 09:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-13 08:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-13 07:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-04-13 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-13 05:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-13 04:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-13 03:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-13 02:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-13 01:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-13 00:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-12 23:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-12 22:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-12 21:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-12 20:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-04-12 19:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-12 18:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-12 17:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-12 16:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-04-12 15:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-04-12 14:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-12 13:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-12 12:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-12 11:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-12 10:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-12 09:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-12 08:00:00+00:00,London Westminster,no2,57.0,µg/m³
+London,GB,2019-04-12 07:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-12 06:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-12 05:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-12 04:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-12 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-12 00:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-11 23:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-11 22:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-11 21:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-11 20:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-11 19:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-04-11 18:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-04-11 17:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-11 16:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-11 15:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-11 14:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-11 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-04-11 12:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-11 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-11 10:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-11 09:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-11 08:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-11 07:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-04-11 06:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-04-11 05:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-11 04:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-11 03:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-11 02:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-04-11 00:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-10 23:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-10 22:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-10 21:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-10 20:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-10 19:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-04-10 18:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-10 17:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-10 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-04-10 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-10 14:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-04-10 13:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-04-10 12:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-10 11:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-04-10 10:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-04-10 09:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-04-10 08:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-10 07:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-10 06:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-10 05:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-10 04:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-04-10 03:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-10 02:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-04-10 01:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-04-10 00:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-04-09 23:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-09 22:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-04-09 21:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-09 20:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-04-09 19:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-09 18:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-04-09 17:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-04-09 16:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-04-09 15:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-04-09 14:00:00+00:00,London Westminster,no2,58.0,µg/m³
+London,GB,2019-04-09 13:00:00+00:00,London Westminster,no2,56.0,µg/m³
+London,GB,2019-04-09 12:00:00+00:00,London Westminster,no2,55.0,µg/m³
+London,GB,2019-04-09 11:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-09 10:00:00+00:00,London Westminster,no2,50.0,µg/m³
+London,GB,2019-04-09 09:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-04-09 08:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-04-09 07:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-04-09 06:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-09 05:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-09 04:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-04-09 03:00:00+00:00,London Westminster,no2,67.0,µg/m³
+London,GB,2019-04-09 02:00:00+00:00,London Westminster,no2,67.0,µg/m³
diff --git a/doc/data/air_quality_no2.csv b/doc/data/air_quality_no2.csv
new file mode 100644
index 0000000000000..7fa879f7c7e78
--- /dev/null
+++ b/doc/data/air_quality_no2.csv
@@ -0,0 +1,1036 @@
+datetime,station_antwerp,station_paris,station_london
+2019-05-07 02:00:00,,,23.0
+2019-05-07 03:00:00,50.5,25.0,19.0
+2019-05-07 04:00:00,45.0,27.7,19.0
+2019-05-07 05:00:00,,50.4,16.0
+2019-05-07 06:00:00,,61.9,
+2019-05-07 07:00:00,,72.4,26.0
+2019-05-07 08:00:00,,77.7,32.0
+2019-05-07 09:00:00,,67.9,32.0
+2019-05-07 10:00:00,,56.0,28.0
+2019-05-07 11:00:00,,34.5,21.0
+2019-05-07 12:00:00,,20.1,21.0
+2019-05-07 13:00:00,,13.0,18.0
+2019-05-07 14:00:00,,10.6,20.0
+2019-05-07 15:00:00,,13.2,18.0
+2019-05-07 16:00:00,,11.0,20.0
+2019-05-07 17:00:00,,11.7,20.0
+2019-05-07 18:00:00,,18.2,21.0
+2019-05-07 19:00:00,,22.3,20.0
+2019-05-07 20:00:00,,21.4,20.0
+2019-05-07 21:00:00,,26.8,24.0
+2019-05-07 22:00:00,,36.2,24.0
+2019-05-07 23:00:00,,33.9,
+2019-05-08 00:00:00,,35.8,24.0
+2019-05-08 01:00:00,,34.0,19.0
+2019-05-08 02:00:00,,22.1,19.0
+2019-05-08 03:00:00,23.0,19.6,20.0
+2019-05-08 04:00:00,20.5,15.3,20.0
+2019-05-08 05:00:00,,13.5,19.0
+2019-05-08 06:00:00,,15.5,19.0
+2019-05-08 07:00:00,,19.3,29.0
+2019-05-08 08:00:00,,21.7,34.0
+2019-05-08 09:00:00,,19.5,36.0
+2019-05-08 10:00:00,,17.0,33.0
+2019-05-08 11:00:00,,19.7,28.0
+2019-05-08 12:00:00,,33.4,27.0
+2019-05-08 13:00:00,,21.4,26.0
+2019-05-08 14:00:00,,15.1,26.0
+2019-05-08 15:00:00,,14.3,24.0
+2019-05-08 16:00:00,,25.3,27.0
+2019-05-08 17:00:00,,26.0,28.0
+2019-05-08 18:00:00,,38.6,31.0
+2019-05-08 19:00:00,,29.3,40.0
+2019-05-08 20:00:00,,27.8,25.0
+2019-05-08 21:00:00,,41.3,29.0
+2019-05-08 22:00:00,,38.3,26.0
+2019-05-08 23:00:00,,48.9,
+2019-05-09 00:00:00,,32.2,25.0
+2019-05-09 01:00:00,,25.2,30.0
+2019-05-09 02:00:00,,14.7,
+2019-05-09 03:00:00,20.0,10.6,31.0
+2019-05-09 04:00:00,20.5,10.0,31.0
+2019-05-09 05:00:00,,10.4,33.0
+2019-05-09 06:00:00,,15.3,33.0
+2019-05-09 07:00:00,,34.5,33.0
+2019-05-09 08:00:00,,50.7,33.0
+2019-05-09 09:00:00,,49.0,35.0
+2019-05-09 10:00:00,,32.2,36.0
+2019-05-09 11:00:00,,32.3,28.0
+2019-05-09 12:00:00,,43.1,27.0
+2019-05-09 13:00:00,,34.2,30.0
+2019-05-09 14:00:00,,35.1,27.0
+2019-05-09 15:00:00,,21.3,34.0
+2019-05-09 16:00:00,,24.6,97.0
+2019-05-09 17:00:00,,23.9,67.0
+2019-05-09 18:00:00,,27.0,60.0
+2019-05-09 19:00:00,,29.9,58.0
+2019-05-09 20:00:00,,24.4,62.0
+2019-05-09 21:00:00,,23.8,59.0
+2019-05-09 22:00:00,,29.2,65.0
+2019-05-09 23:00:00,,34.5,59.0
+2019-05-10 00:00:00,,29.7,59.0
+2019-05-10 01:00:00,,26.7,52.0
+2019-05-10 02:00:00,,22.7,52.0
+2019-05-10 03:00:00,10.5,19.1,41.0
+2019-05-10 04:00:00,11.5,14.1,41.0
+2019-05-10 05:00:00,,15.0,40.0
+2019-05-10 06:00:00,,20.5,40.0
+2019-05-10 07:00:00,,37.8,39.0
+2019-05-10 08:00:00,,47.4,36.0
+2019-05-10 09:00:00,,57.3,39.0
+2019-05-10 10:00:00,,60.7,34.0
+2019-05-10 11:00:00,,53.4,31.0
+2019-05-10 12:00:00,,35.1,29.0
+2019-05-10 13:00:00,,23.2,28.0
+2019-05-10 14:00:00,,25.3,26.0
+2019-05-10 15:00:00,,22.0,25.0
+2019-05-10 16:00:00,,29.3,25.0
+2019-05-10 17:00:00,,29.6,24.0
+2019-05-10 18:00:00,,30.8,26.0
+2019-05-10 19:00:00,,37.8,26.0
+2019-05-10 20:00:00,,33.4,29.0
+2019-05-10 21:00:00,,39.3,29.0
+2019-05-10 22:00:00,,43.6,29.0
+2019-05-10 23:00:00,,37.0,31.0
+2019-05-11 00:00:00,,28.1,31.0
+2019-05-11 01:00:00,,26.0,27.0
+2019-05-11 02:00:00,,24.8,27.0
+2019-05-11 03:00:00,26.5,15.5,32.0
+2019-05-11 04:00:00,21.0,14.9,32.0
+2019-05-11 05:00:00,,,35.0
+2019-05-11 06:00:00,,,35.0
+2019-05-11 07:00:00,,,30.0
+2019-05-11 08:00:00,,28.9,30.0
+2019-05-11 09:00:00,,29.0,27.0
+2019-05-11 10:00:00,,32.1,30.0
+2019-05-11 11:00:00,,35.7,
+2019-05-11 12:00:00,,36.8,
+2019-05-11 13:00:00,,33.2,
+2019-05-11 14:00:00,,30.2,
+2019-05-11 15:00:00,,30.8,
+2019-05-11 16:00:00,,17.8,28.0
+2019-05-11 17:00:00,,18.0,26.0
+2019-05-11 18:00:00,,19.5,28.0
+2019-05-11 19:00:00,,32.0,31.0
+2019-05-11 20:00:00,,33.1,33.0
+2019-05-11 21:00:00,,31.2,33.0
+2019-05-11 22:00:00,,24.2,34.0
+2019-05-11 23:00:00,,21.1,37.0
+2019-05-12 00:00:00,,27.7,37.0
+2019-05-12 01:00:00,,26.4,35.0
+2019-05-12 02:00:00,,22.8,35.0
+2019-05-12 03:00:00,17.5,19.2,38.0
+2019-05-12 04:00:00,20.0,17.2,38.0
+2019-05-12 05:00:00,,16.0,36.0
+2019-05-12 06:00:00,,16.2,36.0
+2019-05-12 07:00:00,,19.2,38.0
+2019-05-12 08:00:00,,20.1,44.0
+2019-05-12 09:00:00,,15.9,32.0
+2019-05-12 10:00:00,,14.6,26.0
+2019-05-12 11:00:00,,11.7,26.0
+2019-05-12 12:00:00,,11.4,21.0
+2019-05-12 13:00:00,,11.4,20.0
+2019-05-12 14:00:00,,10.9,19.0
+2019-05-12 15:00:00,,8.7,21.0
+2019-05-12 16:00:00,,9.1,22.0
+2019-05-12 17:00:00,,9.6,23.0
+2019-05-12 18:00:00,,11.7,24.0
+2019-05-12 19:00:00,,13.9,22.0
+2019-05-12 20:00:00,,18.2,22.0
+2019-05-12 21:00:00,,19.5,22.0
+2019-05-12 22:00:00,,24.1,21.0
+2019-05-12 23:00:00,,34.2,22.0
+2019-05-13 00:00:00,,46.5,22.0
+2019-05-13 01:00:00,,32.5,22.0
+2019-05-13 02:00:00,,25.0,22.0
+2019-05-13 03:00:00,14.5,18.9,24.0
+2019-05-13 04:00:00,14.5,18.5,24.0
+2019-05-13 05:00:00,,18.9,33.0
+2019-05-13 06:00:00,,25.1,33.0
+2019-05-13 07:00:00,,38.3,39.0
+2019-05-13 08:00:00,,45.2,39.0
+2019-05-13 09:00:00,,41.0,31.0
+2019-05-13 10:00:00,,32.1,29.0
+2019-05-13 11:00:00,,20.6,27.0
+2019-05-13 12:00:00,,12.8,26.0
+2019-05-13 13:00:00,,9.6,24.0
+2019-05-13 14:00:00,,9.2,25.0
+2019-05-13 15:00:00,,10.1,26.0
+2019-05-13 16:00:00,,10.7,28.0
+2019-05-13 17:00:00,,10.6,29.0
+2019-05-13 18:00:00,,12.1,30.0
+2019-05-13 19:00:00,,13.0,30.0
+2019-05-13 20:00:00,,15.5,31.0
+2019-05-13 21:00:00,,23.9,31.0
+2019-05-13 22:00:00,,28.3,31.0
+2019-05-13 23:00:00,,30.4,31.0
+2019-05-14 00:00:00,,27.3,31.0
+2019-05-14 01:00:00,,22.8,23.0
+2019-05-14 02:00:00,,20.9,23.0
+2019-05-14 03:00:00,14.5,19.1,26.0
+2019-05-14 04:00:00,11.5,19.0,26.0
+2019-05-14 05:00:00,,22.1,30.0
+2019-05-14 06:00:00,,31.6,30.0
+2019-05-14 07:00:00,,38.6,33.0
+2019-05-14 08:00:00,,46.1,34.0
+2019-05-14 09:00:00,,41.3,33.0
+2019-05-14 10:00:00,,28.8,30.0
+2019-05-14 11:00:00,,19.0,31.0
+2019-05-14 12:00:00,,12.9,27.0
+2019-05-14 13:00:00,,11.3,25.0
+2019-05-14 14:00:00,,10.2,25.0
+2019-05-14 15:00:00,,11.0,25.0
+2019-05-14 16:00:00,,15.2,29.0
+2019-05-14 17:00:00,,13.4,32.0
+2019-05-14 18:00:00,,15.3,33.0
+2019-05-14 19:00:00,,17.7,30.0
+2019-05-14 20:00:00,,17.9,28.0
+2019-05-14 21:00:00,,23.3,27.0
+2019-05-14 22:00:00,,28.4,25.0
+2019-05-14 23:00:00,,29.0,26.0
+2019-05-15 00:00:00,,30.9,26.0
+2019-05-15 01:00:00,,24.3,22.0
+2019-05-15 02:00:00,,18.8,
+2019-05-15 03:00:00,25.5,17.2,22.0
+2019-05-15 04:00:00,22.5,16.8,22.0
+2019-05-15 05:00:00,,17.9,25.0
+2019-05-15 06:00:00,,28.9,25.0
+2019-05-15 07:00:00,,46.5,33.0
+2019-05-15 08:00:00,,48.1,33.0
+2019-05-15 09:00:00,,32.1,34.0
+2019-05-15 10:00:00,,25.7,35.0
+2019-05-15 11:00:00,,0.0,36.0
+2019-05-15 12:00:00,,0.0,35.0
+2019-05-15 13:00:00,,0.0,30.0
+2019-05-15 14:00:00,,9.4,31.0
+2019-05-15 15:00:00,,10.0,30.0
+2019-05-15 16:00:00,,11.9,38.0
+2019-05-15 17:00:00,,12.9,38.0
+2019-05-15 18:00:00,,12.2,33.0
+2019-05-15 19:00:00,,12.9,35.0
+2019-05-15 20:00:00,,16.5,33.0
+2019-05-15 21:00:00,,20.3,31.0
+2019-05-15 22:00:00,,30.1,32.0
+2019-05-15 23:00:00,,36.0,33.0
+2019-05-16 00:00:00,,44.1,33.0
+2019-05-16 01:00:00,,30.9,33.0
+2019-05-16 02:00:00,,27.4,33.0
+2019-05-16 03:00:00,28.0,26.0,28.0
+2019-05-16 04:00:00,,26.7,28.0
+2019-05-16 05:00:00,,27.9,26.0
+2019-05-16 06:00:00,,37.0,26.0
+2019-05-16 07:00:00,,52.6,33.0
+2019-05-16 08:00:00,,,34.0
+2019-05-16 09:00:00,,40.0,33.0
+2019-05-16 10:00:00,,39.4,32.0
+2019-05-16 11:00:00,,29.5,31.0
+2019-05-16 12:00:00,,13.5,33.0
+2019-05-16 13:00:00,,10.5,30.0
+2019-05-16 14:00:00,,9.2,27.0
+2019-05-16 15:00:00,,8.5,27.0
+2019-05-16 16:00:00,,8.1,26.0
+2019-05-16 17:00:00,,10.1,29.0
+2019-05-16 18:00:00,,10.3,30.0
+2019-05-16 19:00:00,,13.5,25.0
+2019-05-16 20:00:00,,15.9,27.0
+2019-05-16 21:00:00,,14.4,26.0
+2019-05-16 22:00:00,,24.8,25.0
+2019-05-16 23:00:00,,24.3,25.0
+2019-05-17 00:00:00,,37.1,25.0
+2019-05-17 01:00:00,,43.7,23.0
+2019-05-17 02:00:00,,46.3,23.0
+2019-05-17 03:00:00,,26.1,21.0
+2019-05-17 04:00:00,,24.6,21.0
+2019-05-17 05:00:00,,26.6,21.0
+2019-05-17 06:00:00,,28.4,21.0
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+2019-06-18 10:00:00,,49.6,
+2019-06-18 21:00:00,,15.3,
+2019-06-18 22:00:00,,17.0,
+2019-06-18 23:00:00,,23.1,
+2019-06-19 00:00:00,,39.3,
+2019-06-19 11:00:00,,27.3,
+2019-06-19 12:00:00,,26.6,
+2019-06-20 15:00:00,,19.4,
+2019-06-20 16:00:00,,20.1,
+2019-06-20 17:00:00,,19.3,
+2019-06-20 18:00:00,,19.0,
+2019-06-20 19:00:00,,23.2,
+2019-06-20 20:00:00,,23.9,
+2019-06-20 21:00:00,,25.3,
+2019-06-20 22:00:00,,21.4,
+2019-06-20 23:00:00,,24.9,
+2019-06-21 00:00:00,,26.5,
+2019-06-21 01:00:00,,21.8,
+2019-06-21 02:00:00,,20.0,
diff --git a/doc/data/air_quality_no2_long.csv b/doc/data/air_quality_no2_long.csv
new file mode 100644
index 0000000000000..5d959370b7d48
--- /dev/null
+++ b/doc/data/air_quality_no2_long.csv
@@ -0,0 +1,2069 @@
+city,country,date.utc,location,parameter,value,unit
+Paris,FR,2019-06-21 00:00:00+00:00,FR04014,no2,20.0,µg/m³
+Paris,FR,2019-06-20 23:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-06-20 22:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-06-20 21:00:00+00:00,FR04014,no2,24.9,µg/m³
+Paris,FR,2019-06-20 20:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-06-20 19:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-06-20 18:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-06-20 17:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-06-20 16:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-06-20 15:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-06-20 14:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-06-20 13:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-06-19 10:00:00+00:00,FR04014,no2,26.6,µg/m³
+Paris,FR,2019-06-19 09:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-06-18 22:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-06-18 21:00:00+00:00,FR04014,no2,23.1,µg/m³
+Paris,FR,2019-06-18 20:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-06-18 19:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-06-18 08:00:00+00:00,FR04014,no2,49.6,µg/m³
+Paris,FR,2019-06-18 07:00:00+00:00,FR04014,no2,52.6,µg/m³
+Paris,FR,2019-06-18 06:00:00+00:00,FR04014,no2,51.4,µg/m³
+Paris,FR,2019-06-18 05:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-06-18 04:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-06-18 03:00:00+00:00,FR04014,no2,45.5,µg/m³
+Paris,FR,2019-06-18 02:00:00+00:00,FR04014,no2,39.8,µg/m³
+Paris,FR,2019-06-18 01:00:00+00:00,FR04014,no2,60.1,µg/m³
+Paris,FR,2019-06-18 00:00:00+00:00,FR04014,no2,66.2,µg/m³
+Paris,FR,2019-06-17 23:00:00+00:00,FR04014,no2,73.3,µg/m³
+Paris,FR,2019-06-17 22:00:00+00:00,FR04014,no2,51.0,µg/m³
+Paris,FR,2019-06-17 21:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-06-17 20:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-06-17 19:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-17 18:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-06-17 17:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-06-17 16:00:00+00:00,FR04014,no2,11.9,µg/m³
+Paris,FR,2019-06-17 15:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-17 14:00:00+00:00,FR04014,no2,11.5,µg/m³
+Paris,FR,2019-06-17 13:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-17 12:00:00+00:00,FR04014,no2,10.1,µg/m³
+Paris,FR,2019-06-17 11:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-17 10:00:00+00:00,FR04014,no2,16.0,µg/m³
+Paris,FR,2019-06-17 09:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-06-17 08:00:00+00:00,FR04014,no2,51.6,µg/m³
+Paris,FR,2019-06-17 07:00:00+00:00,FR04014,no2,54.4,µg/m³
+Paris,FR,2019-06-17 06:00:00+00:00,FR04014,no2,52.3,µg/m³
+Paris,FR,2019-06-17 05:00:00+00:00,FR04014,no2,44.8,µg/m³
+Paris,FR,2019-06-17 04:00:00+00:00,FR04014,no2,45.7,µg/m³
+Paris,FR,2019-06-17 03:00:00+00:00,FR04014,no2,49.1,µg/m³
+Paris,FR,2019-06-17 02:00:00+00:00,FR04014,no2,53.1,µg/m³
+Paris,FR,2019-06-17 01:00:00+00:00,FR04014,no2,58.8,µg/m³
+Paris,FR,2019-06-17 00:00:00+00:00,FR04014,no2,69.3,µg/m³
+Paris,FR,2019-06-16 23:00:00+00:00,FR04014,no2,67.3,µg/m³
+Paris,FR,2019-06-16 22:00:00+00:00,FR04014,no2,56.6,µg/m³
+Paris,FR,2019-06-16 21:00:00+00:00,FR04014,no2,42.7,µg/m³
+Paris,FR,2019-06-16 20:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-06-16 19:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-16 18:00:00+00:00,FR04014,no2,12.3,µg/m³
+Paris,FR,2019-06-16 17:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-16 16:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-06-16 15:00:00+00:00,FR04014,no2,8.4,µg/m³
+Paris,FR,2019-06-16 14:00:00+00:00,FR04014,no2,8.1,µg/m³
+Paris,FR,2019-06-16 13:00:00+00:00,FR04014,no2,8.7,µg/m³
+Paris,FR,2019-06-16 12:00:00+00:00,FR04014,no2,11.2,µg/m³
+Paris,FR,2019-06-16 11:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-06-16 10:00:00+00:00,FR04014,no2,8.7,µg/m³
+Paris,FR,2019-06-16 09:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-06-16 08:00:00+00:00,FR04014,no2,9.9,µg/m³
+Paris,FR,2019-06-16 07:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-16 06:00:00+00:00,FR04014,no2,11.6,µg/m³
+Paris,FR,2019-06-16 05:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-16 04:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-16 03:00:00+00:00,FR04014,no2,11.2,µg/m³
+Paris,FR,2019-06-16 02:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-06-16 01:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-06-16 00:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-06-15 23:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-06-15 22:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-06-15 21:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-06-15 20:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-15 19:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-06-15 18:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-15 17:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-06-15 16:00:00+00:00,FR04014,no2,10.7,µg/m³
+Paris,FR,2019-06-15 15:00:00+00:00,FR04014,no2,10.5,µg/m³
+Paris,FR,2019-06-15 14:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-15 13:00:00+00:00,FR04014,no2,9.0,µg/m³
+Paris,FR,2019-06-15 12:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-06-15 11:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-06-15 10:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-06-15 09:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-15 08:00:00+00:00,FR04014,no2,17.6,µg/m³
+Paris,FR,2019-06-15 07:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-06-15 06:00:00+00:00,FR04014,no2,35.8,µg/m³
+Paris,FR,2019-06-15 02:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-06-15 01:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-06-15 00:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-06-14 23:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-06-14 22:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-06-14 21:00:00+00:00,FR04014,no2,55.0,µg/m³
+Paris,FR,2019-06-14 20:00:00+00:00,FR04014,no2,41.9,µg/m³
+Paris,FR,2019-06-14 19:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-06-14 18:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-06-14 17:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-06-14 16:00:00+00:00,FR04014,no2,18.9,µg/m³
+Paris,FR,2019-06-14 15:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-14 14:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-06-14 13:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-06-14 12:00:00+00:00,FR04014,no2,17.1,µg/m³
+Paris,FR,2019-06-14 11:00:00+00:00,FR04014,no2,21.8,µg/m³
+Paris,FR,2019-06-14 10:00:00+00:00,FR04014,no2,25.1,µg/m³
+Paris,FR,2019-06-14 09:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-06-14 08:00:00+00:00,FR04014,no2,34.3,µg/m³
+Paris,FR,2019-06-14 07:00:00+00:00,FR04014,no2,51.5,µg/m³
+Paris,FR,2019-06-14 06:00:00+00:00,FR04014,no2,64.3,µg/m³
+Paris,FR,2019-06-14 05:00:00+00:00,FR04014,no2,49.3,µg/m³
+Paris,FR,2019-06-14 04:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-06-14 03:00:00+00:00,FR04014,no2,48.5,µg/m³
+Paris,FR,2019-06-14 02:00:00+00:00,FR04014,no2,66.6,µg/m³
+Paris,FR,2019-06-14 01:00:00+00:00,FR04014,no2,68.1,µg/m³
+Paris,FR,2019-06-14 00:00:00+00:00,FR04014,no2,74.2,µg/m³
+Paris,FR,2019-06-13 23:00:00+00:00,FR04014,no2,78.3,µg/m³
+Paris,FR,2019-06-13 22:00:00+00:00,FR04014,no2,77.9,µg/m³
+Paris,FR,2019-06-13 21:00:00+00:00,FR04014,no2,58.8,µg/m³
+Paris,FR,2019-06-13 20:00:00+00:00,FR04014,no2,31.5,µg/m³
+Paris,FR,2019-06-13 19:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-06-13 18:00:00+00:00,FR04014,no2,24.0,µg/m³
+Paris,FR,2019-06-13 17:00:00+00:00,FR04014,no2,38.2,µg/m³
+Paris,FR,2019-06-13 16:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-06-13 15:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-06-13 14:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-06-13 13:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-06-13 12:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-06-13 11:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-06-13 10:00:00+00:00,FR04014,no2,24.5,µg/m³
+Paris,FR,2019-06-13 09:00:00+00:00,FR04014,no2,30.2,µg/m³
+Paris,FR,2019-06-13 08:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-06-13 07:00:00+00:00,FR04014,no2,40.9,µg/m³
+Paris,FR,2019-06-13 06:00:00+00:00,FR04014,no2,39.8,µg/m³
+Paris,FR,2019-06-13 05:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-06-13 04:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-06-13 03:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-06-13 02:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-06-13 01:00:00+00:00,FR04014,no2,18.7,µg/m³
+Paris,FR,2019-06-13 00:00:00+00:00,FR04014,no2,20.0,µg/m³
+Paris,FR,2019-06-12 23:00:00+00:00,FR04014,no2,26.9,µg/m³
+Paris,FR,2019-06-12 22:00:00+00:00,FR04014,no2,25.6,µg/m³
+Paris,FR,2019-06-12 21:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-06-12 20:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-06-12 19:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-06-12 18:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-06-12 17:00:00+00:00,FR04014,no2,24.2,µg/m³
+Paris,FR,2019-06-12 16:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-06-12 15:00:00+00:00,FR04014,no2,16.8,µg/m³
+Paris,FR,2019-06-12 14:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-06-12 13:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-06-12 12:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-06-12 11:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-06-12 10:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-06-12 09:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-06-12 08:00:00+00:00,FR04014,no2,35.5,µg/m³
+Paris,FR,2019-06-12 07:00:00+00:00,FR04014,no2,44.4,µg/m³
+Paris,FR,2019-06-12 06:00:00+00:00,FR04014,no2,38.4,µg/m³
+Paris,FR,2019-06-12 05:00:00+00:00,FR04014,no2,42.7,µg/m³
+Paris,FR,2019-06-12 04:00:00+00:00,FR04014,no2,44.9,µg/m³
+Paris,FR,2019-06-12 03:00:00+00:00,FR04014,no2,36.3,µg/m³
+Paris,FR,2019-06-12 02:00:00+00:00,FR04014,no2,34.7,µg/m³
+Paris,FR,2019-06-12 01:00:00+00:00,FR04014,no2,41.9,µg/m³
+Paris,FR,2019-06-12 00:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-06-11 23:00:00+00:00,FR04014,no2,41.5,µg/m³
+Paris,FR,2019-06-11 22:00:00+00:00,FR04014,no2,59.4,µg/m³
+Paris,FR,2019-06-11 21:00:00+00:00,FR04014,no2,54.1,µg/m³
+Paris,FR,2019-06-11 20:00:00+00:00,FR04014,no2,42.7,µg/m³
+Paris,FR,2019-06-11 19:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-06-11 18:00:00+00:00,FR04014,no2,44.6,µg/m³
+Paris,FR,2019-06-11 17:00:00+00:00,FR04014,no2,35.5,µg/m³
+Paris,FR,2019-06-11 16:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-06-11 15:00:00+00:00,FR04014,no2,19.8,µg/m³
+Paris,FR,2019-06-11 14:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-06-11 13:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-11 12:00:00+00:00,FR04014,no2,12.6,µg/m³
+Paris,FR,2019-06-11 11:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-06-11 10:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-06-11 09:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-06-11 08:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-06-11 07:00:00+00:00,FR04014,no2,58.0,µg/m³
+Paris,FR,2019-06-11 06:00:00+00:00,FR04014,no2,55.4,µg/m³
+Paris,FR,2019-06-11 05:00:00+00:00,FR04014,no2,58.7,µg/m³
+Paris,FR,2019-06-11 04:00:00+00:00,FR04014,no2,52.7,µg/m³
+Paris,FR,2019-06-11 03:00:00+00:00,FR04014,no2,32.3,µg/m³
+Paris,FR,2019-06-11 02:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-06-11 01:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-06-11 00:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-06-10 23:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-06-10 22:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-06-10 21:00:00+00:00,FR04014,no2,23.5,µg/m³
+Paris,FR,2019-06-10 20:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-06-10 19:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-06-10 18:00:00+00:00,FR04014,no2,18.4,µg/m³
+Paris,FR,2019-06-10 17:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-06-10 16:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-06-10 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-10 14:00:00+00:00,FR04014,no2,9.5,µg/m³
+Paris,FR,2019-06-10 13:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-10 12:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-10 11:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-06-10 10:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-06-10 09:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-06-10 08:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-06-10 07:00:00+00:00,FR04014,no2,23.0,µg/m³
+Paris,FR,2019-06-10 06:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-06-10 05:00:00+00:00,FR04014,no2,21.3,µg/m³
+Paris,FR,2019-06-10 04:00:00+00:00,FR04014,no2,13.7,µg/m³
+Paris,FR,2019-06-10 03:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-06-10 02:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-06-10 01:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-06-10 00:00:00+00:00,FR04014,no2,28.1,µg/m³
+Paris,FR,2019-06-09 23:00:00+00:00,FR04014,no2,39.9,µg/m³
+Paris,FR,2019-06-09 22:00:00+00:00,FR04014,no2,37.1,µg/m³
+Paris,FR,2019-06-09 21:00:00+00:00,FR04014,no2,30.9,µg/m³
+Paris,FR,2019-06-09 20:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-06-09 19:00:00+00:00,FR04014,no2,30.6,µg/m³
+Paris,FR,2019-06-09 18:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-09 17:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-06-09 16:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-06-09 15:00:00+00:00,FR04014,no2,7.2,µg/m³
+Paris,FR,2019-06-09 14:00:00+00:00,FR04014,no2,7.9,µg/m³
+Paris,FR,2019-06-09 13:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-09 12:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-06-09 11:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-06-09 10:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-06-09 09:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-06-09 08:00:00+00:00,FR04014,no2,30.2,µg/m³
+Paris,FR,2019-06-09 07:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-06-09 06:00:00+00:00,FR04014,no2,36.7,µg/m³
+Paris,FR,2019-06-09 05:00:00+00:00,FR04014,no2,42.2,µg/m³
+Paris,FR,2019-06-09 04:00:00+00:00,FR04014,no2,43.0,µg/m³
+Paris,FR,2019-06-09 03:00:00+00:00,FR04014,no2,51.5,µg/m³
+Paris,FR,2019-06-09 02:00:00+00:00,FR04014,no2,51.2,µg/m³
+Paris,FR,2019-06-09 01:00:00+00:00,FR04014,no2,41.0,µg/m³
+Paris,FR,2019-06-09 00:00:00+00:00,FR04014,no2,55.9,µg/m³
+Paris,FR,2019-06-08 23:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-06-08 22:00:00+00:00,FR04014,no2,34.8,µg/m³
+Paris,FR,2019-06-08 21:00:00+00:00,FR04014,no2,36.7,µg/m³
+Paris,FR,2019-06-08 18:00:00+00:00,FR04014,no2,22.0,µg/m³
+Paris,FR,2019-06-08 17:00:00+00:00,FR04014,no2,14.8,µg/m³
+Paris,FR,2019-06-08 16:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-06-08 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-08 14:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-06-08 13:00:00+00:00,FR04014,no2,11.1,µg/m³
+Paris,FR,2019-06-08 12:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-06-08 11:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-06-08 10:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-06-08 09:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-08 08:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-06-08 07:00:00+00:00,FR04014,no2,14.0,µg/m³
+Paris,FR,2019-06-08 06:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-06-08 05:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-06-08 04:00:00+00:00,FR04014,no2,10.7,µg/m³
+Paris,FR,2019-06-08 03:00:00+00:00,FR04014,no2,9.8,µg/m³
+Paris,FR,2019-06-08 02:00:00+00:00,FR04014,no2,8.4,µg/m³
+Paris,FR,2019-06-08 01:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-06-08 00:00:00+00:00,FR04014,no2,11.3,µg/m³
+Paris,FR,2019-06-07 23:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-07 22:00:00+00:00,FR04014,no2,14.7,µg/m³
+Paris,FR,2019-06-07 21:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-06-07 20:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-06-07 19:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-06-07 18:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-06-07 17:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-07 16:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-07 15:00:00+00:00,FR04014,no2,15.6,µg/m³
+Paris,FR,2019-06-07 14:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-07 13:00:00+00:00,FR04014,no2,15.0,µg/m³
+Paris,FR,2019-06-07 12:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-06-07 11:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-06-07 10:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-06-07 09:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-06-07 08:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-06-07 07:00:00+00:00,FR04014,no2,23.0,µg/m³
+Paris,FR,2019-06-07 06:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-06-06 14:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-06-06 13:00:00+00:00,FR04014,no2,16.0,µg/m³
+Paris,FR,2019-06-06 12:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-06-06 11:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-06-06 10:00:00+00:00,FR04014,no2,21.2,µg/m³
+Paris,FR,2019-06-06 09:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-06-06 08:00:00+00:00,FR04014,no2,36.0,µg/m³
+Paris,FR,2019-06-06 07:00:00+00:00,FR04014,no2,43.1,µg/m³
+Paris,FR,2019-06-06 06:00:00+00:00,FR04014,no2,40.5,µg/m³
+Paris,FR,2019-06-06 05:00:00+00:00,FR04014,no2,40.3,µg/m³
+Paris,FR,2019-06-06 04:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-06-06 03:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-06-06 02:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-06-06 01:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-06-06 00:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-06-05 23:00:00+00:00,FR04014,no2,31.8,µg/m³
+Paris,FR,2019-06-05 22:00:00+00:00,FR04014,no2,30.3,µg/m³
+Paris,FR,2019-06-05 21:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-06-05 20:00:00+00:00,FR04014,no2,37.5,µg/m³
+Paris,FR,2019-06-05 19:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-06-05 18:00:00+00:00,FR04014,no2,40.8,µg/m³
+Paris,FR,2019-06-05 17:00:00+00:00,FR04014,no2,48.8,µg/m³
+Paris,FR,2019-06-05 16:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-06-05 15:00:00+00:00,FR04014,no2,53.5,µg/m³
+Paris,FR,2019-06-05 14:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-06-05 13:00:00+00:00,FR04014,no2,33.6,µg/m³
+Paris,FR,2019-06-05 12:00:00+00:00,FR04014,no2,47.2,µg/m³
+Paris,FR,2019-06-05 11:00:00+00:00,FR04014,no2,59.0,µg/m³
+Paris,FR,2019-06-05 10:00:00+00:00,FR04014,no2,42.1,µg/m³
+Paris,FR,2019-06-05 09:00:00+00:00,FR04014,no2,36.8,µg/m³
+Paris,FR,2019-06-05 08:00:00+00:00,FR04014,no2,35.3,µg/m³
+Paris,FR,2019-06-05 07:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-06-05 06:00:00+00:00,FR04014,no2,35.8,µg/m³
+Paris,FR,2019-06-05 05:00:00+00:00,FR04014,no2,39.2,µg/m³
+Paris,FR,2019-06-05 04:00:00+00:00,FR04014,no2,24.5,µg/m³
+Paris,FR,2019-06-05 03:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-06-05 02:00:00+00:00,FR04014,no2,12.4,µg/m³
+Paris,FR,2019-06-05 01:00:00+00:00,FR04014,no2,10.8,µg/m³
+Paris,FR,2019-06-05 00:00:00+00:00,FR04014,no2,15.7,µg/m³
+Paris,FR,2019-06-04 23:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-06-04 22:00:00+00:00,FR04014,no2,33.5,µg/m³
+Paris,FR,2019-06-04 21:00:00+00:00,FR04014,no2,26.3,µg/m³
+Paris,FR,2019-06-04 20:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-06-04 19:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-06-04 18:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-06-04 17:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-06-04 16:00:00+00:00,FR04014,no2,26.3,µg/m³
+Paris,FR,2019-06-04 15:00:00+00:00,FR04014,no2,21.5,µg/m³
+Paris,FR,2019-06-04 14:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-06-04 13:00:00+00:00,FR04014,no2,17.4,µg/m³
+Paris,FR,2019-06-04 12:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-06-04 11:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-06-04 10:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-06-04 09:00:00+00:00,FR04014,no2,38.5,µg/m³
+Paris,FR,2019-06-04 08:00:00+00:00,FR04014,no2,50.8,µg/m³
+Paris,FR,2019-06-04 07:00:00+00:00,FR04014,no2,53.5,µg/m³
+Paris,FR,2019-06-04 06:00:00+00:00,FR04014,no2,47.7,µg/m³
+Paris,FR,2019-06-04 05:00:00+00:00,FR04014,no2,36.5,µg/m³
+Paris,FR,2019-06-04 04:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-06-04 03:00:00+00:00,FR04014,no2,41.6,µg/m³
+Paris,FR,2019-06-04 02:00:00+00:00,FR04014,no2,35.0,µg/m³
+Paris,FR,2019-06-04 01:00:00+00:00,FR04014,no2,43.9,µg/m³
+Paris,FR,2019-06-04 00:00:00+00:00,FR04014,no2,52.4,µg/m³
+Paris,FR,2019-06-03 23:00:00+00:00,FR04014,no2,44.6,µg/m³
+Paris,FR,2019-06-03 22:00:00+00:00,FR04014,no2,30.5,µg/m³
+Paris,FR,2019-06-03 21:00:00+00:00,FR04014,no2,31.1,µg/m³
+Paris,FR,2019-06-03 20:00:00+00:00,FR04014,no2,33.0,µg/m³
+Paris,FR,2019-06-03 19:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-06-03 18:00:00+00:00,FR04014,no2,23.1,µg/m³
+Paris,FR,2019-06-03 17:00:00+00:00,FR04014,no2,24.4,µg/m³
+Paris,FR,2019-06-03 16:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-06-03 15:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-06-03 14:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-06-03 13:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-06-03 12:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-06-03 11:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-06-03 10:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-06-03 09:00:00+00:00,FR04014,no2,46.0,µg/m³
+Paris,FR,2019-06-03 08:00:00+00:00,FR04014,no2,43.9,µg/m³
+Paris,FR,2019-06-03 07:00:00+00:00,FR04014,no2,50.0,µg/m³
+Paris,FR,2019-06-03 06:00:00+00:00,FR04014,no2,44.1,µg/m³
+Paris,FR,2019-06-03 05:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-06-03 04:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-06-03 03:00:00+00:00,FR04014,no2,9.8,µg/m³
+Paris,FR,2019-06-03 02:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-06-03 01:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-03 00:00:00+00:00,FR04014,no2,15.7,µg/m³
+Paris,FR,2019-06-02 23:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-06-02 22:00:00+00:00,FR04014,no2,27.6,µg/m³
+Paris,FR,2019-06-02 21:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-06-02 20:00:00+00:00,FR04014,no2,40.9,µg/m³
+Paris,FR,2019-06-02 19:00:00+00:00,FR04014,no2,25.8,µg/m³
+Paris,FR,2019-06-02 18:00:00+00:00,FR04014,no2,15.6,µg/m³
+Paris,FR,2019-06-02 17:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-02 16:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-06-02 15:00:00+00:00,FR04014,no2,13.9,µg/m³
+Paris,FR,2019-06-02 14:00:00+00:00,FR04014,no2,15.0,µg/m³
+Paris,FR,2019-06-02 13:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-06-02 12:00:00+00:00,FR04014,no2,11.5,µg/m³
+Paris,FR,2019-06-02 11:00:00+00:00,FR04014,no2,13.1,µg/m³
+Paris,FR,2019-06-02 10:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-06-02 09:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-06-02 08:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-06-02 07:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-06-02 06:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-06-02 05:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-06-02 04:00:00+00:00,FR04014,no2,24.5,µg/m³
+Paris,FR,2019-06-02 03:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-06-02 02:00:00+00:00,FR04014,no2,39.2,µg/m³
+Paris,FR,2019-06-02 01:00:00+00:00,FR04014,no2,38.2,µg/m³
+Paris,FR,2019-06-02 00:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-06-01 23:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-06-01 22:00:00+00:00,FR04014,no2,48.1,µg/m³
+Paris,FR,2019-06-01 21:00:00+00:00,FR04014,no2,49.4,µg/m³
+Paris,FR,2019-06-01 20:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-06-01 19:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-06-01 18:00:00+00:00,FR04014,no2,14.5,µg/m³
+Paris,FR,2019-06-01 17:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-01 16:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-06-01 15:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-01 14:00:00+00:00,FR04014,no2,10.0,µg/m³
+Paris,FR,2019-06-01 13:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-06-01 12:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-06-01 11:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-06-01 10:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-06-01 09:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-06-01 08:00:00+00:00,FR04014,no2,33.3,µg/m³
+Paris,FR,2019-06-01 07:00:00+00:00,FR04014,no2,46.4,µg/m³
+Paris,FR,2019-06-01 06:00:00+00:00,FR04014,no2,44.6,µg/m³
+Paris,FR,2019-06-01 02:00:00+00:00,FR04014,no2,68.1,µg/m³
+Paris,FR,2019-06-01 01:00:00+00:00,FR04014,no2,74.8,µg/m³
+Paris,FR,2019-06-01 00:00:00+00:00,FR04014,no2,84.7,µg/m³
+Paris,FR,2019-05-31 23:00:00+00:00,FR04014,no2,81.7,µg/m³
+Paris,FR,2019-05-31 22:00:00+00:00,FR04014,no2,68.0,µg/m³
+Paris,FR,2019-05-31 21:00:00+00:00,FR04014,no2,60.2,µg/m³
+Paris,FR,2019-05-31 20:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-05-31 19:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-31 18:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-31 17:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-31 16:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-05-31 15:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-05-31 14:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-05-31 13:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-05-31 12:00:00+00:00,FR04014,no2,13.3,µg/m³
+Paris,FR,2019-05-31 11:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-31 10:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-31 09:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-05-31 08:00:00+00:00,FR04014,no2,36.6,µg/m³
+Paris,FR,2019-05-31 07:00:00+00:00,FR04014,no2,47.4,µg/m³
+Paris,FR,2019-05-31 06:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-05-31 05:00:00+00:00,FR04014,no2,37.2,µg/m³
+Paris,FR,2019-05-31 04:00:00+00:00,FR04014,no2,31.1,µg/m³
+Paris,FR,2019-05-31 03:00:00+00:00,FR04014,no2,40.1,µg/m³
+Paris,FR,2019-05-31 02:00:00+00:00,FR04014,no2,44.1,µg/m³
+Paris,FR,2019-05-31 01:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-05-31 00:00:00+00:00,FR04014,no2,27.2,µg/m³
+Paris,FR,2019-05-30 23:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-05-30 22:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-05-30 21:00:00+00:00,FR04014,no2,26.9,µg/m³
+Paris,FR,2019-05-30 20:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-05-30 19:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-30 18:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-30 17:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-30 16:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-30 15:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-30 14:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-30 13:00:00+00:00,FR04014,no2,16.1,µg/m³
+Paris,FR,2019-05-30 12:00:00+00:00,FR04014,no2,14.2,µg/m³
+Paris,FR,2019-05-30 11:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-30 10:00:00+00:00,FR04014,no2,13.8,µg/m³
+Paris,FR,2019-05-30 09:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-30 08:00:00+00:00,FR04014,no2,16.7,µg/m³
+Paris,FR,2019-05-30 07:00:00+00:00,FR04014,no2,18.3,µg/m³
+Paris,FR,2019-05-30 06:00:00+00:00,FR04014,no2,13.3,µg/m³
+Paris,FR,2019-05-30 05:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-05-30 04:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-05-30 03:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-30 02:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-05-30 01:00:00+00:00,FR04014,no2,12.4,µg/m³
+Paris,FR,2019-05-30 00:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-05-29 23:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-05-29 22:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-29 21:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-05-29 20:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-05-29 19:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-29 18:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-29 17:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-29 16:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-29 15:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-29 14:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-29 13:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-29 12:00:00+00:00,FR04014,no2,13.2,µg/m³
+Paris,FR,2019-05-29 11:00:00+00:00,FR04014,no2,22.0,µg/m³
+Paris,FR,2019-05-29 10:00:00+00:00,FR04014,no2,30.7,µg/m³
+Paris,FR,2019-05-29 09:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-29 08:00:00+00:00,FR04014,no2,45.7,µg/m³
+Paris,FR,2019-05-29 07:00:00+00:00,FR04014,no2,50.5,µg/m³
+Paris,FR,2019-05-29 06:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-29 05:00:00+00:00,FR04014,no2,36.7,µg/m³
+Paris,FR,2019-05-29 04:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-29 03:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-29 02:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-29 01:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-05-29 00:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-05-28 23:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-28 22:00:00+00:00,FR04014,no2,20.2,µg/m³
+Paris,FR,2019-05-28 21:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-28 20:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-28 19:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-28 18:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-05-28 17:00:00+00:00,FR04014,no2,20.8,µg/m³
+Paris,FR,2019-05-28 16:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-05-28 15:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-05-28 14:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-28 13:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-28 12:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-05-28 11:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-28 10:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-05-28 09:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-28 08:00:00+00:00,FR04014,no2,31.2,µg/m³
+Paris,FR,2019-05-28 07:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-05-28 06:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-05-28 05:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-05-28 04:00:00+00:00,FR04014,no2,8.9,µg/m³
+Paris,FR,2019-05-28 03:00:00+00:00,FR04014,no2,6.1,µg/m³
+Paris,FR,2019-05-28 02:00:00+00:00,FR04014,no2,6.4,µg/m³
+Paris,FR,2019-05-28 01:00:00+00:00,FR04014,no2,8.2,µg/m³
+Paris,FR,2019-05-28 00:00:00+00:00,FR04014,no2,15.4,µg/m³
+Paris,FR,2019-05-27 23:00:00+00:00,FR04014,no2,22.6,µg/m³
+Paris,FR,2019-05-27 22:00:00+00:00,FR04014,no2,19.9,µg/m³
+Paris,FR,2019-05-27 21:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-27 20:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-27 19:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-27 18:00:00+00:00,FR04014,no2,25.6,µg/m³
+Paris,FR,2019-05-27 17:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-27 16:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-05-27 15:00:00+00:00,FR04014,no2,25.6,µg/m³
+Paris,FR,2019-05-27 14:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-05-27 13:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-27 12:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-05-27 11:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-05-27 10:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-27 09:00:00+00:00,FR04014,no2,31.4,µg/m³
+Paris,FR,2019-05-27 08:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-05-27 07:00:00+00:00,FR04014,no2,29.5,µg/m³
+Paris,FR,2019-05-27 06:00:00+00:00,FR04014,no2,29.1,µg/m³
+Paris,FR,2019-05-27 05:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-27 04:00:00+00:00,FR04014,no2,6.5,µg/m³
+Paris,FR,2019-05-27 03:00:00+00:00,FR04014,no2,4.8,µg/m³
+Paris,FR,2019-05-27 02:00:00+00:00,FR04014,no2,5.9,µg/m³
+Paris,FR,2019-05-27 01:00:00+00:00,FR04014,no2,7.1,µg/m³
+Paris,FR,2019-05-27 00:00:00+00:00,FR04014,no2,9.5,µg/m³
+Paris,FR,2019-05-26 23:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-26 22:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-05-26 21:00:00+00:00,FR04014,no2,16.1,µg/m³
+Paris,FR,2019-05-26 20:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-05-26 19:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-26 18:00:00+00:00,FR04014,no2,22.8,µg/m³
+Paris,FR,2019-05-26 17:00:00+00:00,FR04014,no2,17.3,µg/m³
+Paris,FR,2019-05-26 16:00:00+00:00,FR04014,no2,17.1,µg/m³
+Paris,FR,2019-05-26 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-26 14:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-26 13:00:00+00:00,FR04014,no2,12.5,µg/m³
+Paris,FR,2019-05-26 12:00:00+00:00,FR04014,no2,11.5,µg/m³
+Paris,FR,2019-05-26 11:00:00+00:00,FR04014,no2,13.3,µg/m³
+Paris,FR,2019-05-26 10:00:00+00:00,FR04014,no2,11.3,µg/m³
+Paris,FR,2019-05-26 09:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-26 08:00:00+00:00,FR04014,no2,11.0,µg/m³
+Paris,FR,2019-05-26 07:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-05-26 06:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-26 05:00:00+00:00,FR04014,no2,16.8,µg/m³
+Paris,FR,2019-05-26 04:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-26 03:00:00+00:00,FR04014,no2,22.9,µg/m³
+Paris,FR,2019-05-26 02:00:00+00:00,FR04014,no2,23.4,µg/m³
+Paris,FR,2019-05-26 01:00:00+00:00,FR04014,no2,49.8,µg/m³
+Paris,FR,2019-05-26 00:00:00+00:00,FR04014,no2,67.0,µg/m³
+Paris,FR,2019-05-25 23:00:00+00:00,FR04014,no2,70.2,µg/m³
+Paris,FR,2019-05-25 22:00:00+00:00,FR04014,no2,63.9,µg/m³
+Paris,FR,2019-05-25 21:00:00+00:00,FR04014,no2,39.5,µg/m³
+Paris,FR,2019-05-25 20:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-05-25 19:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-25 18:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-05-25 17:00:00+00:00,FR04014,no2,20.6,µg/m³
+Paris,FR,2019-05-25 16:00:00+00:00,FR04014,no2,31.9,µg/m³
+Paris,FR,2019-05-25 15:00:00+00:00,FR04014,no2,30.0,µg/m³
+Paris,FR,2019-05-25 14:00:00+00:00,FR04014,no2,23.6,µg/m³
+Paris,FR,2019-05-25 13:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-05-25 12:00:00+00:00,FR04014,no2,18.6,µg/m³
+Paris,FR,2019-05-25 11:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-25 10:00:00+00:00,FR04014,no2,26.3,µg/m³
+Paris,FR,2019-05-25 09:00:00+00:00,FR04014,no2,33.6,µg/m³
+Paris,FR,2019-05-25 08:00:00+00:00,FR04014,no2,44.5,µg/m³
+Paris,FR,2019-05-25 07:00:00+00:00,FR04014,no2,42.1,µg/m³
+Paris,FR,2019-05-25 06:00:00+00:00,FR04014,no2,36.9,µg/m³
+Paris,FR,2019-05-25 02:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-25 01:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-25 00:00:00+00:00,FR04014,no2,17.4,µg/m³
+Paris,FR,2019-05-24 23:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-05-24 22:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-05-24 21:00:00+00:00,FR04014,no2,18.1,µg/m³
+Paris,FR,2019-05-24 20:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-05-24 19:00:00+00:00,FR04014,no2,21.9,µg/m³
+Paris,FR,2019-05-24 18:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-24 17:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-24 16:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-05-24 15:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-24 14:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-24 13:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-24 12:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-05-24 11:00:00+00:00,FR04014,no2,40.6,µg/m³
+Paris,FR,2019-05-24 10:00:00+00:00,FR04014,no2,28.6,µg/m³
+Paris,FR,2019-05-24 09:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-05-24 08:00:00+00:00,FR04014,no2,45.9,µg/m³
+Paris,FR,2019-05-24 07:00:00+00:00,FR04014,no2,54.8,µg/m³
+Paris,FR,2019-05-24 06:00:00+00:00,FR04014,no2,40.7,µg/m³
+Paris,FR,2019-05-24 05:00:00+00:00,FR04014,no2,35.9,µg/m³
+Paris,FR,2019-05-24 04:00:00+00:00,FR04014,no2,28.1,µg/m³
+Paris,FR,2019-05-24 03:00:00+00:00,FR04014,no2,19.4,µg/m³
+Paris,FR,2019-05-24 02:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-24 01:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-05-24 00:00:00+00:00,FR04014,no2,32.8,µg/m³
+Paris,FR,2019-05-23 23:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-05-23 22:00:00+00:00,FR04014,no2,61.9,µg/m³
+Paris,FR,2019-05-23 21:00:00+00:00,FR04014,no2,47.0,µg/m³
+Paris,FR,2019-05-23 20:00:00+00:00,FR04014,no2,33.8,µg/m³
+Paris,FR,2019-05-23 19:00:00+00:00,FR04014,no2,28.0,µg/m³
+Paris,FR,2019-05-23 18:00:00+00:00,FR04014,no2,23.5,µg/m³
+Paris,FR,2019-05-23 17:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-23 16:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-23 15:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-23 14:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-23 13:00:00+00:00,FR04014,no2,21.2,µg/m³
+Paris,FR,2019-05-23 12:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-05-23 11:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-05-23 10:00:00+00:00,FR04014,no2,28.3,µg/m³
+Paris,FR,2019-05-23 09:00:00+00:00,FR04014,no2,79.4,µg/m³
+Paris,FR,2019-05-23 08:00:00+00:00,FR04014,no2,97.0,µg/m³
+Paris,FR,2019-05-23 07:00:00+00:00,FR04014,no2,91.8,µg/m³
+Paris,FR,2019-05-23 06:00:00+00:00,FR04014,no2,79.6,µg/m³
+Paris,FR,2019-05-23 05:00:00+00:00,FR04014,no2,68.7,µg/m³
+Paris,FR,2019-05-23 04:00:00+00:00,FR04014,no2,71.9,µg/m³
+Paris,FR,2019-05-23 03:00:00+00:00,FR04014,no2,76.8,µg/m³
+Paris,FR,2019-05-23 02:00:00+00:00,FR04014,no2,66.6,µg/m³
+Paris,FR,2019-05-23 01:00:00+00:00,FR04014,no2,53.1,µg/m³
+Paris,FR,2019-05-23 00:00:00+00:00,FR04014,no2,53.3,µg/m³
+Paris,FR,2019-05-22 23:00:00+00:00,FR04014,no2,62.1,µg/m³
+Paris,FR,2019-05-22 22:00:00+00:00,FR04014,no2,29.8,µg/m³
+Paris,FR,2019-05-22 21:00:00+00:00,FR04014,no2,37.7,µg/m³
+Paris,FR,2019-05-22 20:00:00+00:00,FR04014,no2,44.9,µg/m³
+Paris,FR,2019-05-22 19:00:00+00:00,FR04014,no2,36.2,µg/m³
+Paris,FR,2019-05-22 18:00:00+00:00,FR04014,no2,34.1,µg/m³
+Paris,FR,2019-05-22 17:00:00+00:00,FR04014,no2,36.1,µg/m³
+Paris,FR,2019-05-22 16:00:00+00:00,FR04014,no2,34.9,µg/m³
+Paris,FR,2019-05-22 15:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-05-22 14:00:00+00:00,FR04014,no2,40.0,µg/m³
+Paris,FR,2019-05-22 13:00:00+00:00,FR04014,no2,38.5,µg/m³
+Paris,FR,2019-05-22 12:00:00+00:00,FR04014,no2,42.2,µg/m³
+Paris,FR,2019-05-22 11:00:00+00:00,FR04014,no2,42.6,µg/m³
+Paris,FR,2019-05-22 10:00:00+00:00,FR04014,no2,57.8,µg/m³
+Paris,FR,2019-05-22 09:00:00+00:00,FR04014,no2,63.1,µg/m³
+Paris,FR,2019-05-22 08:00:00+00:00,FR04014,no2,70.8,µg/m³
+Paris,FR,2019-05-22 07:00:00+00:00,FR04014,no2,75.4,µg/m³
+Paris,FR,2019-05-22 06:00:00+00:00,FR04014,no2,75.7,µg/m³
+Paris,FR,2019-05-22 05:00:00+00:00,FR04014,no2,45.1,µg/m³
+Paris,FR,2019-05-22 04:00:00+00:00,FR04014,no2,33.7,µg/m³
+Paris,FR,2019-05-22 03:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-22 02:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-05-22 01:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-22 00:00:00+00:00,FR04014,no2,27.1,µg/m³
+Paris,FR,2019-05-21 23:00:00+00:00,FR04014,no2,29.5,µg/m³
+Paris,FR,2019-05-21 22:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-05-21 21:00:00+00:00,FR04014,no2,43.0,µg/m³
+Paris,FR,2019-05-21 20:00:00+00:00,FR04014,no2,40.8,µg/m³
+Paris,FR,2019-05-21 19:00:00+00:00,FR04014,no2,50.0,µg/m³
+Paris,FR,2019-05-21 18:00:00+00:00,FR04014,no2,54.3,µg/m³
+Paris,FR,2019-05-21 17:00:00+00:00,FR04014,no2,75.0,µg/m³
+Paris,FR,2019-05-21 16:00:00+00:00,FR04014,no2,42.3,µg/m³
+Paris,FR,2019-05-21 15:00:00+00:00,FR04014,no2,36.6,µg/m³
+Paris,FR,2019-05-21 14:00:00+00:00,FR04014,no2,47.8,µg/m³
+Paris,FR,2019-05-21 13:00:00+00:00,FR04014,no2,49.7,µg/m³
+Paris,FR,2019-05-21 12:00:00+00:00,FR04014,no2,30.5,µg/m³
+Paris,FR,2019-05-21 11:00:00+00:00,FR04014,no2,25.5,µg/m³
+Paris,FR,2019-05-21 10:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-05-21 09:00:00+00:00,FR04014,no2,48.1,µg/m³
+Paris,FR,2019-05-21 08:00:00+00:00,FR04014,no2,54.2,µg/m³
+Paris,FR,2019-05-21 07:00:00+00:00,FR04014,no2,56.0,µg/m³
+Paris,FR,2019-05-21 06:00:00+00:00,FR04014,no2,62.6,µg/m³
+Paris,FR,2019-05-21 05:00:00+00:00,FR04014,no2,38.0,µg/m³
+Paris,FR,2019-05-21 04:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-21 03:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-05-21 02:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-05-21 01:00:00+00:00,FR04014,no2,16.3,µg/m³
+Paris,FR,2019-05-21 00:00:00+00:00,FR04014,no2,16.9,µg/m³
+Paris,FR,2019-05-20 23:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-05-20 22:00:00+00:00,FR04014,no2,20.7,µg/m³
+Paris,FR,2019-05-20 21:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-20 20:00:00+00:00,FR04014,no2,21.6,µg/m³
+Paris,FR,2019-05-20 19:00:00+00:00,FR04014,no2,21.3,µg/m³
+Paris,FR,2019-05-20 18:00:00+00:00,FR04014,no2,32.2,µg/m³
+Paris,FR,2019-05-20 17:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-05-20 16:00:00+00:00,FR04014,no2,32.4,µg/m³
+Paris,FR,2019-05-20 15:00:00+00:00,FR04014,no2,26.5,µg/m³
+Paris,FR,2019-05-20 14:00:00+00:00,FR04014,no2,27.5,µg/m³
+Paris,FR,2019-05-20 13:00:00+00:00,FR04014,no2,23.7,µg/m³
+Paris,FR,2019-05-20 12:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-05-20 11:00:00+00:00,FR04014,no2,35.4,µg/m³
+Paris,FR,2019-05-20 10:00:00+00:00,FR04014,no2,43.9,µg/m³
+Paris,FR,2019-05-20 09:00:00+00:00,FR04014,no2,45.5,µg/m³
+Paris,FR,2019-05-20 08:00:00+00:00,FR04014,no2,46.1,µg/m³
+Paris,FR,2019-05-20 07:00:00+00:00,FR04014,no2,46.9,µg/m³
+Paris,FR,2019-05-20 06:00:00+00:00,FR04014,no2,40.1,µg/m³
+Paris,FR,2019-05-20 05:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-20 04:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-20 03:00:00+00:00,FR04014,no2,12.6,µg/m³
+Paris,FR,2019-05-20 02:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-05-20 01:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-20 00:00:00+00:00,FR04014,no2,16.4,µg/m³
+Paris,FR,2019-05-19 23:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-19 22:00:00+00:00,FR04014,no2,22.2,µg/m³
+Paris,FR,2019-05-19 21:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-05-19 20:00:00+00:00,FR04014,no2,35.6,µg/m³
+Paris,FR,2019-05-19 19:00:00+00:00,FR04014,no2,51.2,µg/m³
+Paris,FR,2019-05-19 18:00:00+00:00,FR04014,no2,32.7,µg/m³
+Paris,FR,2019-05-19 17:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-05-19 16:00:00+00:00,FR04014,no2,32.5,µg/m³
+Paris,FR,2019-05-19 15:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-05-19 14:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-05-19 13:00:00+00:00,FR04014,no2,21.0,µg/m³
+Paris,FR,2019-05-19 12:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-19 11:00:00+00:00,FR04014,no2,32.6,µg/m³
+Paris,FR,2019-05-19 10:00:00+00:00,FR04014,no2,31.0,µg/m³
+Paris,FR,2019-05-19 09:00:00+00:00,FR04014,no2,33.0,µg/m³
+Paris,FR,2019-05-19 08:00:00+00:00,FR04014,no2,31.7,µg/m³
+Paris,FR,2019-05-19 07:00:00+00:00,FR04014,no2,32.4,µg/m³
+Paris,FR,2019-05-19 06:00:00+00:00,FR04014,no2,31.1,µg/m³
+Paris,FR,2019-05-19 05:00:00+00:00,FR04014,no2,40.9,µg/m³
+Paris,FR,2019-05-19 04:00:00+00:00,FR04014,no2,39.4,µg/m³
+Paris,FR,2019-05-19 03:00:00+00:00,FR04014,no2,36.4,µg/m³
+Paris,FR,2019-05-19 02:00:00+00:00,FR04014,no2,38.1,µg/m³
+Paris,FR,2019-05-19 01:00:00+00:00,FR04014,no2,34.9,µg/m³
+Paris,FR,2019-05-19 00:00:00+00:00,FR04014,no2,49.6,µg/m³
+Paris,FR,2019-05-18 23:00:00+00:00,FR04014,no2,50.2,µg/m³
+Paris,FR,2019-05-18 22:00:00+00:00,FR04014,no2,62.5,µg/m³
+Paris,FR,2019-05-18 21:00:00+00:00,FR04014,no2,59.3,µg/m³
+Paris,FR,2019-05-18 20:00:00+00:00,FR04014,no2,36.2,µg/m³
+Paris,FR,2019-05-18 19:00:00+00:00,FR04014,no2,67.5,µg/m³
+Paris,FR,2019-05-18 18:00:00+00:00,FR04014,no2,14.5,µg/m³
+Paris,FR,2019-05-18 17:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-18 16:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-05-18 15:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-18 14:00:00+00:00,FR04014,no2,11.8,µg/m³
+Paris,FR,2019-05-18 13:00:00+00:00,FR04014,no2,10.5,µg/m³
+Paris,FR,2019-05-18 12:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-18 11:00:00+00:00,FR04014,no2,17.5,µg/m³
+Paris,FR,2019-05-18 10:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-18 09:00:00+00:00,FR04014,no2,21.1,µg/m³
+Paris,FR,2019-05-18 08:00:00+00:00,FR04014,no2,20.4,µg/m³
+Paris,FR,2019-05-18 07:00:00+00:00,FR04014,no2,27.4,µg/m³
+Paris,FR,2019-05-18 06:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-18 05:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-18 04:00:00+00:00,FR04014,no2,16.6,µg/m³
+Paris,FR,2019-05-18 03:00:00+00:00,FR04014,no2,16.1,µg/m³
+Paris,FR,2019-05-18 02:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-05-18 01:00:00+00:00,FR04014,no2,37.4,µg/m³
+Paris,FR,2019-05-18 00:00:00+00:00,FR04014,no2,31.5,µg/m³
+Paris,FR,2019-05-17 23:00:00+00:00,FR04014,no2,34.1,µg/m³
+Paris,FR,2019-05-17 22:00:00+00:00,FR04014,no2,28.2,µg/m³
+Paris,FR,2019-05-17 21:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-17 20:00:00+00:00,FR04014,no2,23.5,µg/m³
+Paris,FR,2019-05-17 19:00:00+00:00,FR04014,no2,24.7,µg/m³
+Paris,FR,2019-05-17 18:00:00+00:00,FR04014,no2,33.6,µg/m³
+Paris,FR,2019-05-17 17:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-17 16:00:00+00:00,FR04014,no2,20.7,µg/m³
+Paris,FR,2019-05-17 15:00:00+00:00,FR04014,no2,22.2,µg/m³
+Paris,FR,2019-05-17 14:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-05-17 13:00:00+00:00,FR04014,no2,37.9,µg/m³
+Paris,FR,2019-05-17 12:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-17 11:00:00+00:00,FR04014,no2,43.1,µg/m³
+Paris,FR,2019-05-17 10:00:00+00:00,FR04014,no2,51.5,µg/m³
+Paris,FR,2019-05-17 09:00:00+00:00,FR04014,no2,60.5,µg/m³
+Paris,FR,2019-05-17 08:00:00+00:00,FR04014,no2,57.5,µg/m³
+Paris,FR,2019-05-17 07:00:00+00:00,FR04014,no2,55.0,µg/m³
+Paris,FR,2019-05-17 06:00:00+00:00,FR04014,no2,46.3,µg/m³
+Paris,FR,2019-05-17 05:00:00+00:00,FR04014,no2,34.0,µg/m³
+Paris,FR,2019-05-17 04:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-17 03:00:00+00:00,FR04014,no2,26.6,µg/m³
+Paris,FR,2019-05-17 02:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-05-17 01:00:00+00:00,FR04014,no2,26.1,µg/m³
+Paris,FR,2019-05-17 00:00:00+00:00,FR04014,no2,46.3,µg/m³
+Paris,FR,2019-05-16 23:00:00+00:00,FR04014,no2,43.7,µg/m³
+Paris,FR,2019-05-16 22:00:00+00:00,FR04014,no2,37.1,µg/m³
+Paris,FR,2019-05-16 21:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-16 20:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-05-16 19:00:00+00:00,FR04014,no2,14.4,µg/m³
+Paris,FR,2019-05-16 18:00:00+00:00,FR04014,no2,15.9,µg/m³
+Paris,FR,2019-05-16 17:00:00+00:00,FR04014,no2,13.5,µg/m³
+Paris,FR,2019-05-16 16:00:00+00:00,FR04014,no2,10.3,µg/m³
+Paris,FR,2019-05-16 15:00:00+00:00,FR04014,no2,10.1,µg/m³
+Paris,FR,2019-05-16 14:00:00+00:00,FR04014,no2,8.1,µg/m³
+Paris,FR,2019-05-16 13:00:00+00:00,FR04014,no2,8.5,µg/m³
+Paris,FR,2019-05-16 12:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-05-16 11:00:00+00:00,FR04014,no2,10.5,µg/m³
+Paris,FR,2019-05-16 10:00:00+00:00,FR04014,no2,13.5,µg/m³
+Paris,FR,2019-05-16 09:00:00+00:00,FR04014,no2,29.5,µg/m³
+Paris,FR,2019-05-16 08:00:00+00:00,FR04014,no2,39.4,µg/m³
+Paris,FR,2019-05-16 07:00:00+00:00,FR04014,no2,40.0,µg/m³
+Paris,FR,2019-05-16 05:00:00+00:00,FR04014,no2,52.6,µg/m³
+Paris,FR,2019-05-16 04:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-05-16 03:00:00+00:00,FR04014,no2,27.9,µg/m³
+Paris,FR,2019-05-16 02:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-05-16 01:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-05-16 00:00:00+00:00,FR04014,no2,27.4,µg/m³
+Paris,FR,2019-05-15 23:00:00+00:00,FR04014,no2,30.9,µg/m³
+Paris,FR,2019-05-15 22:00:00+00:00,FR04014,no2,44.1,µg/m³
+Paris,FR,2019-05-15 21:00:00+00:00,FR04014,no2,36.0,µg/m³
+Paris,FR,2019-05-15 20:00:00+00:00,FR04014,no2,30.1,µg/m³
+Paris,FR,2019-05-15 19:00:00+00:00,FR04014,no2,20.3,µg/m³
+Paris,FR,2019-05-15 18:00:00+00:00,FR04014,no2,16.5,µg/m³
+Paris,FR,2019-05-15 17:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-15 16:00:00+00:00,FR04014,no2,12.2,µg/m³
+Paris,FR,2019-05-15 15:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-15 14:00:00+00:00,FR04014,no2,11.9,µg/m³
+Paris,FR,2019-05-15 13:00:00+00:00,FR04014,no2,10.0,µg/m³
+Paris,FR,2019-05-15 12:00:00+00:00,FR04014,no2,9.4,µg/m³
+Paris,FR,2019-05-15 11:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-15 10:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-15 09:00:00+00:00,FR04014,no2,0.0,µg/m³
+Paris,FR,2019-05-15 08:00:00+00:00,FR04014,no2,25.7,µg/m³
+Paris,FR,2019-05-15 07:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-05-15 06:00:00+00:00,FR04014,no2,48.1,µg/m³
+Paris,FR,2019-05-15 05:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-15 04:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-05-15 03:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-05-15 02:00:00+00:00,FR04014,no2,16.8,µg/m³
+Paris,FR,2019-05-15 01:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-15 00:00:00+00:00,FR04014,no2,18.8,µg/m³
+Paris,FR,2019-05-14 23:00:00+00:00,FR04014,no2,24.3,µg/m³
+Paris,FR,2019-05-14 22:00:00+00:00,FR04014,no2,30.9,µg/m³
+Paris,FR,2019-05-14 21:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-05-14 20:00:00+00:00,FR04014,no2,28.4,µg/m³
+Paris,FR,2019-05-14 19:00:00+00:00,FR04014,no2,23.3,µg/m³
+Paris,FR,2019-05-14 18:00:00+00:00,FR04014,no2,17.9,µg/m³
+Paris,FR,2019-05-14 17:00:00+00:00,FR04014,no2,17.7,µg/m³
+Paris,FR,2019-05-14 16:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-14 15:00:00+00:00,FR04014,no2,13.4,µg/m³
+Paris,FR,2019-05-14 14:00:00+00:00,FR04014,no2,15.2,µg/m³
+Paris,FR,2019-05-14 13:00:00+00:00,FR04014,no2,11.0,µg/m³
+Paris,FR,2019-05-14 12:00:00+00:00,FR04014,no2,10.2,µg/m³
+Paris,FR,2019-05-14 11:00:00+00:00,FR04014,no2,11.3,µg/m³
+Paris,FR,2019-05-14 10:00:00+00:00,FR04014,no2,12.9,µg/m³
+Paris,FR,2019-05-14 09:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-14 08:00:00+00:00,FR04014,no2,28.8,µg/m³
+Paris,FR,2019-05-14 07:00:00+00:00,FR04014,no2,41.3,µg/m³
+Paris,FR,2019-05-14 06:00:00+00:00,FR04014,no2,46.1,µg/m³
+Paris,FR,2019-05-14 05:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-05-14 04:00:00+00:00,FR04014,no2,31.6,µg/m³
+Paris,FR,2019-05-14 03:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-14 02:00:00+00:00,FR04014,no2,19.0,µg/m³
+Paris,FR,2019-05-14 01:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-05-14 00:00:00+00:00,FR04014,no2,20.9,µg/m³
+Paris,FR,2019-05-13 23:00:00+00:00,FR04014,no2,22.8,µg/m³
+Paris,FR,2019-05-13 22:00:00+00:00,FR04014,no2,27.3,µg/m³
+Paris,FR,2019-05-13 21:00:00+00:00,FR04014,no2,30.4,µg/m³
+Paris,FR,2019-05-13 20:00:00+00:00,FR04014,no2,28.3,µg/m³
+Paris,FR,2019-05-13 19:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-05-13 18:00:00+00:00,FR04014,no2,15.5,µg/m³
+Paris,FR,2019-05-13 17:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-13 16:00:00+00:00,FR04014,no2,12.1,µg/m³
+Paris,FR,2019-05-13 15:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-13 14:00:00+00:00,FR04014,no2,10.7,µg/m³
+Paris,FR,2019-05-13 13:00:00+00:00,FR04014,no2,10.1,µg/m³
+Paris,FR,2019-05-13 12:00:00+00:00,FR04014,no2,9.2,µg/m³
+Paris,FR,2019-05-13 11:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-05-13 10:00:00+00:00,FR04014,no2,12.8,µg/m³
+Paris,FR,2019-05-13 09:00:00+00:00,FR04014,no2,20.6,µg/m³
+Paris,FR,2019-05-13 08:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-05-13 07:00:00+00:00,FR04014,no2,41.0,µg/m³
+Paris,FR,2019-05-13 06:00:00+00:00,FR04014,no2,45.2,µg/m³
+Paris,FR,2019-05-13 05:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-05-13 04:00:00+00:00,FR04014,no2,25.1,µg/m³
+Paris,FR,2019-05-13 03:00:00+00:00,FR04014,no2,18.9,µg/m³
+Paris,FR,2019-05-13 02:00:00+00:00,FR04014,no2,18.5,µg/m³
+Paris,FR,2019-05-13 01:00:00+00:00,FR04014,no2,18.9,µg/m³
+Paris,FR,2019-05-13 00:00:00+00:00,FR04014,no2,25.0,µg/m³
+Paris,FR,2019-05-12 23:00:00+00:00,FR04014,no2,32.5,µg/m³
+Paris,FR,2019-05-12 22:00:00+00:00,FR04014,no2,46.5,µg/m³
+Paris,FR,2019-05-12 21:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-05-12 20:00:00+00:00,FR04014,no2,24.1,µg/m³
+Paris,FR,2019-05-12 19:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-12 18:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-05-12 17:00:00+00:00,FR04014,no2,13.9,µg/m³
+Paris,FR,2019-05-12 16:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-12 15:00:00+00:00,FR04014,no2,9.6,µg/m³
+Paris,FR,2019-05-12 14:00:00+00:00,FR04014,no2,9.1,µg/m³
+Paris,FR,2019-05-12 13:00:00+00:00,FR04014,no2,8.7,µg/m³
+Paris,FR,2019-05-12 12:00:00+00:00,FR04014,no2,10.9,µg/m³
+Paris,FR,2019-05-12 11:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-05-12 10:00:00+00:00,FR04014,no2,11.4,µg/m³
+Paris,FR,2019-05-12 09:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-12 08:00:00+00:00,FR04014,no2,14.6,µg/m³
+Paris,FR,2019-05-12 07:00:00+00:00,FR04014,no2,15.9,µg/m³
+Paris,FR,2019-05-12 06:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-12 05:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-05-12 04:00:00+00:00,FR04014,no2,16.2,µg/m³
+Paris,FR,2019-05-12 03:00:00+00:00,FR04014,no2,16.0,µg/m³
+Paris,FR,2019-05-12 02:00:00+00:00,FR04014,no2,17.2,µg/m³
+Paris,FR,2019-05-12 01:00:00+00:00,FR04014,no2,19.2,µg/m³
+Paris,FR,2019-05-12 00:00:00+00:00,FR04014,no2,22.8,µg/m³
+Paris,FR,2019-05-11 23:00:00+00:00,FR04014,no2,26.4,µg/m³
+Paris,FR,2019-05-11 22:00:00+00:00,FR04014,no2,27.7,µg/m³
+Paris,FR,2019-05-11 21:00:00+00:00,FR04014,no2,21.1,µg/m³
+Paris,FR,2019-05-11 20:00:00+00:00,FR04014,no2,24.2,µg/m³
+Paris,FR,2019-05-11 19:00:00+00:00,FR04014,no2,31.2,µg/m³
+Paris,FR,2019-05-11 18:00:00+00:00,FR04014,no2,33.1,µg/m³
+Paris,FR,2019-05-11 17:00:00+00:00,FR04014,no2,32.0,µg/m³
+Paris,FR,2019-05-11 16:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-11 15:00:00+00:00,FR04014,no2,18.0,µg/m³
+Paris,FR,2019-05-11 14:00:00+00:00,FR04014,no2,17.8,µg/m³
+Paris,FR,2019-05-11 13:00:00+00:00,FR04014,no2,30.8,µg/m³
+Paris,FR,2019-05-11 12:00:00+00:00,FR04014,no2,30.2,µg/m³
+Paris,FR,2019-05-11 11:00:00+00:00,FR04014,no2,33.2,µg/m³
+Paris,FR,2019-05-11 10:00:00+00:00,FR04014,no2,36.8,µg/m³
+Paris,FR,2019-05-11 09:00:00+00:00,FR04014,no2,35.7,µg/m³
+Paris,FR,2019-05-11 08:00:00+00:00,FR04014,no2,32.1,µg/m³
+Paris,FR,2019-05-11 07:00:00+00:00,FR04014,no2,29.0,µg/m³
+Paris,FR,2019-05-11 06:00:00+00:00,FR04014,no2,28.9,µg/m³
+Paris,FR,2019-05-11 02:00:00+00:00,FR04014,no2,14.9,µg/m³
+Paris,FR,2019-05-11 01:00:00+00:00,FR04014,no2,15.5,µg/m³
+Paris,FR,2019-05-11 00:00:00+00:00,FR04014,no2,24.8,µg/m³
+Paris,FR,2019-05-10 23:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-05-10 22:00:00+00:00,FR04014,no2,28.1,µg/m³
+Paris,FR,2019-05-10 21:00:00+00:00,FR04014,no2,37.0,µg/m³
+Paris,FR,2019-05-10 20:00:00+00:00,FR04014,no2,43.6,µg/m³
+Paris,FR,2019-05-10 19:00:00+00:00,FR04014,no2,39.3,µg/m³
+Paris,FR,2019-05-10 18:00:00+00:00,FR04014,no2,33.4,µg/m³
+Paris,FR,2019-05-10 17:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-05-10 16:00:00+00:00,FR04014,no2,30.8,µg/m³
+Paris,FR,2019-05-10 15:00:00+00:00,FR04014,no2,29.6,µg/m³
+Paris,FR,2019-05-10 14:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-05-10 13:00:00+00:00,FR04014,no2,22.0,µg/m³
+Paris,FR,2019-05-10 12:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-10 11:00:00+00:00,FR04014,no2,23.2,µg/m³
+Paris,FR,2019-05-10 10:00:00+00:00,FR04014,no2,35.1,µg/m³
+Paris,FR,2019-05-10 09:00:00+00:00,FR04014,no2,53.4,µg/m³
+Paris,FR,2019-05-10 08:00:00+00:00,FR04014,no2,60.7,µg/m³
+Paris,FR,2019-05-10 07:00:00+00:00,FR04014,no2,57.3,µg/m³
+Paris,FR,2019-05-10 06:00:00+00:00,FR04014,no2,47.4,µg/m³
+Paris,FR,2019-05-10 05:00:00+00:00,FR04014,no2,37.8,µg/m³
+Paris,FR,2019-05-10 04:00:00+00:00,FR04014,no2,20.5,µg/m³
+Paris,FR,2019-05-10 03:00:00+00:00,FR04014,no2,15.0,µg/m³
+Paris,FR,2019-05-10 02:00:00+00:00,FR04014,no2,14.1,µg/m³
+Paris,FR,2019-05-10 01:00:00+00:00,FR04014,no2,19.1,µg/m³
+Paris,FR,2019-05-10 00:00:00+00:00,FR04014,no2,22.7,µg/m³
+Paris,FR,2019-05-09 23:00:00+00:00,FR04014,no2,26.7,µg/m³
+Paris,FR,2019-05-09 22:00:00+00:00,FR04014,no2,29.7,µg/m³
+Paris,FR,2019-05-09 21:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-09 20:00:00+00:00,FR04014,no2,29.2,µg/m³
+Paris,FR,2019-05-09 19:00:00+00:00,FR04014,no2,23.8,µg/m³
+Paris,FR,2019-05-09 18:00:00+00:00,FR04014,no2,24.4,µg/m³
+Paris,FR,2019-05-09 17:00:00+00:00,FR04014,no2,29.9,µg/m³
+Paris,FR,2019-05-09 16:00:00+00:00,FR04014,no2,27.0,µg/m³
+Paris,FR,2019-05-09 15:00:00+00:00,FR04014,no2,23.9,µg/m³
+Paris,FR,2019-05-09 14:00:00+00:00,FR04014,no2,24.6,µg/m³
+Paris,FR,2019-05-09 13:00:00+00:00,FR04014,no2,21.3,µg/m³
+Paris,FR,2019-05-09 12:00:00+00:00,FR04014,no2,35.1,µg/m³
+Paris,FR,2019-05-09 11:00:00+00:00,FR04014,no2,34.2,µg/m³
+Paris,FR,2019-05-09 10:00:00+00:00,FR04014,no2,43.1,µg/m³
+Paris,FR,2019-05-09 09:00:00+00:00,FR04014,no2,32.3,µg/m³
+Paris,FR,2019-05-09 08:00:00+00:00,FR04014,no2,32.2,µg/m³
+Paris,FR,2019-05-09 07:00:00+00:00,FR04014,no2,49.0,µg/m³
+Paris,FR,2019-05-09 06:00:00+00:00,FR04014,no2,50.7,µg/m³
+Paris,FR,2019-05-09 05:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-09 04:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-09 03:00:00+00:00,FR04014,no2,10.4,µg/m³
+Paris,FR,2019-05-09 02:00:00+00:00,FR04014,no2,10.0,µg/m³
+Paris,FR,2019-05-09 01:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-09 00:00:00+00:00,FR04014,no2,14.7,µg/m³
+Paris,FR,2019-05-08 23:00:00+00:00,FR04014,no2,25.2,µg/m³
+Paris,FR,2019-05-08 22:00:00+00:00,FR04014,no2,32.2,µg/m³
+Paris,FR,2019-05-08 21:00:00+00:00,FR04014,no2,48.9,µg/m³
+Paris,FR,2019-05-08 20:00:00+00:00,FR04014,no2,38.3,µg/m³
+Paris,FR,2019-05-08 19:00:00+00:00,FR04014,no2,41.3,µg/m³
+Paris,FR,2019-05-08 18:00:00+00:00,FR04014,no2,27.8,µg/m³
+Paris,FR,2019-05-08 17:00:00+00:00,FR04014,no2,29.3,µg/m³
+Paris,FR,2019-05-08 16:00:00+00:00,FR04014,no2,38.6,µg/m³
+Paris,FR,2019-05-08 15:00:00+00:00,FR04014,no2,26.0,µg/m³
+Paris,FR,2019-05-08 14:00:00+00:00,FR04014,no2,25.3,µg/m³
+Paris,FR,2019-05-08 13:00:00+00:00,FR04014,no2,14.3,µg/m³
+Paris,FR,2019-05-08 12:00:00+00:00,FR04014,no2,15.1,µg/m³
+Paris,FR,2019-05-08 11:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-05-08 10:00:00+00:00,FR04014,no2,33.4,µg/m³
+Paris,FR,2019-05-08 09:00:00+00:00,FR04014,no2,19.7,µg/m³
+Paris,FR,2019-05-08 08:00:00+00:00,FR04014,no2,17.0,µg/m³
+Paris,FR,2019-05-08 07:00:00+00:00,FR04014,no2,19.5,µg/m³
+Paris,FR,2019-05-08 06:00:00+00:00,FR04014,no2,21.7,µg/m³
+Paris,FR,2019-05-08 05:00:00+00:00,FR04014,no2,19.3,µg/m³
+Paris,FR,2019-05-08 04:00:00+00:00,FR04014,no2,15.5,µg/m³
+Paris,FR,2019-05-08 03:00:00+00:00,FR04014,no2,13.5,µg/m³
+Paris,FR,2019-05-08 02:00:00+00:00,FR04014,no2,15.3,µg/m³
+Paris,FR,2019-05-08 01:00:00+00:00,FR04014,no2,19.6,µg/m³
+Paris,FR,2019-05-08 00:00:00+00:00,FR04014,no2,22.1,µg/m³
+Paris,FR,2019-05-07 23:00:00+00:00,FR04014,no2,34.0,µg/m³
+Paris,FR,2019-05-07 22:00:00+00:00,FR04014,no2,35.8,µg/m³
+Paris,FR,2019-05-07 21:00:00+00:00,FR04014,no2,33.9,µg/m³
+Paris,FR,2019-05-07 20:00:00+00:00,FR04014,no2,36.2,µg/m³
+Paris,FR,2019-05-07 19:00:00+00:00,FR04014,no2,26.8,µg/m³
+Paris,FR,2019-05-07 18:00:00+00:00,FR04014,no2,21.4,µg/m³
+Paris,FR,2019-05-07 17:00:00+00:00,FR04014,no2,22.3,µg/m³
+Paris,FR,2019-05-07 16:00:00+00:00,FR04014,no2,18.2,µg/m³
+Paris,FR,2019-05-07 15:00:00+00:00,FR04014,no2,11.7,µg/m³
+Paris,FR,2019-05-07 14:00:00+00:00,FR04014,no2,11.0,µg/m³
+Paris,FR,2019-05-07 13:00:00+00:00,FR04014,no2,13.2,µg/m³
+Paris,FR,2019-05-07 12:00:00+00:00,FR04014,no2,10.6,µg/m³
+Paris,FR,2019-05-07 11:00:00+00:00,FR04014,no2,13.0,µg/m³
+Paris,FR,2019-05-07 10:00:00+00:00,FR04014,no2,20.1,µg/m³
+Paris,FR,2019-05-07 09:00:00+00:00,FR04014,no2,34.5,µg/m³
+Paris,FR,2019-05-07 08:00:00+00:00,FR04014,no2,56.0,µg/m³
+Paris,FR,2019-05-07 07:00:00+00:00,FR04014,no2,67.9,µg/m³
+Paris,FR,2019-05-07 06:00:00+00:00,FR04014,no2,77.7,µg/m³
+Paris,FR,2019-05-07 05:00:00+00:00,FR04014,no2,72.4,µg/m³
+Paris,FR,2019-05-07 04:00:00+00:00,FR04014,no2,61.9,µg/m³
+Paris,FR,2019-05-07 03:00:00+00:00,FR04014,no2,50.4,µg/m³
+Paris,FR,2019-05-07 02:00:00+00:00,FR04014,no2,27.7,µg/m³
+Paris,FR,2019-05-07 01:00:00+00:00,FR04014,no2,25.0,µg/m³
+Antwerpen,BE,2019-06-17 08:00:00+00:00,BETR801,no2,41.0,µg/m³
+Antwerpen,BE,2019-06-17 07:00:00+00:00,BETR801,no2,45.0,µg/m³
+Antwerpen,BE,2019-06-17 06:00:00+00:00,BETR801,no2,43.5,µg/m³
+Antwerpen,BE,2019-06-17 05:00:00+00:00,BETR801,no2,42.5,µg/m³
+Antwerpen,BE,2019-06-17 04:00:00+00:00,BETR801,no2,39.5,µg/m³
+Antwerpen,BE,2019-06-17 03:00:00+00:00,BETR801,no2,36.0,µg/m³
+Antwerpen,BE,2019-06-17 02:00:00+00:00,BETR801,no2,35.5,µg/m³
+Antwerpen,BE,2019-06-17 01:00:00+00:00,BETR801,no2,42.0,µg/m³
+Antwerpen,BE,2019-06-16 01:00:00+00:00,BETR801,no2,42.5,µg/m³
+Antwerpen,BE,2019-06-15 01:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-06-14 09:00:00+00:00,BETR801,no2,36.5,µg/m³
+Antwerpen,BE,2019-06-13 01:00:00+00:00,BETR801,no2,28.5,µg/m³
+Antwerpen,BE,2019-06-12 01:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-06-11 01:00:00+00:00,BETR801,no2,7.5,µg/m³
+Antwerpen,BE,2019-06-10 01:00:00+00:00,BETR801,no2,18.5,µg/m³
+Antwerpen,BE,2019-06-09 01:00:00+00:00,BETR801,no2,10.0,µg/m³
+Antwerpen,BE,2019-06-05 01:00:00+00:00,BETR801,no2,15.0,µg/m³
+Antwerpen,BE,2019-06-01 01:00:00+00:00,BETR801,no2,52.5,µg/m³
+Antwerpen,BE,2019-05-31 01:00:00+00:00,BETR801,no2,9.0,µg/m³
+Antwerpen,BE,2019-05-30 01:00:00+00:00,BETR801,no2,7.5,µg/m³
+Antwerpen,BE,2019-05-29 01:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-05-28 01:00:00+00:00,BETR801,no2,11.0,µg/m³
+Antwerpen,BE,2019-05-27 01:00:00+00:00,BETR801,no2,10.5,µg/m³
+Antwerpen,BE,2019-05-26 01:00:00+00:00,BETR801,no2,53.0,µg/m³
+Antwerpen,BE,2019-05-25 01:00:00+00:00,BETR801,no2,29.0,µg/m³
+Antwerpen,BE,2019-05-24 01:00:00+00:00,BETR801,no2,74.5,µg/m³
+Antwerpen,BE,2019-05-23 01:00:00+00:00,BETR801,no2,60.5,µg/m³
+Antwerpen,BE,2019-05-22 01:00:00+00:00,BETR801,no2,20.5,µg/m³
+Antwerpen,BE,2019-05-21 01:00:00+00:00,BETR801,no2,15.5,µg/m³
+Antwerpen,BE,2019-05-20 15:00:00+00:00,BETR801,no2,25.5,µg/m³
+Antwerpen,BE,2019-05-20 14:00:00+00:00,BETR801,no2,24.5,µg/m³
+Antwerpen,BE,2019-05-20 13:00:00+00:00,BETR801,no2,32.0,µg/m³
+Antwerpen,BE,2019-05-20 12:00:00+00:00,BETR801,no2,34.5,µg/m³
+Antwerpen,BE,2019-05-20 11:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-05-20 10:00:00+00:00,BETR801,no2,25.0,µg/m³
+Antwerpen,BE,2019-05-20 09:00:00+00:00,BETR801,no2,30.5,µg/m³
+Antwerpen,BE,2019-05-20 08:00:00+00:00,BETR801,no2,40.0,µg/m³
+Antwerpen,BE,2019-05-20 07:00:00+00:00,BETR801,no2,38.0,µg/m³
+Antwerpen,BE,2019-05-20 06:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-20 05:00:00+00:00,BETR801,no2,20.0,µg/m³
+Antwerpen,BE,2019-05-20 04:00:00+00:00,BETR801,no2,14.0,µg/m³
+Antwerpen,BE,2019-05-20 03:00:00+00:00,BETR801,no2,9.0,µg/m³
+Antwerpen,BE,2019-05-20 02:00:00+00:00,BETR801,no2,10.5,µg/m³
+Antwerpen,BE,2019-05-20 01:00:00+00:00,BETR801,no2,17.0,µg/m³
+Antwerpen,BE,2019-05-20 00:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-19 23:00:00+00:00,BETR801,no2,16.5,µg/m³
+Antwerpen,BE,2019-05-19 22:00:00+00:00,BETR801,no2,18.5,µg/m³
+Antwerpen,BE,2019-05-19 21:00:00+00:00,BETR801,no2,12.5,µg/m³
+Antwerpen,BE,2019-05-19 20:00:00+00:00,BETR801,no2,15.0,µg/m³
+Antwerpen,BE,2019-05-19 19:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-19 18:00:00+00:00,BETR801,no2,15.5,µg/m³
+Antwerpen,BE,2019-05-19 17:00:00+00:00,BETR801,no2,18.5,µg/m³
+Antwerpen,BE,2019-05-19 16:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-05-19 15:00:00+00:00,BETR801,no2,33.0,µg/m³
+Antwerpen,BE,2019-05-19 14:00:00+00:00,BETR801,no2,23.0,µg/m³
+Antwerpen,BE,2019-05-19 13:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-19 12:00:00+00:00,BETR801,no2,16.0,µg/m³
+Antwerpen,BE,2019-05-19 11:00:00+00:00,BETR801,no2,17.0,µg/m³
+Antwerpen,BE,2019-05-19 10:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-05-19 09:00:00+00:00,BETR801,no2,16.0,µg/m³
+Antwerpen,BE,2019-05-19 08:00:00+00:00,BETR801,no2,23.5,µg/m³
+Antwerpen,BE,2019-05-19 07:00:00+00:00,BETR801,no2,30.0,µg/m³
+Antwerpen,BE,2019-05-19 06:00:00+00:00,BETR801,no2,30.5,µg/m³
+Antwerpen,BE,2019-05-19 05:00:00+00:00,BETR801,no2,26.0,µg/m³
+Antwerpen,BE,2019-05-19 04:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-05-19 03:00:00+00:00,BETR801,no2,19.0,µg/m³
+Antwerpen,BE,2019-05-19 02:00:00+00:00,BETR801,no2,19.0,µg/m³
+Antwerpen,BE,2019-05-19 01:00:00+00:00,BETR801,no2,22.5,µg/m³
+Antwerpen,BE,2019-05-19 00:00:00+00:00,BETR801,no2,23.5,µg/m³
+Antwerpen,BE,2019-05-18 23:00:00+00:00,BETR801,no2,29.5,µg/m³
+Antwerpen,BE,2019-05-18 22:00:00+00:00,BETR801,no2,34.5,µg/m³
+Antwerpen,BE,2019-05-18 21:00:00+00:00,BETR801,no2,39.0,µg/m³
+Antwerpen,BE,2019-05-18 20:00:00+00:00,BETR801,no2,40.0,µg/m³
+Antwerpen,BE,2019-05-18 19:00:00+00:00,BETR801,no2,35.5,µg/m³
+Antwerpen,BE,2019-05-18 18:00:00+00:00,BETR801,no2,35.5,µg/m³
+Antwerpen,BE,2019-05-18 01:00:00+00:00,BETR801,no2,41.5,µg/m³
+Antwerpen,BE,2019-05-16 01:00:00+00:00,BETR801,no2,28.0,µg/m³
+Antwerpen,BE,2019-05-15 02:00:00+00:00,BETR801,no2,22.5,µg/m³
+Antwerpen,BE,2019-05-15 01:00:00+00:00,BETR801,no2,25.5,µg/m³
+Antwerpen,BE,2019-05-14 02:00:00+00:00,BETR801,no2,11.5,µg/m³
+Antwerpen,BE,2019-05-14 01:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,no2,14.5,µg/m³
+Antwerpen,BE,2019-05-12 02:00:00+00:00,BETR801,no2,20.0,µg/m³
+Antwerpen,BE,2019-05-12 01:00:00+00:00,BETR801,no2,17.5,µg/m³
+Antwerpen,BE,2019-05-11 02:00:00+00:00,BETR801,no2,21.0,µg/m³
+Antwerpen,BE,2019-05-11 01:00:00+00:00,BETR801,no2,26.5,µg/m³
+Antwerpen,BE,2019-05-10 02:00:00+00:00,BETR801,no2,11.5,µg/m³
+Antwerpen,BE,2019-05-10 01:00:00+00:00,BETR801,no2,10.5,µg/m³
+Antwerpen,BE,2019-05-09 02:00:00+00:00,BETR801,no2,20.5,µg/m³
+Antwerpen,BE,2019-05-09 01:00:00+00:00,BETR801,no2,20.0,µg/m³
+Antwerpen,BE,2019-05-08 02:00:00+00:00,BETR801,no2,20.5,µg/m³
+Antwerpen,BE,2019-05-08 01:00:00+00:00,BETR801,no2,23.0,µg/m³
+Antwerpen,BE,2019-05-07 02:00:00+00:00,BETR801,no2,45.0,µg/m³
+Antwerpen,BE,2019-05-07 01:00:00+00:00,BETR801,no2,50.5,µg/m³
+London,GB,2019-06-17 11:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 10:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 09:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 08:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-17 07:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-17 06:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-17 05:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 04:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-17 03:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-17 02:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-17 01:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-17 00:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-16 23:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-16 20:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-16 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-16 18:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-16 17:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-16 14:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-16 13:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-16 12:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-16 11:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-16 10:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-16 09:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-16 08:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-16 07:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-16 06:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-16 05:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-16 04:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-16 03:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-16 02:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-16 01:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-16 00:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-15 23:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-15 22:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-15 21:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-15 20:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-15 19:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-15 18:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-15 17:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-15 16:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-15 15:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-15 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-15 13:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-15 12:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-15 11:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-15 10:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-15 09:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-15 08:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-15 07:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-15 06:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-15 05:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-15 04:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-15 00:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-14 20:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 19:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 18:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-14 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-14 16:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-14 15:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-14 14:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-14 13:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-14 12:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-14 11:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-14 10:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-14 09:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-14 08:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-14 07:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-14 06:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-14 05:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-14 04:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-14 03:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-14 02:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-14 00:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-13 23:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 22:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 21:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 20:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-13 19:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-13 18:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-13 17:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 16:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-13 15:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-13 14:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-13 13:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 12:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 11:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 10:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-13 09:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 08:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 07:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-13 06:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-13 05:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 04:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-13 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-13 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-13 00:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-12 23:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-12 21:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-12 20:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-06-12 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-12 18:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-12 17:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-12 16:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-12 15:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-06-12 14:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-12 13:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-12 12:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-12 11:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-12 10:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-12 09:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-12 08:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-12 07:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-12 06:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-12 05:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-12 04:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-12 03:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-12 00:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-11 23:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-11 22:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-11 21:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 20:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 19:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-06-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-11 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-06-11 16:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-11 15:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-11 14:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-11 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-11 12:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-11 11:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 10:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-11 09:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-11 08:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-11 07:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-11 06:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-11 05:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-11 04:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-11 03:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-11 02:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-11 01:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-11 00:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-10 23:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-10 22:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-10 21:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 20:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-10 19:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-10 17:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-10 16:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-10 15:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-06-10 14:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-06-10 13:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-06-10 12:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-10 11:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-06-10 10:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-06-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-10 08:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-10 07:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-10 06:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 05:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 04:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-10 03:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-10 02:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-10 01:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-10 00:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-09 23:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-09 21:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-09 20:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-09 19:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-09 18:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-09 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-06-09 16:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-09 15:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-09 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-09 13:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-09 12:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-09 11:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-09 10:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-09 09:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-09 08:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-09 07:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-09 06:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-09 05:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-09 04:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-09 03:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-09 02:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-09 01:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-09 00:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-08 23:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-08 21:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-08 20:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-08 19:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-08 18:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-08 17:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-08 16:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-08 15:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-08 14:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-08 13:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-08 12:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-08 11:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-08 10:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 09:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-08 08:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-08 07:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 06:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-08 05:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 04:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-08 03:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-08 02:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-08 00:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-07 23:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-07 21:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-07 20:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-07 19:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-07 18:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-07 16:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-07 15:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-07 14:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-07 12:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 11:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-07 10:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 09:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-07 08:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-07 07:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 06:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-06-07 05:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 04:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 03:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 02:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-07 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-06 23:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-06 22:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-06 21:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 20:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-06 19:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 18:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-06 16:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-06 15:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-06 14:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-06 13:00:00+00:00,London Westminster,no2,10.0,µg/m³
+London,GB,2019-06-06 12:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-06 11:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-06 10:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-06 09:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-06 08:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-06 07:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-06 06:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-06 05:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-06 04:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-06 03:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-06 02:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-06 00:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-05 23:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-05 22:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-05 21:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 20:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 19:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 18:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 17:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 16:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-05 15:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-05 14:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-05 13:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-05 12:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-05 11:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-05 10:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-05 09:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-05 08:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-06-05 07:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-06-05 06:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-05 05:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-05 04:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-05 03:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-05 02:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-05 01:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-05 00:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-06-04 23:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-04 22:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-04 21:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-04 20:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-04 19:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-04 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-06-04 17:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-06-04 16:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-06-04 15:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-06-04 14:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-04 13:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-06-04 12:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-04 11:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-04 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-04 09:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-04 08:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-04 07:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-06-04 06:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-04 05:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-04 04:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-04 03:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-04 02:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-04 01:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-04 00:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-03 23:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-03 22:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-03 21:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-03 20:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-06-03 19:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-06-03 18:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-03 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-06-03 16:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-03 15:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-03 14:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-06-03 13:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-06-03 12:00:00+00:00,London Westminster,no2,14.0,µg/m³
+London,GB,2019-06-03 11:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-03 10:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-03 09:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-03 08:00:00+00:00,London Westminster,no2,7.0,µg/m³
+London,GB,2019-06-03 07:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-06-03 06:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-06-03 05:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-03 04:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-06-03 03:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-03 02:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-03 01:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-03 00:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-06-02 23:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-02 22:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-06-02 21:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-02 20:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 19:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-02 18:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-02 17:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 15:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-02 14:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-02 13:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-06-02 12:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-02 11:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-02 10:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-06-02 09:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 08:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-06-02 07:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 06:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-02 05:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-06-02 04:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-06-02 03:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-02 02:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-06-02 01:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-06-02 00:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-06-01 23:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-06-01 22:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-06-01 21:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-06-01 20:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-06-01 19:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-06-01 18:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-01 17:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-06-01 16:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-06-01 15:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-01 14:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-06-01 13:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-06-01 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-06-01 11:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-06-01 10:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-06-01 09:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-06-01 08:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-06-01 07:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-06-01 06:00:00+00:00,London Westminster,no2,4.0,µg/m³
+London,GB,2019-06-01 05:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-01 04:00:00+00:00,London Westminster,no2,11.0,µg/m³
+London,GB,2019-06-01 03:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-01 02:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-06-01 01:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-06-01 00:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-31 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-31 20:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-31 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-31 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-31 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 16:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-31 15:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-31 14:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-31 13:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-31 12:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-31 11:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-31 10:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-31 09:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-31 08:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-05-31 07:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-31 06:00:00+00:00,London Westminster,no2,8.0,µg/m³
+London,GB,2019-05-31 05:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-31 04:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-31 03:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-05-31 02:00:00+00:00,London Westminster,no2,12.0,µg/m³
+London,GB,2019-05-31 01:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-31 00:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-30 23:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-30 22:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-30 21:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-30 20:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-30 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-30 18:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-30 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-30 16:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-30 15:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-30 14:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-30 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-30 12:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-30 11:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-05-30 10:00:00+00:00,London Westminster,no2,9.0,µg/m³
+London,GB,2019-05-30 09:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-30 08:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-05-30 07:00:00+00:00,London Westminster,no2,2.0,µg/m³
+London,GB,2019-05-30 06:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 05:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 04:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 03:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 02:00:00+00:00,London Westminster,no2,0.0,µg/m³
+London,GB,2019-05-30 01:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-05-30 00:00:00+00:00,London Westminster,no2,1.0,µg/m³
+London,GB,2019-05-29 23:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 22:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 21:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-05-29 20:00:00+00:00,London Westminster,no2,6.0,µg/m³
+London,GB,2019-05-29 19:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 18:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 17:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 16:00:00+00:00,London Westminster,no2,3.0,µg/m³
+London,GB,2019-05-29 15:00:00+00:00,London Westminster,no2,5.0,µg/m³
+London,GB,2019-05-29 14:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-29 13:00:00+00:00,London Westminster,no2,13.0,µg/m³
+London,GB,2019-05-29 12:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-29 11:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-29 10:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-29 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-29 08:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-29 07:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-29 06:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-29 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-29 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-29 03:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-29 02:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-29 01:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-29 00:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-28 23:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-28 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-28 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 19:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 17:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-28 16:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-28 15:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-28 14:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-28 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-28 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-28 11:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-28 10:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-28 09:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-28 08:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-28 07:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-28 06:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-28 05:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-28 04:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-28 03:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-28 02:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-28 01:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-28 00:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-27 23:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 22:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 21:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-27 20:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-27 19:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 18:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 17:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 16:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 15:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-27 14:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-27 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-27 12:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-27 11:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-27 10:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-27 09:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 08:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 07:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 06:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-27 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-27 03:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-27 02:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-27 01:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-27 00:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 21:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-26 20:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-26 19:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 18:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-26 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 16:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-26 15:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 14:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-26 13:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-26 12:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-26 11:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-26 10:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-26 09:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-26 08:00:00+00:00,London Westminster,no2,15.0,µg/m³
+London,GB,2019-05-26 07:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-26 06:00:00+00:00,London Westminster,no2,17.0,µg/m³
+London,GB,2019-05-26 05:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-26 04:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-26 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-26 01:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-26 00:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-25 23:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-25 22:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-25 21:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-25 20:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-25 19:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-25 18:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-25 17:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-25 16:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-25 15:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-25 14:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-25 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-25 12:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-25 11:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-25 10:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-25 09:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-25 08:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-25 07:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-25 06:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-25 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-25 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-25 03:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-25 02:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-25 01:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-25 00:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-24 23:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-24 22:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-24 21:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-24 20:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-24 19:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-24 18:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-24 17:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-24 16:00:00+00:00,London Westminster,no2,43.0,µg/m³
+London,GB,2019-05-24 15:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-24 14:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-24 13:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-24 12:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-24 11:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-24 10:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-24 09:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-24 08:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-24 07:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-24 06:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-24 05:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-24 04:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-24 03:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-24 02:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-24 00:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-23 23:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-23 22:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-23 21:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-23 20:00:00+00:00,London Westminster,no2,45.0,µg/m³
+London,GB,2019-05-23 19:00:00+00:00,London Westminster,no2,51.0,µg/m³
+London,GB,2019-05-23 18:00:00+00:00,London Westminster,no2,54.0,µg/m³
+London,GB,2019-05-23 17:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-05-23 16:00:00+00:00,London Westminster,no2,53.0,µg/m³
+London,GB,2019-05-23 15:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-23 14:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-23 13:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-23 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-23 11:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-23 10:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-23 09:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-23 08:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-23 07:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-23 06:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-23 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-23 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-23 03:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-23 02:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-23 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-23 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-22 23:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-22 22:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-22 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 20:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-22 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-22 18:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-22 17:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-22 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-22 15:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-22 14:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-22 13:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-22 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-22 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-22 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 09:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 08:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-22 07:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-22 06:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-22 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-22 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-22 03:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-22 02:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-22 01:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-22 00:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-21 23:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 22:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 21:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 20:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-21 19:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-21 18:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-21 17:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-21 16:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-21 15:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-21 14:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-21 13:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-21 12:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 11:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 10:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-21 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-21 08:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-21 07:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-21 06:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-21 05:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-21 04:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-21 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-21 01:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-21 00:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-20 23:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-20 22:00:00+00:00,London Westminster,no2,47.0,µg/m³
+London,GB,2019-05-20 21:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-20 20:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 19:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 18:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-20 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-20 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 15:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 14:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 13:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 10:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 08:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-20 07:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-20 06:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-20 05:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-20 04:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-20 03:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 02:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-20 01:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-20 00:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-19 23:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 22:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 21:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 20:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-19 19:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-19 18:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-19 17:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-19 16:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-19 15:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-19 14:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-19 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-19 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-19 11:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-19 10:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-19 09:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-19 08:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-19 07:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-19 06:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-19 05:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 04:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 03:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 02:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 01:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-19 00:00:00+00:00,London Westminster,no2,49.0,µg/m³
+London,GB,2019-05-18 23:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-18 22:00:00+00:00,London Westminster,no2,46.0,µg/m³
+London,GB,2019-05-18 21:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-18 20:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-18 19:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-18 18:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-18 17:00:00+00:00,London Westminster,no2,42.0,µg/m³
+London,GB,2019-05-18 16:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-18 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-18 14:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-18 13:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 12:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-18 11:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-18 10:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-18 09:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-18 08:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-18 07:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 06:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-18 05:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 04:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-18 03:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-18 02:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-18 01:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-18 00:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-17 23:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-17 22:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-17 21:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-17 20:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-17 19:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-17 18:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-17 17:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-17 16:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 15:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-17 14:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-17 13:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-17 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 11:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 10:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-17 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-17 08:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-17 07:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-17 06:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-17 05:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 04:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 03:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 02:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-17 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-17 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-16 23:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 22:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 20:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 19:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-16 18:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-16 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-16 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-16 15:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 14:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-16 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-16 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-16 11:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-16 09:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-16 08:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 07:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-16 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 05:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 04:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-16 03:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-16 02:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-16 01:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-16 00:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 23:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 22:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 21:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-15 20:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-15 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 18:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-15 17:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 16:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-15 15:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-15 14:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-15 13:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-15 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-15 11:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-15 10:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-15 09:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-15 08:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-15 07:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-15 05:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-15 04:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-15 03:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-15 02:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-15 00:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-14 23:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 22:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 21:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 20:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-14 19:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-14 18:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 17:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-14 16:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-14 15:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-14 14:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 13:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 12:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-14 11:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-14 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-14 09:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 08:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-14 07:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-14 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-14 05:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 04:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-14 03:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 02:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-14 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-14 00:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-13 23:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 22:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 21:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 20:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 19:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 18:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-13 17:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-13 16:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-13 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-13 14:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-13 13:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-13 12:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-13 11:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-13 10:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-13 09:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-13 08:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-13 07:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-13 06:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-13 05:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-13 04:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-13 03:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-13 02:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-13 01:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-13 00:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 23:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 22:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 21:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-12 20:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 19:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 18:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 17:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-12 16:00:00+00:00,London Westminster,no2,23.0,µg/m³
+London,GB,2019-05-12 15:00:00+00:00,London Westminster,no2,22.0,µg/m³
+London,GB,2019-05-12 14:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-12 13:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-12 12:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-12 11:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-12 10:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-12 09:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-12 08:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-12 07:00:00+00:00,London Westminster,no2,44.0,µg/m³
+London,GB,2019-05-12 06:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-12 05:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-12 04:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-12 03:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-12 02:00:00+00:00,London Westminster,no2,38.0,µg/m³
+London,GB,2019-05-12 01:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-12 00:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-11 23:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-11 22:00:00+00:00,London Westminster,no2,37.0,µg/m³
+London,GB,2019-05-11 21:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-11 20:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-11 19:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-11 18:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-11 17:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-11 16:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-11 15:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-11 09:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-11 08:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-11 07:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-11 06:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-11 05:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-11 04:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-11 03:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-11 02:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-11 01:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-11 00:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-10 23:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-10 22:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-10 21:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 19:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 18:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-10 17:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-10 16:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-10 15:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-10 14:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-10 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-10 12:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-10 11:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-10 10:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-10 09:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-10 08:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-10 07:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-10 06:00:00+00:00,London Westminster,no2,39.0,µg/m³
+London,GB,2019-05-10 05:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-10 04:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-10 03:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-10 02:00:00+00:00,London Westminster,no2,41.0,µg/m³
+London,GB,2019-05-10 01:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-05-10 00:00:00+00:00,London Westminster,no2,52.0,µg/m³
+London,GB,2019-05-09 23:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-05-09 22:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-05-09 21:00:00+00:00,London Westminster,no2,65.0,µg/m³
+London,GB,2019-05-09 20:00:00+00:00,London Westminster,no2,59.0,µg/m³
+London,GB,2019-05-09 19:00:00+00:00,London Westminster,no2,62.0,µg/m³
+London,GB,2019-05-09 18:00:00+00:00,London Westminster,no2,58.0,µg/m³
+London,GB,2019-05-09 17:00:00+00:00,London Westminster,no2,60.0,µg/m³
+London,GB,2019-05-09 16:00:00+00:00,London Westminster,no2,67.0,µg/m³
+London,GB,2019-05-09 15:00:00+00:00,London Westminster,no2,97.0,µg/m³
+London,GB,2019-05-09 14:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-09 13:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-09 12:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-09 11:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-09 10:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-09 09:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-09 08:00:00+00:00,London Westminster,no2,35.0,µg/m³
+London,GB,2019-05-09 07:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 06:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 05:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 04:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-09 03:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-09 02:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-09 00:00:00+00:00,London Westminster,no2,30.0,µg/m³
+London,GB,2019-05-08 23:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-08 21:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-08 20:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-08 19:00:00+00:00,London Westminster,no2,25.0,µg/m³
+London,GB,2019-05-08 18:00:00+00:00,London Westminster,no2,40.0,µg/m³
+London,GB,2019-05-08 17:00:00+00:00,London Westminster,no2,31.0,µg/m³
+London,GB,2019-05-08 16:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-08 15:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-08 14:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-08 13:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-08 12:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-08 11:00:00+00:00,London Westminster,no2,27.0,µg/m³
+London,GB,2019-05-08 10:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-08 09:00:00+00:00,London Westminster,no2,33.0,µg/m³
+London,GB,2019-05-08 08:00:00+00:00,London Westminster,no2,36.0,µg/m³
+London,GB,2019-05-08 07:00:00+00:00,London Westminster,no2,34.0,µg/m³
+London,GB,2019-05-08 06:00:00+00:00,London Westminster,no2,29.0,µg/m³
+London,GB,2019-05-08 05:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-08 04:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-08 03:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-08 02:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-08 01:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-08 00:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-07 23:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-07 21:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-07 20:00:00+00:00,London Westminster,no2,24.0,µg/m³
+London,GB,2019-05-07 19:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 18:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 17:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-07 16:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 15:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 14:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-07 13:00:00+00:00,London Westminster,no2,20.0,µg/m³
+London,GB,2019-05-07 12:00:00+00:00,London Westminster,no2,18.0,µg/m³
+London,GB,2019-05-07 11:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-07 10:00:00+00:00,London Westminster,no2,21.0,µg/m³
+London,GB,2019-05-07 09:00:00+00:00,London Westminster,no2,28.0,µg/m³
+London,GB,2019-05-07 08:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-07 07:00:00+00:00,London Westminster,no2,32.0,µg/m³
+London,GB,2019-05-07 06:00:00+00:00,London Westminster,no2,26.0,µg/m³
+London,GB,2019-05-07 04:00:00+00:00,London Westminster,no2,16.0,µg/m³
+London,GB,2019-05-07 03:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-07 02:00:00+00:00,London Westminster,no2,19.0,µg/m³
+London,GB,2019-05-07 01:00:00+00:00,London Westminster,no2,23.0,µg/m³
diff --git a/doc/data/air_quality_parameters.csv b/doc/data/air_quality_parameters.csv
new file mode 100644
index 0000000000000..915f6300e43b8
--- /dev/null
+++ b/doc/data/air_quality_parameters.csv
@@ -0,0 +1,8 @@
+id,description,name
+bc,Black Carbon,BC
+co,Carbon Monoxide,CO
+no2,Nitrogen Dioxide,NO2
+o3,Ozone,O3
+pm10,Particulate matter less than 10 micrometers in diameter,PM10
+pm25,Particulate matter less than 2.5 micrometers in diameter,PM2.5
+so2,Sulfur Dioxide,SO2
diff --git a/doc/data/air_quality_pm25_long.csv b/doc/data/air_quality_pm25_long.csv
new file mode 100644
index 0000000000000..f74053c249339
--- /dev/null
+++ b/doc/data/air_quality_pm25_long.csv
@@ -0,0 +1,1111 @@
+city,country,date.utc,location,parameter,value,unit
+Antwerpen,BE,2019-06-18 06:00:00+00:00,BETR801,pm25,18.0,µg/m³
+Antwerpen,BE,2019-06-17 08:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-06-17 07:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-06-17 06:00:00+00:00,BETR801,pm25,16.0,µg/m³
+Antwerpen,BE,2019-06-17 05:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-06-17 04:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-06-17 03:00:00+00:00,BETR801,pm25,7.0,µg/m³
+Antwerpen,BE,2019-06-17 02:00:00+00:00,BETR801,pm25,7.0,µg/m³
+Antwerpen,BE,2019-06-17 01:00:00+00:00,BETR801,pm25,8.0,µg/m³
+Antwerpen,BE,2019-06-16 01:00:00+00:00,BETR801,pm25,15.0,µg/m³
+Antwerpen,BE,2019-06-15 01:00:00+00:00,BETR801,pm25,11.0,µg/m³
+Antwerpen,BE,2019-06-14 09:00:00+00:00,BETR801,pm25,12.0,µg/m³
+Antwerpen,BE,2019-06-13 01:00:00+00:00,BETR801,pm25,3.0,µg/m³
+Antwerpen,BE,2019-06-12 01:00:00+00:00,BETR801,pm25,16.0,µg/m³
+Antwerpen,BE,2019-06-11 01:00:00+00:00,BETR801,pm25,3.5,µg/m³
+Antwerpen,BE,2019-06-10 01:00:00+00:00,BETR801,pm25,8.5,µg/m³
+Antwerpen,BE,2019-06-09 01:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-06-08 01:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-06-06 01:00:00+00:00,BETR801,pm25,6.5,µg/m³
+Antwerpen,BE,2019-06-05 01:00:00+00:00,BETR801,pm25,11.0,µg/m³
+Antwerpen,BE,2019-06-04 01:00:00+00:00,BETR801,pm25,10.5,µg/m³
+Antwerpen,BE,2019-06-03 01:00:00+00:00,BETR801,pm25,12.5,µg/m³
+Antwerpen,BE,2019-06-02 01:00:00+00:00,BETR801,pm25,19.0,µg/m³
+Antwerpen,BE,2019-06-01 01:00:00+00:00,BETR801,pm25,9.0,µg/m³
+Antwerpen,BE,2019-05-31 01:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-05-30 01:00:00+00:00,BETR801,pm25,5.0,µg/m³
+Antwerpen,BE,2019-05-29 01:00:00+00:00,BETR801,pm25,5.5,µg/m³
+Antwerpen,BE,2019-05-28 01:00:00+00:00,BETR801,pm25,7.0,µg/m³
+Antwerpen,BE,2019-05-27 01:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-05-26 01:00:00+00:00,BETR801,pm25,26.5,µg/m³
+Antwerpen,BE,2019-05-25 01:00:00+00:00,BETR801,pm25,10.0,µg/m³
+Antwerpen,BE,2019-05-24 01:00:00+00:00,BETR801,pm25,13.0,µg/m³
+Antwerpen,BE,2019-05-23 01:00:00+00:00,BETR801,pm25,7.5,µg/m³
+Antwerpen,BE,2019-05-22 01:00:00+00:00,BETR801,pm25,15.5,µg/m³
+Antwerpen,BE,2019-05-21 01:00:00+00:00,BETR801,pm25,20.5,µg/m³
+Antwerpen,BE,2019-05-20 17:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-05-20 16:00:00+00:00,BETR801,pm25,17.0,µg/m³
+Antwerpen,BE,2019-05-20 15:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-05-20 14:00:00+00:00,BETR801,pm25,14.5,µg/m³
+Antwerpen,BE,2019-05-20 13:00:00+00:00,BETR801,pm25,17.0,µg/m³
+Antwerpen,BE,2019-05-20 12:00:00+00:00,BETR801,pm25,17.5,µg/m³
+Antwerpen,BE,2019-05-20 11:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-20 10:00:00+00:00,BETR801,pm25,10.5,µg/m³
+Antwerpen,BE,2019-05-20 09:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-20 08:00:00+00:00,BETR801,pm25,19.5,µg/m³
+Antwerpen,BE,2019-05-20 07:00:00+00:00,BETR801,pm25,23.5,µg/m³
+Antwerpen,BE,2019-05-20 06:00:00+00:00,BETR801,pm25,22.0,µg/m³
+Antwerpen,BE,2019-05-20 05:00:00+00:00,BETR801,pm25,25.0,µg/m³
+Antwerpen,BE,2019-05-20 04:00:00+00:00,BETR801,pm25,24.5,µg/m³
+Antwerpen,BE,2019-05-20 03:00:00+00:00,BETR801,pm25,15.0,µg/m³
+Antwerpen,BE,2019-05-20 02:00:00+00:00,BETR801,pm25,18.5,µg/m³
+Antwerpen,BE,2019-05-20 01:00:00+00:00,BETR801,pm25,28.0,µg/m³
+Antwerpen,BE,2019-05-19 21:00:00+00:00,BETR801,pm25,35.5,µg/m³
+Antwerpen,BE,2019-05-19 20:00:00+00:00,BETR801,pm25,40.0,µg/m³
+Antwerpen,BE,2019-05-19 19:00:00+00:00,BETR801,pm25,43.5,µg/m³
+Antwerpen,BE,2019-05-19 18:00:00+00:00,BETR801,pm25,35.0,µg/m³
+Antwerpen,BE,2019-05-19 17:00:00+00:00,BETR801,pm25,34.0,µg/m³
+Antwerpen,BE,2019-05-19 16:00:00+00:00,BETR801,pm25,36.5,µg/m³
+Antwerpen,BE,2019-05-19 15:00:00+00:00,BETR801,pm25,44.0,µg/m³
+Antwerpen,BE,2019-05-19 14:00:00+00:00,BETR801,pm25,43.5,µg/m³
+Antwerpen,BE,2019-05-19 13:00:00+00:00,BETR801,pm25,46.0,µg/m³
+Antwerpen,BE,2019-05-19 12:00:00+00:00,BETR801,pm25,43.0,µg/m³
+Antwerpen,BE,2019-05-19 11:00:00+00:00,BETR801,pm25,41.0,µg/m³
+Antwerpen,BE,2019-05-19 10:00:00+00:00,BETR801,pm25,41.5,µg/m³
+Antwerpen,BE,2019-05-19 09:00:00+00:00,BETR801,pm25,42.5,µg/m³
+Antwerpen,BE,2019-05-19 08:00:00+00:00,BETR801,pm25,51.5,µg/m³
+Antwerpen,BE,2019-05-19 07:00:00+00:00,BETR801,pm25,56.0,µg/m³
+Antwerpen,BE,2019-05-19 06:00:00+00:00,BETR801,pm25,58.5,µg/m³
+Antwerpen,BE,2019-05-19 05:00:00+00:00,BETR801,pm25,60.0,µg/m³
+Antwerpen,BE,2019-05-19 04:00:00+00:00,BETR801,pm25,56.5,µg/m³
+Antwerpen,BE,2019-05-19 03:00:00+00:00,BETR801,pm25,52.5,µg/m³
+Antwerpen,BE,2019-05-19 02:00:00+00:00,BETR801,pm25,51.5,µg/m³
+Antwerpen,BE,2019-05-19 01:00:00+00:00,BETR801,pm25,52.0,µg/m³
+Antwerpen,BE,2019-05-19 00:00:00+00:00,BETR801,pm25,49.5,µg/m³
+Antwerpen,BE,2019-05-18 23:00:00+00:00,BETR801,pm25,45.5,µg/m³
+Antwerpen,BE,2019-05-18 22:00:00+00:00,BETR801,pm25,42.0,µg/m³
+Antwerpen,BE,2019-05-18 21:00:00+00:00,BETR801,pm25,40.5,µg/m³
+Antwerpen,BE,2019-05-18 20:00:00+00:00,BETR801,pm25,41.0,µg/m³
+Antwerpen,BE,2019-05-18 19:00:00+00:00,BETR801,pm25,36.5,µg/m³
+Antwerpen,BE,2019-05-18 18:00:00+00:00,BETR801,pm25,37.0,µg/m³
+Antwerpen,BE,2019-05-18 01:00:00+00:00,BETR801,pm25,24.0,µg/m³
+Antwerpen,BE,2019-05-17 01:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-16 01:00:00+00:00,BETR801,pm25,11.0,µg/m³
+Antwerpen,BE,2019-05-15 02:00:00+00:00,BETR801,pm25,12.5,µg/m³
+Antwerpen,BE,2019-05-15 01:00:00+00:00,BETR801,pm25,13.0,µg/m³
+Antwerpen,BE,2019-05-14 02:00:00+00:00,BETR801,pm25,4.0,µg/m³
+Antwerpen,BE,2019-05-14 01:00:00+00:00,BETR801,pm25,4.0,µg/m³
+Antwerpen,BE,2019-05-13 02:00:00+00:00,BETR801,pm25,5.5,µg/m³
+Antwerpen,BE,2019-05-13 01:00:00+00:00,BETR801,pm25,5.0,µg/m³
+Antwerpen,BE,2019-05-12 02:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-05-12 01:00:00+00:00,BETR801,pm25,6.0,µg/m³
+Antwerpen,BE,2019-05-11 02:00:00+00:00,BETR801,pm25,19.5,µg/m³
+Antwerpen,BE,2019-05-11 01:00:00+00:00,BETR801,pm25,17.0,µg/m³
+Antwerpen,BE,2019-05-10 02:00:00+00:00,BETR801,pm25,13.5,µg/m³
+Antwerpen,BE,2019-05-10 01:00:00+00:00,BETR801,pm25,11.5,µg/m³
+Antwerpen,BE,2019-05-09 02:00:00+00:00,BETR801,pm25,3.5,µg/m³
+Antwerpen,BE,2019-05-09 01:00:00+00:00,BETR801,pm25,4.5,µg/m³
+Antwerpen,BE,2019-05-08 02:00:00+00:00,BETR801,pm25,14.0,µg/m³
+Antwerpen,BE,2019-05-08 01:00:00+00:00,BETR801,pm25,14.5,µg/m³
+Antwerpen,BE,2019-05-07 02:00:00+00:00,BETR801,pm25,14.0,µg/m³
+Antwerpen,BE,2019-05-07 01:00:00+00:00,BETR801,pm25,12.5,µg/m³
+London,GB,2019-06-21 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-20 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-20 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-20 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-20 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-19 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-19 12:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-19 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-19 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-06-18 23:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-06-18 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-06-18 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-18 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-18 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-17 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-17 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-16 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-16 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-15 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-15 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-14 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-14 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-14 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-13 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-13 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-13 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-13 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-13 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-12 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-12 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-11 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-11 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-10 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-10 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-09 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-09 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-08 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-08 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-08 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-08 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-08 03:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-08 02:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-08 00:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 23:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 21:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 20:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 19:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-06-07 18:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 17:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 16:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 15:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 14:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 13:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-06-07 12:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 11:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 10:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 09:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 08:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-06-07 07:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-06-07 06:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-06-07 05:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-06-07 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-06-07 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-07 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-07 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-07 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-06 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-06 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-06 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-06 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-05 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-05 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-05 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-05 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-05 01:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-05 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-06-04 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-04 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-04 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-03 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-03 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-06-02 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-02 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-06-01 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-06-01 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-06-01 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 22:00:00+00:00,London Westminster,pm25,5.0,µg/m³
+London,GB,2019-05-31 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-31 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-31 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-30 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-30 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-29 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-29 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-28 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-28 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-27 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-27 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 20:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 19:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 18:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 17:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 16:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 15:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 14:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 13:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 12:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 11:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 07:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 06:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 05:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 04:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 03:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 02:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 01:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-26 00:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 23:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 22:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 21:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-25 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-25 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-25 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-25 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-25 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-25 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-25 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-24 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-24 22:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-24 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-24 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-24 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 06:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-23 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 02:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-23 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 10:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 09:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 08:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-22 05:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-22 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-21 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-21 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-21 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-21 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-21 07:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-21 06:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-21 05:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-21 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-21 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-21 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-21 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-21 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 22:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 21:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 20:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 19:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 18:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-20 17:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-20 16:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-20 15:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 14:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 13:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 12:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 11:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 10:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-20 09:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-20 08:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-20 07:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-20 06:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-20 05:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-20 04:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 03:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 02:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 01:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-20 00:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 23:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 22:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 21:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 20:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 19:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 18:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 17:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 16:00:00+00:00,London Westminster,pm25,20.0,µg/m³
+London,GB,2019-05-19 15:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 14:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 13:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 12:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 11:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 10:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 09:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 08:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 07:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 06:00:00+00:00,London Westminster,pm25,19.0,µg/m³
+London,GB,2019-05-19 05:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 04:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 03:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 02:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 01:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-19 00:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 23:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 22:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 21:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 20:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 19:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 18:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 17:00:00+00:00,London Westminster,pm25,18.0,µg/m³
+London,GB,2019-05-18 16:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 15:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 14:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 13:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 12:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 11:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 10:00:00+00:00,London Westminster,pm25,17.0,µg/m³
+London,GB,2019-05-18 09:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-18 08:00:00+00:00,London Westminster,pm25,16.0,µg/m³
+London,GB,2019-05-18 07:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-18 06:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-18 05:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-18 04:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-18 03:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-18 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-18 01:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-18 00:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-17 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-17 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 20:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 13:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 12:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 11:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-17 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 04:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 03:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-17 01:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-17 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-16 23:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-16 22:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-16 21:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-16 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-16 16:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 14:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 13:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 12:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 11:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 10:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 09:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 08:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 07:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 06:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 05:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 04:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 03:00:00+00:00,London Westminster,pm25,15.0,µg/m³
+London,GB,2019-05-16 02:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 01:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-16 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-15 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-15 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-15 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-15 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-15 19:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 18:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 17:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 16:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-15 15:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 14:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 13:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 12:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-15 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-15 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-15 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-15 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 03:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 02:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-15 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-14 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-14 22:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 21:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 20:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 19:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 18:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 17:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 16:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 15:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 14:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 13:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 12:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 11:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 10:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 09:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 08:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 07:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 04:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 03:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-14 01:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-14 00:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 23:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 22:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 21:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 20:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 19:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 18:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 17:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 16:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 15:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 14:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 13:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 12:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 11:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 10:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-13 09:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-13 08:00:00+00:00,London Westminster,pm25,6.0,µg/m³
+London,GB,2019-05-13 07:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 06:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 05:00:00+00:00,London Westminster,pm25,7.0,µg/m³
+London,GB,2019-05-13 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-13 00:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-12 23:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-12 22:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 14:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-12 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-12 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-12 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-12 10:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 09:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 08:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 07:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-12 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-12 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-12 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-12 03:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-12 02:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-12 01:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-12 00:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-11 23:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-11 22:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 21:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 20:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 19:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 18:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 17:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 16:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 15:00:00+00:00,London Westminster,pm25,14.0,µg/m³
+London,GB,2019-05-11 09:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 08:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 07:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-11 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-11 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-11 04:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-11 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-11 02:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-11 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-11 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-10 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-10 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 20:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 18:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 17:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 16:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 15:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 14:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 13:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 12:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 11:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 10:00:00+00:00,London Westminster,pm25,13.0,µg/m³
+London,GB,2019-05-10 09:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 08:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 07:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 06:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 05:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 04:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 03:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 02:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-10 01:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-10 00:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-09 23:00:00+00:00,London Westminster,pm25,12.0,µg/m³
+London,GB,2019-05-09 22:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-09 21:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-09 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 19:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 18:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-09 10:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 09:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 08:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 05:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 04:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 03:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 02:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-09 00:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 23:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 21:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 20:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 19:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 18:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 17:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 16:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 15:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-08 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 07:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 06:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 05:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 04:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-08 03:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-08 02:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 01:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-08 00:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 23:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 21:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 20:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 19:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-07 18:00:00+00:00,London Westminster,pm25,11.0,µg/m³
+London,GB,2019-05-07 17:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 16:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 15:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 14:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 13:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 12:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 11:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 10:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 09:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 08:00:00+00:00,London Westminster,pm25,10.0,µg/m³
+London,GB,2019-05-07 07:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-07 06:00:00+00:00,London Westminster,pm25,9.0,µg/m³
+London,GB,2019-05-07 04:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-07 03:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-07 02:00:00+00:00,London Westminster,pm25,8.0,µg/m³
+London,GB,2019-05-07 01:00:00+00:00,London Westminster,pm25,8.0,µg/m³
diff --git a/doc/data/air_quality_stations.csv b/doc/data/air_quality_stations.csv
new file mode 100644
index 0000000000000..9ab1a377dcdae
--- /dev/null
+++ b/doc/data/air_quality_stations.csv
@@ -0,0 +1,67 @@
+location,coordinates.latitude,coordinates.longitude
+BELAL01,51.23619,4.38522
+BELHB23,51.1703,4.341
+BELLD01,51.10998,5.00486
+BELLD02,51.12038,5.02155
+BELR833,51.32766,4.36226
+BELSA04,51.31393,4.40387
+BELWZ02,51.1928,5.22153
+BETM802,51.26099,4.4244
+BETN016,51.23365,5.16398
+BETR801,51.20966,4.43182
+BETR802,51.20952,4.43179
+BETR803,51.22863,4.42845
+BETR805,51.20823,4.42156
+BETR811,51.2521,4.49136
+BETR815,51.2147,4.33221
+BETR817,51.17713,4.41795
+BETR820,51.32042,4.44481
+BETR822,51.26429,4.34128
+BETR831,51.3488,4.33971
+BETR834,51.092,4.3801
+BETR891,51.25581,4.38536
+BETR893,51.28138,4.38577
+BETR894,51.2835,4.3495
+BETR897,51.25011,4.3421
+FR04004,48.89167,2.34667
+FR04012,48.82778,2.3275
+FR04014,48.83724,2.3939
+FR04014,48.83722,2.3939
+FR04031,48.86887,2.31194
+FR04031,48.86889,2.31194
+FR04037,48.82861,2.36028
+FR04060,48.8572,2.2933
+FR04071,48.8564,2.33528
+FR04071,48.85639,2.33528
+FR04118,48.87027,2.3325
+FR04118,48.87029,2.3325
+FR04131,48.87333,2.33028
+FR04135,48.83795,2.40806
+FR04135,48.83796,2.40806
+FR04141,48.85278,2.36056
+FR04141,48.85279,2.36056
+FR04143,48.859,2.351
+FR04143,48.85944,2.35111
+FR04179,48.83038,2.26989
+FR04329,48.8386,2.41279
+FR04329,48.83862,2.41278
+Camden Kerbside,51.54421,-0.17527
+Ealing Horn Lane,51.51895,-0.26562
+Haringey Roadside,51.5993,-0.06822
+London Bexley,51.46603,0.18481
+London Bloomsbury,51.52229,-0.12589
+London Eltham,51.45258,0.07077
+London Haringey Priory Park South,51.58413,-0.12525
+London Harlington,51.48879,-0.44161
+London Harrow Stanmore,51.61733,-0.29878
+London Hillingdon,51.49633,-0.46086
+London Marylebone Road,51.52253,-0.15461
+London N. Kensington,51.52105,-0.21349
+London Teddington,51.42099,-0.33965
+London Teddington Bushy Park,51.42529,-0.34561
+London Westminster,51.49467,-0.13193
+Southend-on-Sea,51.5442,0.67841
+Southwark A2 Old Kent Road,51.4805,-0.05955
+Thurrock,51.47707,0.31797
+Tower Hamlets Roadside,51.52253,-0.04216
+Groton Fort Griswold,41.3536,-72.0789
diff --git a/doc/data/titanic.csv b/doc/data/titanic.csv
new file mode 100644
index 0000000000000..5cc466e97cf12
--- /dev/null
+++ b/doc/data/titanic.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
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diff --git a/doc/source/_static/css/getting_started.css b/doc/source/_static/css/getting_started.css
new file mode 100644
index 0000000000000..bb24761cdb159
--- /dev/null
+++ b/doc/source/_static/css/getting_started.css
@@ -0,0 +1,251 @@
+/* Getting started pages */
+
+/* data intro */
+.gs-data {
+ font-size: 0.9rem;
+}
+
+.gs-data-title {
+ align-items: center;
+ font-size: 0.9rem;
+}
+
+.gs-data-title .badge {
+ margin: 10px;
+ padding: 5px;
+}
+
+.gs-data .badge {
+ cursor: pointer;
+ padding: 10px;
+ border: none;
+ text-align: left;
+ outline: none;
+ font-size: 12px;
+}
+
+.gs-data .btn {
+ background-color: grey;
+ border: none;
+}
+
+/* note/alert properties */
+
+.alert-heading {
+ font-size: 1.2rem;
+}
+
+/* callout properties */
+.gs-callout {
+ padding: 20px;
+ margin: 20px 0;
+ border: 1px solid #eee;
+ border-left-width: 5px;
+ border-radius: 3px;
+}
+.gs-callout h4 {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
+.gs-callout p:last-child {
+ margin-bottom: 0;
+}
+.gs-callout code {
+ border-radius: 3px;
+}
+.gs-callout+.gs-callout {
+ margin-top: -5px;
+}
+.gs-callout-remember {
+ border-left-color: #f0ad4e;
+ align-items: center;
+ font-size: 1.2rem;
+}
+.gs-callout-remember h4 {
+ color: #f0ad4e;
+}
+
+/* reference to user guide */
+.gs-torefguide {
+ align-items: center;
+ font-size: 0.9rem;
+}
+
+.gs-torefguide .badge {
+ background-color: #130654;
+ margin: 10px 10px 10px 0px;
+ padding: 5px;
+}
+
+.gs-torefguide a {
+ margin-left: 5px;
+ color: #130654;
+ border-bottom: 1px solid #FFCA00f3;
+ box-shadow: 0px -10px 0px #FFCA00f3 inset;
+}
+
+.gs-torefguide p {
+ margin-top: 1rem;
+}
+
+.gs-torefguide a:hover {
+ margin-left: 5px;
+ color: grey;
+ text-decoration: none;
+ border-bottom: 1px solid #b2ff80f3;
+ box-shadow: 0px -10px 0px #b2ff80f3 inset;
+}
+
+/* question-task environment */
+
+ul.task-bullet, ol.custom-bullet{
+ list-style:none;
+ padding-left: 0;
+ margin-top: 2em;
+}
+
+ul.task-bullet > li:before {
+ content:"";
+ height:2em;
+ width:2em;
+ display:block;
+ float:left;
+ margin-left:-2em;
+ background-position:center;
+ background-repeat:no-repeat;
+ background-color: #130654;
+ border-radius: 50%;
+ background-size:100%;
+ background-image:url('../question_mark_noback.svg');
+ }
+
+ul.task-bullet > li {
+ border-left: 1px solid #130654;
+ padding-left:1em;
+}
+
+ul.task-bullet > li > p:first-child {
+ font-size: 1.1rem;
+ padding-left: 0.75rem;
+}
+
+/* Getting started index page */
+
+.intro-card {
+ background:#FFF;
+ border-radius:0;
+ padding: 30px 10px 10px 10px;
+ margin: 10px 0px;
+}
+
+.intro-card .card-text {
+ margin:20px 0px;
+ /*min-height: 150px; */
+}
+
+.intro-card .card-img-top {
+ margin: 10px;
+}
+
+.install-block {
+ padding-bottom: 30px;
+}
+
+.install-card .card-header {
+ border: none;
+ background-color:white;
+ color: #150458;
+ font-size: 1.1rem;
+ font-weight: bold;
+ padding: 1rem 1rem 0rem 1rem;
+}
+
+.install-card .card-footer {
+ border: none;
+ background-color:white;
+}
+
+.install-card pre {
+ margin: 0 1em 1em 1em;
+}
+
+.custom-button {
+ background-color:#DCDCDC;
+ border: none;
+ color: #484848;
+ text-align: center;
+ text-decoration: none;
+ display: inline-block;
+ font-size: 0.9rem;
+ border-radius: 0.5rem;
+ max-width: 120px;
+ padding: 0.5rem 0rem;
+}
+
+.custom-button a {
+ color: #484848;
+}
+
+.custom-button p {
+ margin-top: 0;
+ margin-bottom: 0rem;
+ color: #484848;
+}
+
+/* intro to tutorial collapsed cards */
+
+.tutorial-accordion {
+ margin-top: 20px;
+ margin-bottom: 20px;
+}
+
+.tutorial-card .card-header.card-link .btn {
+ margin-right: 12px;
+}
+
+.tutorial-card .card-header.card-link .btn:after {
+ content: "-";
+}
+
+.tutorial-card .card-header.card-link.collapsed .btn:after {
+ content: "+";
+}
+
+.tutorial-card-header-1 {
+ justify-content: space-between;
+ align-items: center;
+}
+
+.tutorial-card-header-2 {
+ justify-content: flex-start;
+ align-items: center;
+ font-size: 1.3rem;
+}
+
+.tutorial-card .card-header {
+ cursor: pointer;
+ background-color: white;
+}
+
+.tutorial-card .card-body {
+ background-color: #F0F0F0;
+}
+
+.tutorial-card .badge {
+ background-color: #130654;
+ margin: 10px 10px 10px 10px;
+ padding: 5px;
+}
+
+.tutorial-card .gs-badge-link p {
+ margin: 0px;
+}
+
+.tutorial-card .gs-badge-link a {
+ color: white;
+ text-decoration: none;
+}
+
+.tutorial-card .badge:hover {
+ background-color: grey;
+}
diff --git a/doc/source/_static/logo_r.svg b/doc/source/_static/logo_r.svg
new file mode 100644
index 0000000000000..389b03c113e0f
--- /dev/null
+++ b/doc/source/_static/logo_r.svg
@@ -0,0 +1,14 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid" width="724" height="561" viewBox="0 0 724 561">
+ <defs>
+ <linearGradient id="gradientFill-1" x1="0" x2="1" y1="0" y2="1" gradientUnits="objectBoundingBox" spreadMethod="pad">
+ <stop offset="0" stop-color="rgb(203,206,208)" stop-opacity="1"/>
+ <stop offset="1" stop-color="rgb(132,131,139)" stop-opacity="1"/>
+ </linearGradient>
+ <linearGradient id="gradientFill-2" x1="0" x2="1" y1="0" y2="1" gradientUnits="objectBoundingBox" spreadMethod="pad">
+ <stop offset="0" stop-color="rgb(39,109,195)" stop-opacity="1"/>
+ <stop offset="1" stop-color="rgb(22,92,170)" stop-opacity="1"/>
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diff --git a/doc/source/conf.py b/doc/source/conf.py
index f126ad99cc463..6891f7d82d6c1 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -231,6 +231,7 @@
html_static_path = ["_static"]
html_css_files = [
+ "css/getting_started.css",
"css/pandas.css",
]
diff --git a/doc/source/getting_started/dsintro.rst b/doc/source/getting_started/dsintro.rst
index 8bd271815549d..93e60ff9d239c 100644
--- a/doc/source/getting_started/dsintro.rst
+++ b/doc/source/getting_started/dsintro.rst
@@ -444,6 +444,7 @@ dtype. For example:
data
pd.DataFrame.from_records(data, index='C')
+.. _basics.dataframe.sel_add_del:
Column selection, addition, deletion
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/doc/source/getting_started/index.rst b/doc/source/getting_started/index.rst
index 34bb4f930f175..a2f8f79f22ae4 100644
--- a/doc/source/getting_started/index.rst
+++ b/doc/source/getting_started/index.rst
@@ -6,15 +6,666 @@
Getting started
===============
+Installation
+------------
+
+Before you can use pandas, you’ll need to get it installed.
+
+.. raw:: html
+
+ <div class="container">
+ <div class="row">
+ <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block">
+ <div class="card install-card shadow w-100">
+ <div class="card-header">
+ Working with conda?
+ </div>
+ <div class="card-body">
+ <p class="card-text">
+
+Pandas is part of the `Anaconda <http://docs.continuum.io/anaconda/>`__ distribution and can be
+installed with Anaconda or Miniconda:
+
+.. raw:: html
+
+ </p>
+ </div>
+ <div class="card-footer text-muted">
+
+.. code-block:: bash
+
+ conda install pandas
+
+.. raw:: html
+
+ </div>
+ </div>
+ </div>
+ <div class="col-lg-6 col-md-6 col-sm-12 col-xs-12 d-flex install-block">
+ <div class="card install-card shadow w-100">
+ <div class="card-header">
+ Prefer pip?
+ </div>
+ <div class="card-body">
+ <p class="card-text">
+
+Pandas can be installed via pip from `PyPI <https://pypi.org/project/pandas>`__.
+
+.. raw:: html
+
+ </p>
+ </div>
+ <div class="card-footer text-muted">
+
+.. code-block:: bash
+
+ pip install pandas
+
+.. raw:: html
+
+ </div>
+ </div>
+ </div>
+ <div class="col-12 d-flex install-block">
+ <div class="card install-card shadow w-100">
+ <div class="card-header">
+ In-depth instructions?
+ </div>
+ <div class="card-body">
+ <p class="card-text">Installing a specific version?
+ Installing from source?
+ Check the advanced installation page.</p>
+
+.. container:: custom-button
+
+ :ref:`Learn more <install>`
+
+.. raw:: html
+
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+
+.. _gentle_intro:
+
+Intro to pandas
+---------------
+
+.. raw:: html
+
+ <div class="container">
+ <div id="accordion" class="shadow tutorial-accordion">
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseOne">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ What kind of data does Pandas handle?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_01_tableoriented>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseOne" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+When working with tabular data, such as data stored in spreadsheets or databases, Pandas is the right tool for you. Pandas will help you
+to explore, clean and process your data. In Pandas, a data table is called a :class:`DataFrame`.
+
+.. image:: ../_static/schemas/01_table_dataframe.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_01_tableoriented>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <dsintro>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseTwo">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How do I read and write tabular data?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_02_read_write>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseTwo" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these
+data sources is provided by function with the prefix ``read_*``. Similarly, the ``to_*`` methods are used to store data.
+
+.. image:: ../_static/schemas/02_io_readwrite.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_02_read_write>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <io>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseThree">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How do I select a subset of a table?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_03_subset>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseThree" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the
+data you need are available in Pandas.
+
+.. image:: ../_static/schemas/03_subset_columns_rows.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_03_subset>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <indexing>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseFour">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How to create plots in pandas?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_04_plotting>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseFour" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Pandas provides plotting your data out of the box, using the power of Matplotlib. You can pick the plot type (scatter, bar, boxplot,...)
+corresponding to your data.
+
+.. image:: ../_static/schemas/04_plot_overview.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_04_plotting>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <visualization>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseFive">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How to create new columns derived from existing columns?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_05_columns>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseFive" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise.
+Adding a column to a :class:`DataFrame` based on existing data in other columns is straightforward.
+
+.. image:: ../_static/schemas/05_newcolumn_2.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_05_columns>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <basics.dataframe.sel_add_del>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseSix">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How to calculate summary statistics?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_06_stats>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseSix" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Basic statistics (mean, median, min, max, counts...) are easily calculable. These or custom aggregations can be applied on the entire
+data set, a sliding window of the data or grouped by categories. The latter is also known as the split-apply-combine approach.
+
+.. image:: ../_static/schemas/06_groupby.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_06_stats>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <groupby>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseSeven">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How to reshape the layout of tables?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_07_reshape>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseSeven" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Change the structure of your data table in multiple ways. You can :func:`~pandas.melt` your data table from wide to long/tidy form or :func:`~pandas.pivot`
+from long to wide format. With aggregations built-in, a pivot table is created with a sinlge command.
+
+.. image:: ../_static/schemas/07_melt.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_07_reshape>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <reshaping>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseEight">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How to combine data from multiple tables?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_08_combine>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseEight" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Multiple tables can be concatenated both column wise as row wise and database-like join/merge operations are provided to combine multiple tables of data.
+
+.. image:: ../_static/schemas/08_concat_row.svg
+ :align: center
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_08_combine>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <merging>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseNine">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How to handle time series data?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_09_timeseries>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseNine" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data.
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_09_timeseries>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <timeseries>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ <div class="card tutorial-card">
+ <div class="card-header collapsed card-link" data-toggle="collapse" data-target="#collapseTen">
+ <div class="d-flex flex-row tutorial-card-header-1">
+ <div class="d-flex flex-row tutorial-card-header-2">
+ <button class="btn btn-dark btn-sm"></button>
+ How to manipulate textual data?
+ </div>
+ <span class="badge gs-badge-link">
+
+:ref:`Straight to tutorial...<10min_tut_10_text>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ <div id="collapseTen" class="collapse" data-parent="#accordion">
+ <div class="card-body">
+
+Data sets do not only contain numerical data. Pandas provides a wide range of functions to cleaning textual data and extract useful information from it.
+
+.. raw:: html
+
+ <div class="d-flex flex-row">
+ <span class="badge gs-badge-link">
+
+:ref:`To introduction tutorial <10min_tut_10_text>`
+
+.. raw:: html
+
+ </span>
+ <span class="badge gs-badge-link">
+
+:ref:`To user guide <timeseries>`
+
+.. raw:: html
+
+ </span>
+ </div>
+ </div>
+ </div>
+ </div>
+
+ </div>
+ </div>
+
+
+.. _comingfrom:
+
+Coming from...
+--------------
+
+Currently working with other software for data manipulation in a tabular format? You're probably familiar to typical
+data operations and know *what* to do with your tabular data, but lacking the syntax to execute these operations. Get to know
+the pandas syntax by looking for equivalents from the software you already know:
+
+.. raw:: html
+
+ <div class="container">
+ <div class="row">
+ <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
+ <div class="card text-center intro-card shadow">
+ <img src="../_static/logo_r.svg" class="card-img-top" alt="R project logo" height="72">
+ <div class="card-body flex-fill">
+ <p class="card-text">The <a href="https://www.r-project.org/">R programming language</a> provides the <code>data.frame</code> data structure and multiple packages,
+ such as <a href="https://www.tidyverse.org/">tidyverse</a> use and extend <code>data.frame</code>s for convenient data handling
+ functionalities similar to pandas.</p>
+
+.. container:: custom-button
+
+ :ref:`Learn more <compare_with_r>`
+
+.. raw:: html
+
+ </div>
+ </div>
+ </div>
+ <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
+ <div class="card text-center intro-card shadow">
+ <img src="../_static/logo_sql.svg" class="card-img-top" alt="SQL logo" height="72">
+ <div class="card-body flex-fill">
+ <p class="card-text">Already familiar to <code>SELECT</code>, <code>GROUP BY</code>, <code>JOIN</code>,...?
+ Most of these SQL manipulations do have equivalents in pandas.</p>
+
+.. container:: custom-button
+
+ :ref:`Learn more <compare_with_sql>`
+
+.. raw:: html
+
+ </div>
+ </div>
+ </div>
+ <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
+ <div class="card text-center intro-card shadow">
+ <img src="../_static/logo_stata.svg" class="card-img-top" alt="STATA logo" height="52">
+ <div class="card-body flex-fill">
+ <p class="card-text">The <code>data set</code> included in the
+ <a href="https://en.wikipedia.org/wiki/Stata">STATA</a> statistical software suite corresponds
+ to the pandas <code>data.frame</code>. Many of the operations known from STATA have an equivalent
+ in pandas.</p>
+
+.. container:: custom-button
+
+ :ref:`Learn more <compare_with_stata>`
+
+.. raw:: html
+
+ </div>
+ </div>
+ </div>
+ <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
+ <div class="card text-center intro-card shadow">
+ <img src="../_static/logo_sas.svg" class="card-img-top" alt="SAS logo" height="52">
+ <div class="card-body flex-fill">
+ <p class="card-text">The <a href="https://en.wikipedia.org/wiki/SAS_(software)">SAS</a> statistical software suite
+ also provides the <code>data set</code> corresponding to the pandas <code>data.frame</code>.
+ Also vectorized operations, filtering, string processing operations,... from SAS have similar
+ functions in pandas.</p>
+
+.. container:: custom-button
+
+ :ref:`Learn more <compare_with_sas>`
+
+.. raw:: html
+
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+
+Community tutorials
+-------------------
+
+The community produces a wide variety of tutorials available online. Some of the
+material is enlisted in the community contributed :ref:`tutorials`.
+
+
.. If you update this toctree, also update the manual toctree in the
main index.rst.template
.. toctree::
:maxdepth: 2
+ :hidden:
install
overview
10min
+ intro_tutorials/index
basics
dsintro
comparison/index
diff --git a/doc/source/getting_started/intro_tutorials/01_table_oriented.rst b/doc/source/getting_started/intro_tutorials/01_table_oriented.rst
new file mode 100644
index 0000000000000..02e59b3c81755
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/01_table_oriented.rst
@@ -0,0 +1,218 @@
+.. _10min_tut_01_tableoriented:
+
+{{ header }}
+
+What kind of data does pandas handle?
+=====================================
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to start using pandas
+
+.. ipython:: python
+
+ import pandas as pd
+
+To load the pandas package and start working with it, import the
+package. The community agreed alias for pandas is ``pd``, so loading
+pandas as ``pd`` is assumed standard practice for all of the pandas
+documentation.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Pandas data table representation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/01_table_dataframe.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to store passenger data of the Titanic. For a number of passengers, I know the name (characters), age (integers) and sex (male/female) data.
+
+.. ipython:: python
+
+ df = pd.DataFrame({
+ "Name": ["Braund, Mr. Owen Harris",
+ "Allen, Mr. William Henry",
+ "Bonnell, Miss. Elizabeth"],
+ "Age": [22, 35, 58],
+ "Sex": ["male", "male", "female"]}
+ )
+ df
+
+To manually store data in a table, create a ``DataFrame``. When using a Python dictionary of lists, the dictionary keys will be used as column headers and
+the values in each list as rows of the ``DataFrame``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+A :class:`DataFrame` is a 2-dimensional data structure that can store data of
+different types (including characters, integers, floating point values,
+categorical data and more) in columns. It is similar to a spreadsheet, a
+SQL table or the ``data.frame`` in R.
+
+- The table has 3 columns, each of them with a column label. The column
+ labels are respectively ``Name``, ``Age`` and ``Sex``.
+- The column ``Name`` consists of textual data with each value a
+ string, the column ``Age`` are numbers and the column ``Sex`` is
+ textual data.
+
+In spreadsheet software, the table representation of our data would look
+very similar:
+
+.. image:: ../../_static/schemas/01_table_spreadsheet.png
+ :align: center
+
+Each column in a ``DataFrame`` is a ``Series``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/01_table_series.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m just interested in working with the data in the column ``Age``
+
+.. ipython:: python
+
+ df["Age"]
+
+When selecting a single column of a pandas :class:`DataFrame`, the result is
+a pandas :class:`Series`. To select the column, use the column label in
+between square brackets ``[]``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. note::
+ If you are familiar to Python
+ :ref:`dictionaries <python:tut-dictionaries>`, the selection of a
+ single column is very similar to selection of dictionary values based on
+ the key.
+
+You can create a ``Series`` from scratch as well:
+
+.. ipython:: python
+
+ ages = pd.Series([22, 35, 58], name="Age")
+ ages
+
+A pandas ``Series`` has no column labels, as it is just a single column
+of a ``DataFrame``. A Series does have row labels.
+
+Do something with a DataFrame or Series
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to know the maximum Age of the passengers
+
+We can do this on the ``DataFrame`` by selecting the ``Age`` column and
+applying ``max()``:
+
+.. ipython:: python
+
+ df["Age"].max()
+
+Or to the ``Series``:
+
+.. ipython:: python
+
+ ages.max()
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+As illustrated by the ``max()`` method, you can *do* things with a
+``DataFrame`` or ``Series``. pandas provides a lot of functionalities,
+each of them a *method* you can apply to a ``DataFrame`` or ``Series``.
+As methods are functions, do not forget to use parentheses ``()``.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in some basic statistics of the numerical data of my data table
+
+.. ipython:: python
+
+ df.describe()
+
+The :func:`~DataFrame.describe` method provides a quick overview of the numerical data in
+a ``DataFrame``. As the ``Name`` and ``Sex`` columns are textual data,
+these are by default not taken into account by the :func:`~DataFrame.describe` method.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Many pandas operations return a ``DataFrame`` or a ``Series``. The
+:func:`~DataFrame.describe` method is an example of a pandas operation returning a
+pandas ``Series``.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+Check more options on ``describe`` in the user guide section about :ref:`aggregations with describe <basics.describe>`
+
+.. raw:: html
+
+ </div>
+
+.. note::
+ This is just a starting point. Similar to spreadsheet
+ software, pandas represents data as a table with columns and rows. Apart
+ from the representation, also the data manipulations and calculations
+ you would do in spreadsheet software are supported by pandas. Continue
+ reading the next tutorials to get started!
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- Import the package, aka ``import pandas as pd``
+- A table of data is stored as a pandas ``DataFrame``
+- Each column in a ``DataFrame`` is a ``Series``
+- You can do things by applying a method to a ``DataFrame`` or ``Series``
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A more extended explanation to ``DataFrame`` and ``Series`` is provided in the :ref:`introduction to data structures <dsintro>`.
+
+.. raw:: html
+
+ </div>
\ No newline at end of file
diff --git a/doc/source/getting_started/intro_tutorials/02_read_write.rst b/doc/source/getting_started/intro_tutorials/02_read_write.rst
new file mode 100644
index 0000000000000..797bdbcf25d17
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/02_read_write.rst
@@ -0,0 +1,232 @@
+.. _10min_tut_02_read_write:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Titanic data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+This tutorial uses the titanic data set, stored as CSV. The data
+consists of the following data columns:
+
+- PassengerId: Id of every passenger.
+- Survived: This feature have value 0 and 1. 0 for not survived and 1
+ for survived.
+- Pclass: There are 3 classes: Class 1, Class 2 and Class 3.
+- Name: Name of passenger.
+- Sex: Gender of passenger.
+- Age: Age of passenger.
+- SibSp: Indication that passenger have siblings and spouse.
+- Parch: Whether a passenger is alone or have family.
+- Ticket: Ticket number of passenger.
+- Fare: Indicating the fare.
+- Cabin: The cabin of passenger.
+- Embarked: The embarked category.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+ </li>
+ </ul>
+ </div>
+
+How do I read and write tabular data?
+=====================================
+
+.. image:: ../../_static/schemas/02_io_readwrite.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to analyse the titanic passenger data, available as a CSV file.
+
+.. ipython:: python
+
+ titanic = pd.read_csv("data/titanic.csv")
+
+pandas provides the :func:`read_csv` function to read data stored as a csv
+file into a pandas ``DataFrame``. pandas supports many different file
+formats or data sources out of the box (csv, excel, sql, json, parquet,
+…), each of them with the prefix ``read_*``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Make sure to always have a check on the data after reading in the
+data. When displaying a ``DataFrame``, the first and last 5 rows will be
+shown by default:
+
+.. ipython:: python
+
+ titanic
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to see the first 8 rows of a pandas DataFrame.
+
+.. ipython:: python
+
+ titanic.head(8)
+
+To see the first N rows of a ``DataFrame``, use the :meth:`~DataFrame.head` method with
+the required number of rows (in this case 8) as argument.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. note::
+
+ Interested in the last N rows instead? pandas also provides a
+ :meth:`~DataFrame.tail` method. For example, ``titanic.tail(10)`` will return the last
+ 10 rows of the DataFrame.
+
+A check on how pandas interpreted each of the column data types can be
+done by requesting the pandas ``dtypes`` attribute:
+
+.. ipython:: python
+
+ titanic.dtypes
+
+For each of the columns, the used data type is enlisted. The data types
+in this ``DataFrame`` are integers (``int64``), floats (``float63``) and
+strings (``object``).
+
+.. note::
+ When asking for the ``dtypes``, no brackets are used!
+ ``dtypes`` is an attribute of a ``DataFrame`` and ``Series``. Attributes
+ of ``DataFrame`` or ``Series`` do not need brackets. Attributes
+ represent a characteristic of a ``DataFrame``/``Series``, whereas a
+ method (which requires brackets) *do* something with the
+ ``DataFrame``/``Series`` as introduced in the :ref:`first tutorial <10min_tut_01_tableoriented>`.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+My colleague requested the titanic data as a spreadsheet.
+
+.. ipython:: python
+
+ titanic.to_excel('titanic.xlsx', sheet_name='passengers', index=False)
+
+Whereas ``read_*`` functions are used to read data to pandas, the
+``to_*`` methods are used to store data. The :meth:`~DataFrame.to_excel` method stores
+the data as an excel file. In the example here, the ``sheet_name`` is
+named *passengers* instead of the default *Sheet1*. By setting
+``index=False`` the row index labels are not saved in the spreadsheet.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The equivalent read function :meth:`~DataFrame.to_excel` will reload the data to a
+``DataFrame``:
+
+.. ipython:: python
+
+ titanic = pd.read_excel('titanic.xlsx', sheet_name='passengers')
+
+.. ipython:: python
+
+ titanic.head()
+
+.. ipython:: python
+ :suppress:
+
+ import os
+ os.remove('titanic.xlsx')
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in a technical summary of a ``DataFrame``
+
+.. ipython:: python
+
+ titanic.info()
+
+
+The method :meth:`~DataFrame.info` provides technical information about a
+``DataFrame``, so let’s explain the output in more detail:
+
+- It is indeed a :class:`DataFrame`.
+- There are 891 entries, i.e. 891 rows.
+- Each row has a row label (aka the ``index``) with values ranging from
+ 0 to 890.
+- The table has 12 columns. Most columns have a value for each of the
+ rows (all 891 values are ``non-null``). Some columns do have missing
+ values and less than 891 ``non-null`` values.
+- The columns ``Name``, ``Sex``, ``Cabin`` and ``Embarked`` consists of
+ textual data (strings, aka ``object``). The other columns are
+ numerical data with some of them whole numbers (aka ``integer``) and
+ others are real numbers (aka ``float``).
+- The kind of data (characters, integers,…) in the different columns
+ are summarized by listing the ``dtypes``.
+- The approximate amount of RAM used to hold the DataFrame is provided
+ as well.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- Getting data in to pandas from many different file formats or data
+ sources is supported by ``read_*`` functions.
+- Exporting data out of pandas is provided by different
+ ``to_*``\ methods.
+- The ``head``/``tail``/``info`` methods and the ``dtypes`` attribute
+ are convenient for a first check.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row bg-light gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+For a complete overview of the input and output possibilites from and to pandas, see the user guide section about :ref:`reader and writer functions <io>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/03_subset_data.rst b/doc/source/getting_started/intro_tutorials/03_subset_data.rst
new file mode 100644
index 0000000000000..7a4347905ad8d
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/03_subset_data.rst
@@ -0,0 +1,405 @@
+.. _10min_tut_03_subset:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Titanic data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+This tutorial uses the titanic data set, stored as CSV. The data
+consists of the following data columns:
+
+- PassengerId: Id of every passenger.
+- Survived: This feature have value 0 and 1. 0 for not survived and 1
+ for survived.
+- Pclass: There are 3 classes: Class 1, Class 2 and Class 3.
+- Name: Name of passenger.
+- Sex: Gender of passenger.
+- Age: Age of passenger.
+- SibSp: Indication that passenger have siblings and spouse.
+- Parch: Whether a passenger is alone or have family.
+- Ticket: Ticket number of passenger.
+- Fare: Indicating the fare.
+- Cabin: The cabin of passenger.
+- Embarked: The embarked category.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ titanic = pd.read_csv("data/titanic.csv")
+ titanic.head()
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+How do I select a subset of a ``DataFrame``?
+============================================
+
+How do I select specific columns from a ``DataFrame``?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/03_subset_columns.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in the age of the titanic passengers.
+
+.. ipython:: python
+
+ ages = titanic["Age"]
+ ages.head()
+
+To select a single column, use square brackets ``[]`` with the column
+name of the column of interest.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Each column in a :class:`DataFrame` is a :class:`Series`. As a single column is
+selected, the returned object is a pandas :class:`DataFrame`. We can verify this
+by checking the type of the output:
+
+.. ipython:: python
+
+ type(titanic["Age"])
+
+And have a look at the ``shape`` of the output:
+
+.. ipython:: python
+
+ titanic["Age"].shape
+
+:attr:`DataFrame.shape` is an attribute (remember :ref:`tutorial on reading and writing <10min_tut_02_read_write>`, do not use parantheses for attributes) of a
+pandas ``Series`` and ``DataFrame`` containing the number of rows and
+columns: *(nrows, ncolumns)*. A pandas Series is 1-dimensional and only
+the number of rows is returned.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in the age and sex of the titanic passengers.
+
+.. ipython:: python
+
+ age_sex = titanic[["Age", "Sex"]]
+ age_sex.head()
+
+To select multiple columns, use a list of column names within the
+selection brackets ``[]``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. note::
+ The inner square brackets define a
+ :ref:`Python list <python:tut-morelists>` with column names, whereas
+ the outer brackets are used to select the data from a pandas
+ ``DataFrame`` as seen in the previous example.
+
+The returned data type is a pandas DataFrame:
+
+.. ipython:: python
+
+ type(titanic[["Age", "Sex"]])
+
+.. ipython:: python
+
+ titanic[["Age", "Sex"]].shape
+
+The selection returned a ``DataFrame`` with 891 rows and 2 columns. Remember, a
+``DataFrame`` is 2-dimensional with both a row and column dimension.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+For basic information on indexing, see the user guide section on :ref:`indexing and selecting data <indexing.basics>`.
+
+.. raw:: html
+
+ </div>
+
+How do I filter specific rows from a ``DataFrame``?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/03_subset_rows.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in the passengers older than 35 years.
+
+.. ipython:: python
+
+ above_35 = titanic[titanic["Age"] > 35]
+ above_35.head()
+
+To select rows based on a conditional expression, use a condition inside
+the selection brackets ``[]``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The condition inside the selection
+brackets ``titanic["Age"] > 35`` checks for which rows the ``Age``
+column has a value larger than 35:
+
+.. ipython:: python
+
+ titanic["Age"] > 35
+
+The output of the conditional expression (``>``, but also ``==``,
+``!=``, ``<``, ``<=``,… would work) is actually a pandas ``Series`` of
+boolean values (either ``True`` or ``False``) with the same number of
+rows as the original ``DataFrame``. Such a ``Series`` of boolean values
+can be used to filter the ``DataFrame`` by putting it in between the
+selection brackets ``[]``. Only rows for which the value is ``True``
+will be selected.
+
+We now from before that the original titanic ``DataFrame`` consists of
+891 rows. Let’s have a look at the amount of rows which satisfy the
+condition by checking the ``shape`` attribute of the resulting
+``DataFrame`` ``above_35``:
+
+.. ipython:: python
+
+ above_35.shape
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in the titanic passengers from cabin class 2 and 3.
+
+.. ipython:: python
+
+ class_23 = titanic[titanic["Pclass"].isin([2, 3])]
+ class_23.head()
+
+Similar to the conditional expression, the :func:`~Series.isin` conditional function
+returns a ``True`` for each row the values are in the provided list. To
+filter the rows based on such a function, use the conditional function
+inside the selection brackets ``[]``. In this case, the condition inside
+the selection brackets ``titanic["Pclass"].isin([2, 3])`` checks for
+which rows the ``Pclass`` column is either 2 or 3.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The above is equivalent to filtering by rows for which the class is
+either 2 or 3 and combining the two statements with an ``|`` (or)
+operator:
+
+.. ipython:: python
+
+ class_23 = titanic[(titanic["Pclass"] == 2) | (titanic["Pclass"] == 3)]
+ class_23.head()
+
+.. note::
+ When combining multiple conditional statements, each condition
+ must be surrounded by parentheses ``()``. Moreover, you can not use
+ ``or``/``and`` but need to use the ``or`` operator ``|`` and the ``and``
+ operator ``&``.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+See the dedicated section in the user guide about :ref:`boolean indexing <indexing.boolean>` or about the :ref:`isin function <indexing.basics.indexing_isin>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to work with passenger data for which the age is known.
+
+.. ipython:: python
+
+ age_no_na = titanic[titanic["Age"].notna()]
+ age_no_na.head()
+
+The :meth:`~Series.notna` conditional function returns a ``True`` for each row the
+values are not an ``Null`` value. As such, this can be combined with the
+selection brackets ``[]`` to filter the data table.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+You might wonder what actually changed, as the first 5 lines are still
+the same values. One way to verify is to check if the shape has changed:
+
+.. ipython:: python
+
+ age_no_na.shape
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+For more dedicated functions on missing values, see the user guide section about :ref:`handling missing data <missing_data>`.
+
+.. raw:: html
+
+ </div>
+
+How do I select specific rows and columns from a ``DataFrame``?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/03_subset_columns_rows.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in the names of the passengers older than 35 years.
+
+.. ipython:: python
+
+ adult_names = titanic.loc[titanic["Age"] > 35, "Name"]
+ adult_names.head()
+
+In this case, a subset of both rows and columns is made in one go and
+just using selection brackets ``[]`` is not sufficient anymore. The
+``loc``/``iloc`` operators are required in front of the selection
+brackets ``[]``. When using ``loc``/``iloc``, the part before the comma
+is the rows you want, and the part after the comma is the columns you
+want to select.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+When using the column names, row labels or a condition expression, use
+the ``loc`` operator in front of the selection brackets ``[]``. For both
+the part before and after the comma, you can use a single label, a list
+of labels, a slice of labels, a conditional expression or a colon. Using
+a colon specificies you want to select all rows or columns.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I’m interested in rows 10 till 25 and columns 3 to 5.
+
+.. ipython:: python
+
+ titanic.iloc[9:25, 2:5]
+
+Again, a subset of both rows and columns is made in one go and just
+using selection brackets ``[]`` is not sufficient anymore. When
+specifically interested in certain rows and/or columns based on their
+position in the table, use the ``iloc`` operator in front of the
+selection brackets ``[]``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+When selecting specific rows and/or columns with ``loc`` or ``iloc``,
+new values can be assigned to the selected data. For example, to assign
+the name ``anonymous`` to the first 3 elements of the third column:
+
+.. ipython:: python
+
+ titanic.iloc[0:3, 3] = "anonymous"
+ titanic.head()
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+See the user guide section on :ref:`different choices for indexing <indexing.choice>` to get more insight in the usage of ``loc`` and ``iloc``.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- When selecting subsets of data, square brackets ``[]`` are used.
+- Inside these brackets, you can use a single column/row label, a list
+ of column/row labels, a slice of labels, a conditional expression or
+ a colon.
+- Select specific rows and/or columns using ``loc`` when using the row
+ and column names
+- Select specific rows and/or columns using ``iloc`` when using the
+ positions in the table
+- You can assign new values to a selection based on ``loc``/``iloc``.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A full overview about indexing is provided in the user guide pages on :ref:`indexing and selecting data <indexing>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/04_plotting.rst b/doc/source/getting_started/intro_tutorials/04_plotting.rst
new file mode 100644
index 0000000000000..f3d99ee56359a
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/04_plotting.rst
@@ -0,0 +1,252 @@
+.. _10min_tut_04_plotting:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+ import matplotlib.pyplot as plt
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Air quality data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+For this tutorial, air quality data about :math:`NO_2` is used, made
+available by `openaq <https://openaq.org>`__ and using the
+`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+The ``air_quality_no2.csv`` data set provides :math:`NO_2` values for
+the measurement stations *FR04014*, *BETR801* and *London Westminster*
+in respectively Paris, Antwerp and London.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ air_quality = pd.read_csv("data/air_quality_no2.csv",
+ index_col=0, parse_dates=True)
+ air_quality.head()
+
+.. note::
+ The usage of the ``index_col`` and ``parse_dates`` parameters of the ``read_csv`` function to define the first (0th) column as
+ index of the resulting ``DataFrame`` and convert the dates in the column to :class:`Timestamp` objects, respectively.
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+How to create plots in pandas?
+------------------------------
+
+.. image:: ../../_static/schemas/04_plot_overview.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want a quick visual check of the data.
+
+.. ipython:: python
+
+ @savefig 04_airqual_quick.png
+ air_quality.plot()
+
+With a ``DataFrame``, pandas creates by default one line plot for each of
+the columns with numeric data.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to plot only the columns of the data table with the data from Paris.
+
+.. ipython:: python
+
+ @savefig 04_airqual_paris.png
+ air_quality["station_paris"].plot()
+
+To plot a specific column, use the selection method of the
+:ref:`subset data tutorial <10min_tut_03_subset>` in combination with the :meth:`~DataFrame.plot`
+method. Hence, the :meth:`~DataFrame.plot` method works on both ``Series`` and
+``DataFrame``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to visually compare the :math:`N0_2` values measured in London versus Paris.
+
+.. ipython:: python
+
+ @savefig 04_airqual_scatter.png
+ air_quality.plot.scatter(x="station_london",
+ y="station_paris",
+ alpha=0.5)
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Apart from the default ``line`` plot when using the ``plot`` function, a
+number of alternatives are available to plot data. Let’s use some
+standard Python to get an overview of the available plot methods:
+
+.. ipython:: python
+
+ [method_name for method_name in dir(air_quality.plot)
+ if not method_name.startswith("_")]
+
+.. note::
+ In many development environments as well as ipython and
+ jupyter notebook, use the TAB button to get an overview of the available
+ methods, for example ``air_quality.plot.`` + TAB.
+
+One of the options is :meth:`DataFrame.plot.box`, which refers to a
+`boxplot <https://en.wikipedia.org/wiki/Box_plot>`__. The ``box``
+method is applicable on the air quality example data:
+
+.. ipython:: python
+
+ @savefig 04_airqual_boxplot.png
+ air_quality.plot.box()
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+For an introduction to plots other than the default line plot, see the user guide section about :ref:`supported plot styles <visualization.other>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want each of the columns in a separate subplot.
+
+.. ipython:: python
+
+ @savefig 04_airqual_area_subplot.png
+ axs = air_quality.plot.area(figsize=(12, 4), subplots=True)
+
+Separate subplots for each of the data columns is supported by the ``subplots`` argument
+of the ``plot`` functions. The builtin options available in each of the pandas plot
+functions that are worthwhile to have a look.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+Some more formatting options are explained in the user guide section on :ref:`plot formatting <visualization.formatting>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to further customize, extend or save the resulting plot.
+
+.. ipython:: python
+
+ fig, axs = plt.subplots(figsize=(12, 4));
+ air_quality.plot.area(ax=axs);
+ @savefig 04_airqual_customized.png
+ axs.set_ylabel("NO$_2$ concentration");
+ fig.savefig("no2_concentrations.png")
+
+.. ipython:: python
+ :suppress:
+
+ import os
+ os.remove('no2_concentrations.png')
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Each of the plot objects created by pandas are a
+`matplotlib <https://matplotlib.org/>`__ object. As Matplotlib provides
+plenty of options to customize plots, making the link between pandas and
+Matplotlib explicit enables all the power of matplotlib to the plot.
+This strategy is applied in the previous example:
+
+::
+
+ fig, axs = plt.subplots(figsize=(12, 4)) # Create an empty matplotlib Figure and Axes
+ air_quality.plot.area(ax=axs) # Use pandas to put the area plot on the prepared Figure/Axes
+ axs.set_ylabel("NO$_2$ concentration") # Do any matplotlib customization you like
+ fig.savefig("no2_concentrations.png") # Save the Figure/Axes using the existing matplotlib method.
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- The ``.plot.*`` methods are applicable on both Series and DataFrames
+- By default, each of the columns is plotted as a different element
+ (line, boxplot,…)
+- Any plot created by pandas is a Matplotlib object.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A full overview of plotting in pandas is provided in the :ref:`visualization pages <visualization>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/05_add_columns.rst b/doc/source/getting_started/intro_tutorials/05_add_columns.rst
new file mode 100644
index 0000000000000..d4f6a8d6bb4a2
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/05_add_columns.rst
@@ -0,0 +1,186 @@
+.. _10min_tut_05_columns:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Air quality data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+For this tutorial, air quality data about :math:`NO_2` is used, made
+available by `openaq <https://openaq.org>`__ and using the
+`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+The ``air_quality_no2.csv`` data set provides :math:`NO_2` values for
+the measurement stations *FR04014*, *BETR801* and *London Westminster*
+in respectively Paris, Antwerp and London.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ air_quality = pd.read_csv("data/air_quality_no2.csv",
+ index_col=0, parse_dates=True)
+ air_quality.head()
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+How to create new columns derived from existing columns?
+--------------------------------------------------------
+
+.. image:: ../../_static/schemas/05_newcolumn_1.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to express the :math:`NO_2` concentration of the station in London in mg/m\ :math:`^3`
+
+(*If we assume temperature of 25 degrees Celsius and pressure of 1013
+hPa, the conversion factor is 1.882*)
+
+.. ipython:: python
+
+ air_quality["london_mg_per_cubic"] = air_quality["station_london"] * 1.882
+ air_quality.head()
+
+To create a new column, use the ``[]`` brackets with the new column name
+at the left side of the assignment.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. note::
+ The calculation of the values is done **element_wise**. This
+ means all values in the given column are multiplied by the value 1.882
+ at once. You do not need to use a loop to iterate each of the rows!
+
+.. image:: ../../_static/schemas/05_newcolumn_2.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to check the ratio of the values in Paris versus Antwerp and save the result in a new column
+
+.. ipython:: python
+
+ air_quality["ratio_paris_antwerp"] = \
+ air_quality["station_paris"] / air_quality["station_antwerp"]
+ air_quality.head()
+
+The calculation is again element-wise, so the ``/`` is applied *for the
+values in each row*.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Also other mathematical operators (+, -, \*, /) or
+logical operators (<, >, =,…) work element wise. The latter was already
+used in the :ref:`subset data tutorial <10min_tut_03_subset>` to filter
+rows of a table using a conditional expression.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to rename the data columns to the corresponding station identifiers used by openAQ
+
+.. ipython:: python
+
+ air_quality_renamed = air_quality.rename(
+ columns={"station_antwerp": "BETR801",
+ "station_paris": "FR04014",
+ "station_london": "London Westminster"})
+
+.. ipython:: python
+
+ air_quality_renamed.head()
+
+The :meth:`~DataFrame.rename` function can be used for both row labels and column
+labels. Provide a dictionary with the keys the current names and the
+values the new names to update the corresponding names.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The mapping should not be restricted to fixed names only, but can be a
+mapping function as well. For example, converting the column names to
+lowercase letters can be done using a function as well:
+
+.. ipython:: python
+
+ air_quality_renamed = air_quality_renamed.rename(columns=str.lower)
+ air_quality_renamed.head()
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+Details about column or row label renaming is provided in the user guide section on :ref:`renaming labels <basics.rename>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- Create a new column by assigning the output to the DataFrame with a
+ new column name in between the ``[]``.
+- Operations are element-wise, no need to loop over rows.
+- Use ``rename`` with a dictionary or function to rename row labels or
+ column names.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+The user guide contains a separate section on :ref:`column addition and deletion <basics.dataframe.sel_add_del>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst b/doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst
new file mode 100644
index 0000000000000..7a94c90525027
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst
@@ -0,0 +1,310 @@
+.. _10min_tut_06_stats:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Titanic data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+This tutorial uses the titanic data set, stored as CSV. The data
+consists of the following data columns:
+
+- PassengerId: Id of every passenger.
+- Survived: This feature have value 0 and 1. 0 for not survived and 1
+ for survived.
+- Pclass: There are 3 classes: Class 1, Class 2 and Class 3.
+- Name: Name of passenger.
+- Sex: Gender of passenger.
+- Age: Age of passenger.
+- SibSp: Indication that passenger have siblings and spouse.
+- Parch: Whether a passenger is alone or have family.
+- Ticket: Ticket number of passenger.
+- Fare: Indicating the fare.
+- Cabin: The cabin of passenger.
+- Embarked: The embarked category.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ titanic = pd.read_csv("data/titanic.csv")
+ titanic.head()
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+How to calculate summary statistics?
+------------------------------------
+
+Aggregating statistics
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/06_aggregate.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+What is the average age of the titanic passengers?
+
+.. ipython:: python
+
+ titanic["Age"].mean()
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Different statistics are available and can be applied to columns with
+numerical data. Operations in general exclude missing data and operate
+across rows by default.
+
+.. image:: ../../_static/schemas/06_reduction.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+What is the median age and ticket fare price of the titanic passengers?
+
+.. ipython:: python
+
+ titanic[["Age", "Fare"]].median()
+
+The statistic applied to multiple columns of a ``DataFrame`` (the selection of two columns
+return a ``DataFrame``, see the :ref:`subset data tutorial <10min_tut_03_subset>`) is calculated for each numeric column.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The aggregating statistic can be calculated for multiple columns at the
+same time. Remember the ``describe`` function from :ref:`first tutorial <10min_tut_01_tableoriented>` tutorial?
+
+.. ipython:: python
+
+ titanic[["Age", "Fare"]].describe()
+
+Instead of the predefined statistics, specific combinations of
+aggregating statistics for given columns can be defined using the
+:func:`DataFrame.agg` method:
+
+.. ipython:: python
+
+ titanic.agg({'Age': ['min', 'max', 'median', 'skew'],
+ 'Fare': ['min', 'max', 'median', 'mean']})
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+Details about descriptive statistics are provided in the user guide section on :ref:`descriptive statistics <basics.stats>`.
+
+.. raw:: html
+
+ </div>
+
+
+Aggregating statistics grouped by category
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/06_groupby.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+What is the average age for male versus female titanic passengers?
+
+.. ipython:: python
+
+ titanic[["Sex", "Age"]].groupby("Sex").mean()
+
+As our interest is the average age for each gender, a subselection on
+these two columns is made first: ``titanic[["Sex", "Age"]]``. Next, the
+:meth:`~DataFrame.groupby` method is applied on the ``Sex`` column to make a group per
+category. The average age *for each gender* is calculated and
+returned.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Calculating a given statistic (e.g. ``mean`` age) *for each category in
+a column* (e.g. male/female in the ``Sex`` column) is a common pattern.
+The ``groupby`` method is used to support this type of operations. More
+general, this fits in the more general ``split-apply-combine`` pattern:
+
+- **Split** the data into groups
+- **Apply** a function to each group independently
+- **Combine** the results into a data structure
+
+The apply and combine steps are typically done together in pandas.
+
+In the previous example, we explicitly selected the 2 columns first. If
+not, the ``mean`` method is applied to each column containing numerical
+columns:
+
+.. ipython:: python
+
+ titanic.groupby("Sex").mean()
+
+It does not make much sense to get the average value of the ``Pclass``.
+if we are only interested in the average age for each gender, the
+selection of columns (rectangular brackets ``[]`` as usual) is supported
+on the grouped data as well:
+
+.. ipython:: python
+
+ titanic.groupby("Sex")["Age"].mean()
+
+.. image:: ../../_static/schemas/06_groupby_select_detail.svg
+ :align: center
+
+.. note::
+ The `Pclass` column contains numerical data but actually
+ represents 3 categories (or factors) with respectively the labels ‘1’,
+ ‘2’ and ‘3’. Calculating statistics on these does not make much sense.
+ Therefore, pandas provides a ``Categorical`` data type to handle this
+ type of data. More information is provided in the user guide
+ :ref:`categorical` section.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+What is the mean ticket fare price for each of the sex and cabin class combinations?
+
+.. ipython:: python
+
+ titanic.groupby(["Sex", "Pclass"])["Fare"].mean()
+
+Grouping can be done by multiple columns at the same time. Provide the
+column names as a list to the :meth:`~DataFrame.groupby` method.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A full description on the split-apply-combine approach is provided in the user guide section on :ref:`groupby operations <groupby>`.
+
+.. raw:: html
+
+ </div>
+
+Count number of records by category
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/06_valuecounts.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+What is the number of passengers in each of the cabin classes?
+
+.. ipython:: python
+
+ titanic["Pclass"].value_counts()
+
+The :meth:`~Series.value_counts` method counts the number of records for each
+category in a column.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The function is a shortcut, as it is actually a groupby operation in combination with counting of the number of records
+within each group:
+
+.. ipython:: python
+
+ titanic.groupby("Pclass")["Pclass"].count()
+
+.. note::
+ Both ``size`` and ``count`` can be used in combination with
+ ``groupby``. Whereas ``size`` includes ``NaN`` values and just provides
+ the number of rows (size of the table), ``count`` excludes the missing
+ values. In the ``value_counts`` method, use the ``dropna`` argument to
+ include or exclude the ``NaN`` values.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+The user guide has a dedicated section on ``value_counts`` , see page on :ref:`discretization <basics.discretization>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- Aggregation statistics can be calculated on entire columns or rows
+- ``groupby`` provides the power of the *split-apply-combine* pattern
+- ``value_counts`` is a convenient shortcut to count the number of
+ entries in each category of a variable
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A full description on the split-apply-combine approach is provided in the user guide pages about :ref:`groupby operations <groupby>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst b/doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst
new file mode 100644
index 0000000000000..b28a9012a4ad9
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst
@@ -0,0 +1,404 @@
+.. _10min_tut_07_reshape:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Titanic data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+This tutorial uses the titanic data set, stored as CSV. The data
+consists of the following data columns:
+
+- PassengerId: Id of every passenger.
+- Survived: This feature have value 0 and 1. 0 for not survived and 1
+ for survived.
+- Pclass: There are 3 classes: Class 1, Class 2 and Class 3.
+- Name: Name of passenger.
+- Sex: Gender of passenger.
+- Age: Age of passenger.
+- SibSp: Indication that passenger have siblings and spouse.
+- Parch: Whether a passenger is alone or have family.
+- Ticket: Ticket number of passenger.
+- Fare: Indicating the fare.
+- Cabin: The cabin of passenger.
+- Embarked: The embarked category.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ titanic = pd.read_csv("data/titanic.csv")
+ titanic.head()
+
+.. raw:: html
+
+ </li>
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata2" role="button" aria-expanded="false" aria-controls="collapsedata2">
+ <span class="badge badge-dark">Air quality data</span>
+ </div>
+ <div class="collapse" id="collapsedata2">
+ <div class="card-body">
+ <p class="card-text">
+
+This tutorial uses air quality data about :math:`NO_2` and Particulate matter less than 2.5
+micrometers, made available by
+`openaq <https://openaq.org>`__ and using the
+`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+The ``air_quality_long.csv`` data set provides :math:`NO_2` and
+:math:`PM_{25}` values for the measurement stations *FR04014*, *BETR801*
+and *London Westminster* in respectively Paris, Antwerp and London.
+
+The air-quality data set has the following columns:
+
+- city: city where the sensor is used, either Paris, Antwerp or London
+- country: country where the sensor is used, either FR, BE or GB
+- location: the id of the sensor, either *FR04014*, *BETR801* or
+ *London Westminster*
+- parameter: the parameter measured by the sensor, either :math:`NO_2`
+ or Particulate matter
+- value: the measured value
+- unit: the unit of the measured parameter, in this case ‘µg/m³’
+
+and the index of the ``DataFrame`` is ``datetime``, the datetime of the
+measurement.
+
+.. note::
+ The air-quality data is provided in a so-called *long format*
+ data representation with each observation on a separate row and each
+ variable a separate column of the data table. The long/narrow format is
+ also known as the `tidy data
+ format <https://www.jstatsoft.org/article/view/v059i10>`__.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_long.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ air_quality = pd.read_csv("data/air_quality_long.csv",
+ index_col="date.utc", parse_dates=True)
+ air_quality.head()
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+How to reshape the layout of tables?
+------------------------------------
+
+Sort table rows
+~~~~~~~~~~~~~~~
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to sort the titanic data according to the age of the passengers.
+
+.. ipython:: python
+
+ titanic.sort_values(by="Age").head()
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to sort the titanic data according to the cabin class and age in descending order.
+
+.. ipython:: python
+
+ titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head()
+
+With :meth:`Series.sort_values`, the rows in the table are sorted according to the
+defined column(s). The index will follow the row order.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+More details about sorting of tables is provided in the using guide section on :ref:`sorting data <basics.sorting>`.
+
+.. raw:: html
+
+ </div>
+
+Long to wide table format
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Let’s use a small subset of the air quality data set. We focus on
+:math:`NO_2` data and only use the first two measurements of each
+location (i.e. the head of each group). The subset of data will be
+called ``no2_subset``
+
+.. ipython:: python
+
+ # filter for no2 data only
+ no2 = air_quality[air_quality["parameter"] == "no2"]
+
+.. ipython:: python
+
+ # use 2 measurements (head) for each location (groupby)
+ no2_subset = no2.sort_index().groupby(["location"]).head(2)
+ no2_subset
+
+.. image:: ../../_static/schemas/07_pivot.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want the values for the three stations as separate columns next to each other
+
+.. ipython:: python
+
+ no2_subset.pivot(columns="location", values="value")
+
+The :meth:`~pandas.pivot_table` function is purely reshaping of the data: a single value
+for each index/column combination is required.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+As pandas support plotting of multiple columns (see :ref:`plotting tutorial <10min_tut_04_plotting>`) out of the box, the conversion from
+*long* to *wide* table format enables the plotting of the different time
+series at the same time:
+
+.. ipython:: python
+
+ no2.head()
+
+.. ipython:: python
+
+ @savefig 7_reshape_columns.png
+ no2.pivot(columns="location", values="value").plot()
+
+.. note::
+ When the ``index`` parameter is not defined, the existing
+ index (row labels) is used.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+For more information about :meth:`~DataFrame.pivot`, see the user guide section on :ref:`pivoting DataFrame objects <reshaping.reshaping>`.
+
+.. raw:: html
+
+ </div>
+
+Pivot table
+~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/07_pivot_table.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want the mean concentrations for :math:`NO_2` and :math:`PM_{2.5}` in each of the stations in table form
+
+.. ipython:: python
+
+ air_quality.pivot_table(values="value", index="location",
+ columns="parameter", aggfunc="mean")
+
+In the case of :meth:`~DataFrame.pivot`, the data is only rearranged. When multiple
+values need to be aggregated (in this specific case, the values on
+different time steps) :meth:`~DataFrame.pivot_table` can be used, providing an
+aggregation function (e.g. mean) on how to combine these values.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Pivot table is a well known concept in spreadsheet software. When
+interested in summary columns for each variable separately as well, put
+the ``margin`` parameter to ``True``:
+
+.. ipython:: python
+
+ air_quality.pivot_table(values="value", index="location",
+ columns="parameter", aggfunc="mean",
+ margins=True)
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+For more information about :meth:`~DataFrame.pivot_table`, see the user guide section on :ref:`pivot tables <reshaping.pivot>`.
+
+.. raw:: html
+
+ </div>
+
+.. note::
+ If case you are wondering, :meth:`~DataFrame.pivot_table` is indeed directly linked
+ to :meth:`~DataFrame.groupby`. The same result can be derived by grouping on both
+ ``parameter`` and ``location``:
+
+ ::
+
+ air_quality.groupby(["parameter", "location"]).mean()
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+Have a look at :meth:`~DataFrame.groupby` in combination with :meth:`~DataFrame.unstack` at the user guide section on :ref:`combining stats and groupby <reshaping.combine_with_groupby>`.
+
+.. raw:: html
+
+ </div>
+
+Wide to long format
+~~~~~~~~~~~~~~~~~~~
+
+Starting again from the wide format table created in the previous
+section:
+
+.. ipython:: python
+
+ no2_pivoted = no2.pivot(columns="location", values="value").reset_index()
+ no2_pivoted.head()
+
+.. image:: ../../_static/schemas/07_melt.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to collect all air quality :math:`NO_2` measurements in a single column (long format)
+
+.. ipython:: python
+
+ no_2 = no2_pivoted.melt(id_vars="date.utc")
+ no_2.head()
+
+The :func:`pandas.melt` method on a ``DataFrame`` converts the data table from wide
+format to long format. The column headers become the variable names in a
+newly created column.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The solution is the short version on how to apply :func:`pandas.melt`. The method
+will *melt* all columns NOT mentioned in ``id_vars`` together into two
+columns: A columns with the column header names and a column with the
+values itself. The latter column gets by default the name ``value``.
+
+The :func:`pandas.melt` method can be defined in more detail:
+
+.. ipython:: python
+
+ no_2 = no2_pivoted.melt(id_vars="date.utc",
+ value_vars=["BETR801",
+ "FR04014",
+ "London Westminster"],
+ value_name="NO_2",
+ var_name="id_location")
+ no_2.head()
+
+The result in the same, but in more detail defined:
+
+- ``value_vars`` defines explicitly which columns to *melt* together
+- ``value_name`` provides a custom column name for the values column
+ instead of the default columns name ``value``
+- ``var_name`` provides a custom column name for the columns collecting
+ the column header names. Otherwise it takes the index name or a
+ default ``variable``
+
+Hence, the arguments ``value_name`` and ``var_name`` are just
+user-defined names for the two generated columns. The columns to melt
+are defined by ``id_vars`` and ``value_vars``.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+Conversion from wide to long format with :func:`pandas.melt` is explained in the user guide section on :ref:`reshaping by melt <reshaping.melt>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- Sorting by one or more columns is supported by ``sort_values``
+- The ``pivot`` function is purely restructering of the data,
+ ``pivot_table`` supports aggregations
+- The reverse of ``pivot`` (long to wide format) is ``melt`` (wide to
+ long format)
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A full overview is available in the user guide on the pages about :ref:`reshaping and pivoting <reshaping>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst b/doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst
new file mode 100644
index 0000000000000..f317e7a1f91b4
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst
@@ -0,0 +1,326 @@
+.. _10min_tut_08_combine:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Air quality Nitrate data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+For this tutorial, air quality data about :math:`NO_2` is used, made available by
+`openaq <https://openaq.org>`__ and downloaded using the
+`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+
+The ``air_quality_no2_long.csv`` data set provides :math:`NO_2`
+values for the measurement stations *FR04014*, *BETR801* and *London
+Westminster* in respectively Paris, Antwerp and London.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2_long.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ air_quality_no2 = pd.read_csv("data/air_quality_no2_long.csv",
+ parse_dates=True)
+ air_quality_no2 = air_quality_no2[["date.utc", "location",
+ "parameter", "value"]]
+ air_quality_no2.head()
+
+.. raw:: html
+
+ </li>
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata2" role="button" aria-expanded="false" aria-controls="collapsedata2">
+ <span class="badge badge-dark">Air quality Particulate matter data</span>
+ </div>
+ <div class="collapse" id="collapsedata2">
+ <div class="card-body">
+ <p class="card-text">
+
+For this tutorial, air quality data about Particulate
+matter less than 2.5 micrometers is used, made available by
+`openaq <https://openaq.org>`__ and downloaded using the
+`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+
+The ``air_quality_pm25_long.csv`` data set provides :math:`PM_{25}`
+values for the measurement stations *FR04014*, *BETR801* and *London
+Westminster* in respectively Paris, Antwerp and London.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_pm25_long.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ air_quality_pm25 = pd.read_csv("data/air_quality_pm25_long.csv",
+ parse_dates=True)
+ air_quality_pm25 = air_quality_pm25[["date.utc", "location",
+ "parameter", "value"]]
+ air_quality_pm25.head()
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+
+How to combine data from multiple tables?
+-----------------------------------------
+
+Concatenating objects
+~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/08_concat_row.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to combine the measurements of :math:`NO_2` and :math:`PM_{25}`, two tables with a similar structure, in a single table
+
+.. ipython:: python
+
+ air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis=0)
+ air_quality.head()
+
+The :func:`~pandas.concat` function performs concatenation operations of multiple
+tables along one of the axis (row-wise or column-wise).
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+By default concatenation is along axis 0, so the resulting table combines the rows
+of the input tables. Let’s check the shape of the original and the
+concatenated tables to verify the operation:
+
+.. ipython:: python
+
+ print('Shape of the `air_quality_pm25` table: ', air_quality_pm25.shape)
+ print('Shape of the `air_quality_no2` table: ', air_quality_no2.shape)
+ print('Shape of the resulting `air_quality` table: ', air_quality.shape)
+
+Hence, the resulting table has 3178 = 1110 + 2068 rows.
+
+.. note::
+ The **axis** argument will return in a number of pandas
+ methods that can be applied **along an axis**. A ``DataFrame`` has two
+ corresponding axes: the first running vertically downwards across rows
+ (axis 0), and the second running horizontally across columns (axis 1).
+ Most operations like concatenation or summary statistics are by default
+ across rows (axis 0), but can be applied across columns as well.
+
+Sorting the table on the datetime information illustrates also the
+combination of both tables, with the ``parameter`` column defining the
+origin of the table (either ``no2`` from table ``air_quality_no2`` or
+``pm25`` from table ``air_quality_pm25``):
+
+.. ipython:: python
+
+ air_quality = air_quality.sort_values("date.utc")
+ air_quality.head()
+
+In this specific example, the ``parameter`` column provided by the data
+ensures that each of the original tables can be identified. This is not
+always the case. the ``concat`` function provides a convenient solution
+with the ``keys`` argument, adding an additional (hierarchical) row
+index. For example:
+
+.. ipython:: python
+
+ air_quality_ = pd.concat([air_quality_pm25, air_quality_no2],
+ keys=["PM25", "NO2"])
+
+.. ipython:: python
+
+ air_quality_.head()
+
+.. note::
+ The existence of multiple row/column indices at the same time
+ has not been mentioned within these tutorials. *Hierarchical indexing*
+ or *MultiIndex* is an advanced and powerfull pandas feature to analyze
+ higher dimensional data.
+
+ Multi-indexing is out of scope for this pandas introduction. For the
+ moment, remember that the function ``reset_index`` can be used to
+ convert any level of an index to a column, e.g.
+ ``air_quality.reset_index(level=0)``
+
+ .. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+ Feel free to dive into the world of multi-indexing at the user guide section on :ref:`advanced indexing <advanced>`.
+
+ .. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+More options on table concatenation (row and column
+wise) and how ``concat`` can be used to define the logic (union or
+intersection) of the indexes on the other axes is provided at the section on
+:ref:`object concatenation <merging.concat>`.
+
+.. raw:: html
+
+ </div>
+
+Join tables using a common identifier
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/schemas/08_merge_left.svg
+ :align: center
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Add the station coordinates, provided by the stations metadata table, to the corresponding rows in the measurements table.
+
+.. warning::
+ The air quality measurement station coordinates are stored in a data
+ file ``air_quality_stations.csv``, downloaded using the
+ `py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+
+.. ipython:: python
+
+ stations_coord = pd.read_csv("data/air_quality_stations.csv")
+ stations_coord.head()
+
+.. note::
+ The stations used in this example (FR04014, BETR801 and London
+ Westminster) are just three entries enlisted in the metadata table. We
+ only want to add the coordinates of these three to the measurements
+ table, each on the corresponding rows of the ``air_quality`` table.
+
+.. ipython:: python
+
+ air_quality.head()
+
+.. ipython:: python
+
+ air_quality = pd.merge(air_quality, stations_coord,
+ how='left', on='location')
+ air_quality.head()
+
+Using the :meth:`~pandas.merge` function, for each of the rows in the
+``air_quality`` table, the corresponding coordinates are added from the
+``air_quality_stations_coord`` table. Both tables have the column
+``location`` in common which is used as a key to combine the
+information. By choosing the ``left`` join, only the locations available
+in the ``air_quality`` (left) table, i.e. FR04014, BETR801 and London
+Westminster, end up in the resulting table. The ``merge`` function
+supports multiple join options similar to database-style operations.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Add the parameter full description and name, provided by the parameters metadata table, to the measurements table
+
+.. warning::
+ The air quality parameters metadata are stored in a data file
+ ``air_quality_parameters.csv``, downloaded using the
+ `py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+
+.. ipython:: python
+
+ air_quality_parameters = pd.read_csv("data/air_quality_parameters.csv")
+ air_quality_parameters.head()
+
+.. ipython:: python
+
+ air_quality = pd.merge(air_quality, air_quality_parameters,
+ how='left', left_on='parameter', right_on='id')
+ air_quality.head()
+
+Compared to the previous example, there is no common column name.
+However, the ``parameter`` column in the ``air_quality`` table and the
+``id`` column in the ``air_quality_parameters_name`` both provide the
+measured variable in a common format. The ``left_on`` and ``right_on``
+arguments are used here (instead of just ``on``) to make the link
+between the two tables.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+pandas supports also inner, outer, and right joins.
+More information on join/merge of tables is provided in the user guide section on
+:ref:`database style merging of tables <merging.join>`. Or have a look at the
+:ref:`comparison with SQL<compare_with_sql.join>` page.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- Multiple tables can be concatenated both column as row wise using
+ the ``concat`` function.
+- For database-like merging/joining of tables, use the ``merge``
+ function.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+See the user guide for a full description of the various :ref:`facilities to combine data tables <merging>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/09_timeseries.rst b/doc/source/getting_started/intro_tutorials/09_timeseries.rst
new file mode 100644
index 0000000000000..d5b4b316130bb
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/09_timeseries.rst
@@ -0,0 +1,389 @@
+.. _10min_tut_09_timeseries:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+ import matplotlib.pyplot as plt
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Air quality data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+For this tutorial, air quality data about :math:`NO_2` and Particulate
+matter less than 2.5 micrometers is used, made available by
+`openaq <https://openaq.org>`__ and downloaded using the
+`py-openaq <http://dhhagan.github.io/py-openaq/index.html>`__ package.
+The ``air_quality_no2_long.csv"`` data set provides :math:`NO_2` values
+for the measurement stations *FR04014*, *BETR801* and *London
+Westminster* in respectively Paris, Antwerp and London.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/air_quality_no2_long.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ air_quality = pd.read_csv("data/air_quality_no2_long.csv")
+ air_quality = air_quality.rename(columns={"date.utc": "datetime"})
+ air_quality.head()
+
+.. ipython:: python
+
+ air_quality.city.unique()
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+How to handle time series data with ease?
+-----------------------------------------
+
+Using pandas datetime properties
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to work with the dates in the column ``datetime`` as datetime objects instead of plain text
+
+.. ipython:: python
+
+ air_quality["datetime"] = pd.to_datetime(air_quality["datetime"])
+ air_quality["datetime"]
+
+Initially, the values in ``datetime`` are character strings and do not
+provide any datetime operations (e.g. extract the year, day of the
+week,…). By applying the ``to_datetime`` function, pandas interprets the
+strings and convert these to datetime (i.e. ``datetime64[ns, UTC]``)
+objects. In pandas we call these datetime objects similar to
+``datetime.datetime`` from the standard library a :class:`pandas.Timestamp`.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. note::
+ As many data sets do contain datetime information in one of
+ the columns, pandas input function like :func:`pandas.read_csv` and :func:`pandas.read_json`
+ can do the transformation to dates when reading the data using the
+ ``parse_dates`` parameter with a list of the columns to read as
+ Timestamp:
+
+ ::
+
+ pd.read_csv("../data/air_quality_no2_long.csv", parse_dates=["datetime"])
+
+Why are these :class:`pandas.Timestamp` objects useful. Let’s illustrate the added
+value with some example cases.
+
+ What is the start and end date of the time series data set working
+ with?
+
+.. ipython:: python
+
+ air_quality["datetime"].min(), air_quality["datetime"].max()
+
+Using :class:`pandas.Timestamp` for datetimes enable us to calculate with date
+information and make them comparable. Hence, we can use this to get the
+length of our time series:
+
+.. ipython:: python
+
+ air_quality["datetime"].max() - air_quality["datetime"].min()
+
+The result is a :class:`pandas.Timedelta` object, similar to ``datetime.timedelta``
+from the standard Python library and defining a time duration.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+The different time concepts supported by pandas are explained in the user guide section on :ref:`time related concepts <timeseries.overview>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+I want to add a new column to the ``DataFrame`` containing only the month of the measurement
+
+.. ipython:: python
+
+ air_quality["month"] = air_quality["datetime"].dt.month
+ air_quality.head()
+
+By using ``Timestamp`` objects for dates, a lot of time-related
+properties are provided by pandas. For example the ``month``, but also
+``year``, ``weekofyear``, ``quarter``,… All of these properties are
+accessible by the ``dt`` accessor.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+An overview of the existing date properties is given in the
+:ref:`time and date components overview table <timeseries.components>`. More details about the ``dt`` accessor
+to return datetime like properties is explained in a dedicated section on the :ref:`dt accessor <basics.dt_accessors>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+What is the average :math:`NO_2` concentration for each day of the week for each of the measurement locations?
+
+.. ipython:: python
+
+ air_quality.groupby(
+ [air_quality["datetime"].dt.weekday, "location"])["value"].mean()
+
+Remember the split-apply-combine pattern provided by ``groupby`` from the
+:ref:`tutorial on statistics calculation <10min_tut_06_stats>`?
+Here, we want to calculate a given statistic (e.g. mean :math:`NO_2`)
+**for each weekday** and **for each measurement location**. To group on
+weekdays, we use the datetime property ``weekday`` (with Monday=0 and
+Sunday=6) of pandas ``Timestamp``, which is also accessible by the
+``dt`` accessor. The grouping on both locations and weekdays can be done
+to split the calculation of the mean on each of these combinations.
+
+.. danger::
+ As we are working with a very short time series in these
+ examples, the analysis does not provide a long-term representative
+ result!
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Plot the typical :math:`NO_2` pattern during the day of our time series of all stations together. In other words, what is the average value for each hour of the day?
+
+.. ipython:: python
+
+ fig, axs = plt.subplots(figsize=(12, 4))
+ air_quality.groupby(
+ air_quality["datetime"].dt.hour)["value"].mean().plot(kind='bar',
+ rot=0,
+ ax=axs)
+ plt.xlabel("Hour of the day"); # custom x label using matplotlib
+ @savefig 09_bar_chart.png
+ plt.ylabel("$NO_2 (µg/m^3)$");
+
+Similar to the previous case, we want to calculate a given statistic
+(e.g. mean :math:`NO_2`) **for each hour of the day** and we can use the
+split-apply-combine approach again. For this case, the datetime property ``hour``
+of pandas ``Timestamp``, which is also accessible by the ``dt`` accessor.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Datetime as index
+~~~~~~~~~~~~~~~~~
+
+In the :ref:`tutorial on reshaping <10min_tut_07_reshape>`,
+:meth:`~pandas.pivot` was introduced to reshape the data table with each of the
+measurements locations as a separate column:
+
+.. ipython:: python
+
+ no_2 = air_quality.pivot(index="datetime", columns="location", values="value")
+ no_2.head()
+
+.. note::
+ By pivoting the data, the datetime information became the
+ index of the table. In general, setting a column as an index can be
+ achieved by the ``set_index`` function.
+
+Working with a datetime index (i.e. ``DatetimeIndex``) provides powerful
+functionalities. For example, we do not need the ``dt`` accessor to get
+the time series properties, but have these properties available on the
+index directly:
+
+.. ipython:: python
+
+ no_2.index.year, no_2.index.weekday
+
+Some other advantages are the convenient subsetting of time period or
+the adapted time scale on plots. Let’s apply this on our data.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Create a plot of the :math:`NO_2` values in the different stations from the 20th of May till the end of 21st of May
+
+.. ipython:: python
+ :okwarning:
+
+ @savefig 09_time_section.png
+ no_2["2019-05-20":"2019-05-21"].plot();
+
+By providing a **string that parses to a datetime**, a specific subset of the data can be selected on a ``DatetimeIndex``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+More information on the ``DatetimeIndex`` and the slicing by using strings is provided in the section on :ref:`time series indexing <timeseries.datetimeindex>`.
+
+.. raw:: html
+
+ </div>
+
+Resample a time series to another frequency
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Aggregate the current hourly time series values to the monthly maximum value in each of the stations.
+
+.. ipython:: python
+
+ monthly_max = no_2.resample("M").max()
+ monthly_max
+
+A very powerful method on time series data with a datetime index, is the
+ability to :meth:`~Series.resample` time series to another frequency (e.g.,
+converting secondly data into 5-minutely data).
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+The :meth:`~Series.resample` method is similar to a groupby operation:
+
+- it provides a time-based grouping, by using a string (e.g. ``M``,
+ ``5H``,…) that defines the target frequency
+- it requires an aggregation function such as ``mean``, ``max``,…
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+An overview of the aliases used to define time series frequencies is given in the :ref:`offset aliases overview table <timeseries.offset_aliases>`.
+
+.. raw:: html
+
+ </div>
+
+When defined, the frequency of the time series is provided by the
+``freq`` attribute:
+
+.. ipython:: python
+
+ monthly_max.index.freq
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Make a plot of the daily median :math:`NO_2` value in each of the stations.
+
+.. ipython:: python
+ :okwarning:
+
+ @savefig 09_resample_mean.png
+ no_2.resample("D").mean().plot(style="-o", figsize=(10, 5));
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+More details on the power of time series ``resampling`` is provided in the user gudie section on :ref:`resampling <timeseries.resampling>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- Valid date strings can be converted to datetime objects using
+ ``to_datetime`` function or as part of read functions.
+- Datetime objects in pandas supports calculations, logical operations
+ and convenient date-related properties using the ``dt`` accessor.
+- A ``DatetimeIndex`` contains these date-related properties and
+ supports convenient slicing.
+- ``Resample`` is a powerful method to change the frequency of a time
+ series.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A full overview on time series is given in the pages on :ref:`time series and date functionality <timeseries>`.
+
+.. raw:: html
+
+ </div>
\ No newline at end of file
diff --git a/doc/source/getting_started/intro_tutorials/10_text_data.rst b/doc/source/getting_started/intro_tutorials/10_text_data.rst
new file mode 100644
index 0000000000000..3ff64875d807b
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/10_text_data.rst
@@ -0,0 +1,278 @@
+.. _10min_tut_10_text:
+
+{{ header }}
+
+.. ipython:: python
+
+ import pandas as pd
+
+.. raw:: html
+
+ <div class="card gs-data">
+ <div class="card-header">
+ <div class="gs-data-title">
+ Data used for this tutorial:
+ </div>
+ </div>
+ <ul class="list-group list-group-flush">
+ <li class="list-group-item">
+ <div data-toggle="collapse" href="#collapsedata" role="button" aria-expanded="false" aria-controls="collapsedata">
+ <span class="badge badge-dark">Titanic data</span>
+ </div>
+ <div class="collapse" id="collapsedata">
+ <div class="card-body">
+ <p class="card-text">
+
+This tutorial uses the titanic data set, stored as CSV. The data
+consists of the following data columns:
+
+- PassengerId: Id of every passenger.
+- Survived: This feature have value 0 and 1. 0 for not survived and 1
+ for survived.
+- Pclass: There are 3 classes: Class 1, Class 2 and Class 3.
+- Name: Name of passenger.
+- Sex: Gender of passenger.
+- Age: Age of passenger.
+- SibSp: Indication that passenger have siblings and spouse.
+- Parch: Whether a passenger is alone or have family.
+- Ticket: Ticket number of passenger.
+- Fare: Indicating the fare.
+- Cabin: The cabin of passenger.
+- Embarked: The embarked category.
+
+.. raw:: html
+
+ </p>
+ <a href="https://github.com/pandas-dev/pandas/tree/master/doc/data/titanic.csv" class="btn btn-dark btn-sm">To raw data</a>
+ </div>
+ </div>
+
+.. ipython:: python
+
+ titanic = pd.read_csv("data/titanic.csv")
+ titanic.head()
+
+.. raw:: html
+
+ </li>
+ </ul>
+ </div>
+
+How to manipulate textual data?
+-------------------------------
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Make all name characters lowercase
+
+.. ipython:: python
+
+ titanic["Name"].str.lower()
+
+To make each of the strings in the ``Name`` column lowercase, select the ``Name`` column
+(see :ref:`tutorial on selection of data <10min_tut_03_subset>`), add the ``str`` accessor and
+apply the ``lower`` method. As such, each of the strings is converted element wise.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+Similar to datetime objects in the :ref:`time series tutorial <10min_tut_09_timeseries>`
+having a ``dt`` accessor, a number of
+specialized string methods are available when using the ``str``
+accessor. These methods have in general matching names with the
+equivalent built-in string methods for single elements, but are applied
+element-wise (remember :ref:`element wise calculations <10min_tut_05_columns>`?)
+on each of the values of the columns.
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Create a new column ``Surname`` that contains the surname of the Passengers by extracting the part before the comma.
+
+.. ipython:: python
+
+ titanic["Name"].str.split(",")
+
+Using the :meth:`Series.str.split` method, each of the values is returned as a list of
+2 elements. The first element is the part before the comma and the
+second element the part after the comma.
+
+.. ipython:: python
+
+ titanic["Surname"] = titanic["Name"].str.split(",").str.get(0)
+ titanic["Surname"]
+
+As we are only interested in the first part representing the surname
+(element 0), we can again use the ``str`` accessor and apply :meth:`Series.str.get` to
+extract the relevant part. Indeed, these string functions can be
+concatenated to combine multiple functions at once!
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+More information on extracting parts of strings is available in the user guide section on :ref:`splitting and replacing strings <text.split>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Extract the passenger data about the Countess on board of the Titanic.
+
+.. ipython:: python
+
+ titanic["Name"].str.contains("Countess")
+
+.. ipython:: python
+
+ titanic[titanic["Name"].str.contains("Countess")]
+
+(*Interested in her story? See*\ `Wikipedia <https://en.wikipedia.org/wiki/No%C3%ABl_Leslie,_Countess_of_Rothes>`__\ *!*)
+
+The string method :meth:`Series.str.contains` checks for each of the values in the
+column ``Name`` if the string contains the word ``Countess`` and returns
+for each of the values ``True`` (``Countess`` is part of the name) of
+``False`` (``Countess`` is notpart of the name). This output can be used
+to subselect the data using conditional (boolean) indexing introduced in
+the :ref:`subsetting of data tutorial <10min_tut_03_subset>`. As there was
+only 1 Countess on the Titanic, we get one row as a result.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. note::
+ More powerful extractions on strings is supported, as the
+ :meth:`Series.str.contains` and :meth:`Series.str.extract` methods accepts `regular
+ expressions <https://docs.python.org/3/library/re.html>`__, but out of
+ scope of this tutorial.
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+More information on extracting parts of strings is available in the user guide section on :ref:`string matching and extracting <text.extract>`.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+Which passenger of the titanic has the longest name?
+
+.. ipython:: python
+
+ titanic["Name"].str.len()
+
+To get the longest name we first have to get the lenghts of each of the
+names in the ``Name`` column. By using pandas string methods, the
+:meth:`Series.str.len` function is applied to each of the names individually
+(element-wise).
+
+.. ipython:: python
+
+ titanic["Name"].str.len().idxmax()
+
+Next, we need to get the corresponding location, preferably the index
+label, in the table for which the name length is the largest. The
+:meth:`~Series.idxmax`` method does exactly that. It is not a string method and is
+applied to integers, so no ``str`` is used.
+
+.. ipython:: python
+
+ titanic.loc[titanic["Name"].str.len().idxmax(), "Name"]
+
+Based on the index name of the row (``307``) and the column (``Name``),
+we can do a selection using the ``loc`` operator, introduced in the
+`tutorial on subsetting <3_subset_data.ipynb>`__.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. raw:: html
+
+ <ul class="task-bullet">
+ <li>
+
+In the ‘Sex’ columns, replace values of ‘male’ by ‘M’ and all ‘female’ values by ‘F’
+
+.. ipython:: python
+
+ titanic["Sex_short"] = titanic["Sex"].replace({"male": "M",
+ "female": "F"})
+ titanic["Sex_short"]
+
+Whereas :meth:`~Series.replace` is not a string method, it provides a convenient way
+to use mappings or vocabularies to translate certain values. It requires
+a ``dictionary`` to define the mapping ``{from : to}``.
+
+.. raw:: html
+
+ </li>
+ </ul>
+
+.. warning::
+ There is also a :meth:`~Series.str.replace` methods available to replace a
+ specific set of characters. However, when having a mapping of multiple
+ values, this would become:
+
+ ::
+
+ titanic["Sex_short"] = titanic["Sex"].str.replace("female", "F")
+ titanic["Sex_short"] = titanic["Sex_short"].str.replace("male", "M")
+
+ This would become cumbersome and easily lead to mistakes. Just think (or
+ try out yourself) what would happen if those two statements are applied
+ in the opposite order…
+
+.. raw:: html
+
+ <div class="shadow gs-callout gs-callout-remember">
+ <h4>REMEMBER</h4>
+
+- String methods are available using the ``str`` accessor.
+- String methods work element wise and can be used for conditional
+ indexing.
+- The ``replace`` method is a convenient method to convert values
+ according to a given dictionary.
+
+.. raw:: html
+
+ </div>
+
+.. raw:: html
+
+ <div class="d-flex flex-row gs-torefguide">
+ <span class="badge badge-info">To user guide</span>
+
+A full overview is provided in the user guide pages on :ref:`working with text data <text>`.
+
+.. raw:: html
+
+ </div>
diff --git a/doc/source/getting_started/intro_tutorials/index.rst b/doc/source/getting_started/intro_tutorials/index.rst
new file mode 100644
index 0000000000000..28e7610866461
--- /dev/null
+++ b/doc/source/getting_started/intro_tutorials/index.rst
@@ -0,0 +1,22 @@
+{{ header }}
+
+.. _10times1minute:
+
+=========================
+Getting started tutorials
+=========================
+
+.. toctree::
+ :maxdepth: 1
+
+ 01_table_oriented
+ 02_read_write
+ 03_subset_data
+ 04_plotting
+ 05_add_columns
+ 06_calculate_statistics
+ 07_reshape_table_layout
+ 08_combine_dataframes
+ 09_timeseries
+ 10_text_data
+
diff --git a/doc/source/index.rst.template b/doc/source/index.rst.template
index 820a9d6285e0e..5428239ed2e27 100644
--- a/doc/source/index.rst.template
+++ b/doc/source/index.rst.template
@@ -104,6 +104,7 @@ programming language.
{% if single_doc and single_doc.endswith('.rst') -%}
.. toctree::
:maxdepth: 3
+ :titlesonly:
{{ single_doc[:-4] }}
{% elif single_doc %}
@@ -115,6 +116,7 @@ programming language.
.. toctree::
:maxdepth: 3
:hidden:
+ :titlesonly:
{% endif %}
{% if not single_doc %}
What's New in 1.0.1 <whatsnew/v1.0.1>
diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst
index b28354cd8b5f2..bbec9a770477d 100644
--- a/doc/source/user_guide/reshaping.rst
+++ b/doc/source/user_guide/reshaping.rst
@@ -6,6 +6,8 @@
Reshaping and pivot tables
**************************
+.. _reshaping.reshaping:
+
Reshaping by pivoting DataFrame objects
---------------------------------------
@@ -314,6 +316,8 @@ user-friendly.
dft
pd.wide_to_long(dft, ["A", "B"], i="id", j="year")
+.. _reshaping.combine_with_groupby:
+
Combining with stats and GroupBy
--------------------------------
diff --git a/doc/source/user_guide/text.rst b/doc/source/user_guide/text.rst
index 88c86ac212f11..2e4d0fecaf5cf 100644
--- a/doc/source/user_guide/text.rst
+++ b/doc/source/user_guide/text.rst
@@ -189,12 +189,11 @@ and replacing any remaining whitespaces with underscores:
Generally speaking, the ``.str`` accessor is intended to work only on strings. With very few
exceptions, other uses are not supported, and may be disabled at a later point.
+.. _text.split:
Splitting and replacing strings
-------------------------------
-.. _text.split:
-
Methods like ``split`` return a Series of lists:
.. ipython:: python
| Backport PR #31156: DOC: Update of the 'getting started' pages in the sphinx section of the documentation | https://api.github.com/repos/pandas-dev/pandas/pulls/32052 | 2020-02-17T11:51:00Z | 2020-02-18T15:42:01Z | 2020-02-18T15:42:01Z | 2020-02-18T15:42:02Z |
WEB: update blog link to only include my pandas blog posts | diff --git a/web/pandas/config.yml b/web/pandas/config.yml
index 83eb152c9d944..a52c580f23530 100644
--- a/web/pandas/config.yml
+++ b/web/pandas/config.yml
@@ -54,7 +54,7 @@ blog:
- https://dev.pandas.io/pandas-blog/feeds/all.atom.xml
- https://wesmckinney.com/feeds/pandas.atom.xml
- https://tomaugspurger.github.io/feed
- - https://jorisvandenbossche.github.io/feeds/all.atom.xml
+ - https://jorisvandenbossche.github.io/feeds/pandas.atom.xml
- https://datapythonista.github.io/blog/feeds/pandas.atom.xml
- https://numfocus.org/tag/pandas/feed/
maintainers:
| https://api.github.com/repos/pandas-dev/pandas/pulls/32051 | 2020-02-17T10:42:48Z | 2020-02-18T08:16:33Z | 2020-02-18T08:16:33Z | 2020-02-18T08:16:38Z | |
use numexpr for Series comparisons | diff --git a/pandas/core/ops/__init__.py b/pandas/core/ops/__init__.py
index 1ee4c8e85be6b..d0adf2da04db3 100644
--- a/pandas/core/ops/__init__.py
+++ b/pandas/core/ops/__init__.py
@@ -510,6 +510,7 @@ def _comp_method_SERIES(cls, op, special):
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
+ str_rep = _get_opstr(op)
op_name = _get_op_name(op, special)
@unpack_zerodim_and_defer(op_name)
@@ -523,7 +524,7 @@ def wrapper(self, other):
lvalues = extract_array(self, extract_numpy=True)
rvalues = extract_array(other, extract_numpy=True)
- res_values = comparison_op(lvalues, rvalues, op)
+ res_values = comparison_op(lvalues, rvalues, op, str_rep)
return _construct_result(self, res_values, index=self.index, name=res_name)
diff --git a/pandas/core/ops/array_ops.py b/pandas/core/ops/array_ops.py
index 10e3f32de3958..b216a927f65b3 100644
--- a/pandas/core/ops/array_ops.py
+++ b/pandas/core/ops/array_ops.py
@@ -126,7 +126,7 @@ def na_op(x, y):
return na_op
-def na_arithmetic_op(left, right, op, str_rep: str):
+def na_arithmetic_op(left, right, op, str_rep: Optional[str], is_cmp: bool = False):
"""
Return the result of evaluating op on the passed in values.
@@ -137,6 +137,8 @@ def na_arithmetic_op(left, right, op, str_rep: str):
left : np.ndarray
right : np.ndarray or scalar
str_rep : str or None
+ is_cmp : bool, default False
+ If this a comparison operation.
Returns
-------
@@ -151,8 +153,18 @@ def na_arithmetic_op(left, right, op, str_rep: str):
try:
result = expressions.evaluate(op, str_rep, left, right)
except TypeError:
+ if is_cmp:
+ # numexpr failed on comparison op, e.g. ndarray[float] > datetime
+ # In this case we do not fall back to the masked op, as that
+ # will handle complex numbers incorrectly, see GH#32047
+ raise
result = masked_arith_op(left, right, op)
+ if is_cmp and (is_scalar(result) or result is NotImplemented):
+ # numpy returned a scalar instead of operating element-wise
+ # e.g. numeric array vs str
+ return invalid_comparison(left, right, op)
+
return missing.dispatch_fill_zeros(op, left, right, result)
@@ -199,7 +211,9 @@ def arithmetic_op(left: ArrayLike, right: Any, op, str_rep: str):
return res_values
-def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
+def comparison_op(
+ left: ArrayLike, right: Any, op, str_rep: Optional[str] = None,
+) -> ArrayLike:
"""
Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`.
@@ -244,16 +258,8 @@ def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike:
res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues)
else:
- op_name = f"__{op.__name__}__"
- method = getattr(lvalues, op_name)
with np.errstate(all="ignore"):
- res_values = method(rvalues)
-
- if res_values is NotImplemented:
- res_values = invalid_comparison(lvalues, rvalues, op)
- if is_scalar(res_values):
- typ = type(rvalues)
- raise TypeError(f"Could not compare {typ} type with Series")
+ res_values = na_arithmetic_op(lvalues, rvalues, op, str_rep, is_cmp=True)
return res_values
@@ -380,7 +386,7 @@ def get_array_op(op, str_rep: Optional[str] = None):
"""
op_name = op.__name__.strip("_")
if op_name in {"eq", "ne", "lt", "le", "gt", "ge"}:
- return partial(comparison_op, op=op)
+ return partial(comparison_op, op=op, str_rep=str_rep)
elif op_name in {"and", "or", "xor", "rand", "ror", "rxor"}:
return partial(logical_op, op=op)
else:
diff --git a/pandas/tests/arithmetic/test_numeric.py b/pandas/tests/arithmetic/test_numeric.py
index 51d09a92773b1..d4baf2f374cdf 100644
--- a/pandas/tests/arithmetic/test_numeric.py
+++ b/pandas/tests/arithmetic/test_numeric.py
@@ -66,8 +66,7 @@ def test_df_numeric_cmp_dt64_raises(self):
ts = pd.Timestamp.now()
df = pd.DataFrame({"x": range(5)})
- msg = "Invalid comparison between dtype=int64 and Timestamp"
-
+ msg = "'[<>]' not supported between instances of 'Timestamp' and 'int'"
with pytest.raises(TypeError, match=msg):
df > ts
with pytest.raises(TypeError, match=msg):
| subsumes #31984. | https://api.github.com/repos/pandas-dev/pandas/pulls/32047 | 2020-02-17T03:31:33Z | 2020-02-26T12:53:46Z | 2020-02-26T12:53:46Z | 2020-02-26T15:36:19Z |
Use fixtures in pandas/tests/base | diff --git a/pandas/conftest.py b/pandas/conftest.py
index 7851cba9cd91a..0d3f8b034beb7 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -967,7 +967,7 @@ def __len__(self):
"uint": tm.makeUIntIndex(100),
"range": tm.makeRangeIndex(100),
"float": tm.makeFloatIndex(100),
- "bool": tm.makeBoolIndex(2),
+ "bool": tm.makeBoolIndex(10),
"categorical": tm.makeCategoricalIndex(100),
"interval": tm.makeIntervalIndex(100),
"empty": Index([]),
@@ -978,6 +978,15 @@ def __len__(self):
@pytest.fixture(params=indices_dict.keys())
def indices(request):
+ """
+ Fixture for many "simple" kinds of indices.
+
+ These indices are unlikely to cover corner cases, e.g.
+ - no names
+ - no NaTs/NaNs
+ - no values near implementation bounds
+ - ...
+ """
# copy to avoid mutation, e.g. setting .name
return indices_dict[request.param].copy()
@@ -995,6 +1004,14 @@ def _create_series(index):
}
+@pytest.fixture
+def series_with_simple_index(indices):
+ """
+ Fixture for tests on series with changing types of indices.
+ """
+ return _create_series(indices)
+
+
_narrow_dtypes = [
np.float16,
np.float32,
diff --git a/pandas/tests/base/test_ops.py b/pandas/tests/base/test_ops.py
index 9deb56f070d56..625d559001e72 100644
--- a/pandas/tests/base/test_ops.py
+++ b/pandas/tests/base/test_ops.py
@@ -137,227 +137,238 @@ def setup_method(self, method):
self.is_valid_objs = self.objs
self.not_valid_objs = []
- def test_none_comparison(self):
+ def test_none_comparison(self, series_with_simple_index):
+ series = series_with_simple_index
+ if isinstance(series.index, IntervalIndex):
+ # IntervalIndex breaks on "series[0] = np.nan" below
+ pytest.skip("IntervalIndex doesn't support assignment")
+ if len(series) < 1:
+ pytest.skip("Test doesn't make sense on empty data")
# bug brought up by #1079
# changed from TypeError in 0.17.0
- for o in self.is_valid_objs:
- if isinstance(o, Series):
+ series[0] = np.nan
+
+ # noinspection PyComparisonWithNone
+ result = series == None # noqa
+ assert not result.iat[0]
+ assert not result.iat[1]
+
+ # noinspection PyComparisonWithNone
+ result = series != None # noqa
+ assert result.iat[0]
+ assert result.iat[1]
+
+ result = None == series # noqa
+ assert not result.iat[0]
+ assert not result.iat[1]
+
+ result = None != series # noqa
+ assert result.iat[0]
+ assert result.iat[1]
+
+ if is_datetime64_dtype(series) or is_datetime64tz_dtype(series):
+ # Following DatetimeIndex (and Timestamp) convention,
+ # inequality comparisons with Series[datetime64] raise
+ msg = "Invalid comparison"
+ with pytest.raises(TypeError, match=msg):
+ None > series
+ with pytest.raises(TypeError, match=msg):
+ series > None
+ else:
+ result = None > series
+ assert not result.iat[0]
+ assert not result.iat[1]
- o[0] = np.nan
-
- # noinspection PyComparisonWithNone
- result = o == None # noqa
- assert not result.iat[0]
- assert not result.iat[1]
-
- # noinspection PyComparisonWithNone
- result = o != None # noqa
- assert result.iat[0]
- assert result.iat[1]
-
- result = None == o # noqa
- assert not result.iat[0]
- assert not result.iat[1]
-
- result = None != o # noqa
- assert result.iat[0]
- assert result.iat[1]
-
- if is_datetime64_dtype(o) or is_datetime64tz_dtype(o):
- # Following DatetimeIndex (and Timestamp) convention,
- # inequality comparisons with Series[datetime64] raise
- msg = "Invalid comparison"
- with pytest.raises(TypeError, match=msg):
- None > o
- with pytest.raises(TypeError, match=msg):
- o > None
- else:
- result = None > o
- assert not result.iat[0]
- assert not result.iat[1]
+ result = series < None
+ assert not result.iat[0]
+ assert not result.iat[1]
- result = o < None
- assert not result.iat[0]
- assert not result.iat[1]
+ def test_ndarray_compat_properties(self, index_or_series_obj):
+ obj = index_or_series_obj
- def test_ndarray_compat_properties(self):
+ # Check that we work.
+ for p in ["shape", "dtype", "T", "nbytes"]:
+ assert getattr(obj, p, None) is not None
- for o in self.objs:
- # Check that we work.
- for p in ["shape", "dtype", "T", "nbytes"]:
- assert getattr(o, p, None) is not None
+ # deprecated properties
+ for p in ["flags", "strides", "itemsize", "base", "data"]:
+ assert not hasattr(obj, p)
- # deprecated properties
- for p in ["flags", "strides", "itemsize", "base", "data"]:
- assert not hasattr(o, p)
+ msg = "can only convert an array of size 1 to a Python scalar"
+ with pytest.raises(ValueError, match=msg):
+ obj.item() # len > 1
- msg = "can only convert an array of size 1 to a Python scalar"
- with pytest.raises(ValueError, match=msg):
- o.item() # len > 1
-
- assert o.ndim == 1
- assert o.size == len(o)
+ assert obj.ndim == 1
+ assert obj.size == len(obj)
assert Index([1]).item() == 1
assert Series([1]).item() == 1
- def test_value_counts_unique_nunique(self):
- for orig in self.objs:
- o = orig.copy()
- klass = type(o)
- values = o._values
-
- if isinstance(values, Index):
- # reset name not to affect latter process
- values.name = None
-
- # create repeated values, 'n'th element is repeated by n+1 times
- # skip boolean, because it only has 2 values at most
- if isinstance(o, Index) and o.is_boolean():
- continue
- elif isinstance(o, Index):
- expected_index = Index(o[::-1])
- expected_index.name = None
- o = o.repeat(range(1, len(o) + 1))
- o.name = "a"
- else:
- expected_index = Index(values[::-1])
- idx = o.index.repeat(range(1, len(o) + 1))
- # take-based repeat
- indices = np.repeat(np.arange(len(o)), range(1, len(o) + 1))
- rep = values.take(indices)
- o = klass(rep, index=idx, name="a")
-
- # check values has the same dtype as the original
- assert o.dtype == orig.dtype
-
- expected_s = Series(
- range(10, 0, -1), index=expected_index, dtype="int64", name="a"
+ def test_value_counts_unique_nunique(self, index_or_series_obj):
+ orig = index_or_series_obj
+ obj = orig.copy()
+ klass = type(obj)
+ values = obj._values
+
+ if orig.duplicated().any():
+ pytest.xfail(
+ "The test implementation isn't flexible enough to deal"
+ " with duplicated values. This isn't a bug in the"
+ " application code, but in the test code."
)
- result = o.value_counts()
- tm.assert_series_equal(result, expected_s)
- assert result.index.name is None
- assert result.name == "a"
+ # create repeated values, 'n'th element is repeated by n+1 times
+ if isinstance(obj, Index):
+ expected_index = Index(obj[::-1])
+ expected_index.name = None
+ obj = obj.repeat(range(1, len(obj) + 1))
+ else:
+ expected_index = Index(values[::-1])
+ idx = obj.index.repeat(range(1, len(obj) + 1))
+ # take-based repeat
+ indices = np.repeat(np.arange(len(obj)), range(1, len(obj) + 1))
+ rep = values.take(indices)
+ obj = klass(rep, index=idx)
+
+ # check values has the same dtype as the original
+ assert obj.dtype == orig.dtype
+
+ expected_s = Series(
+ range(len(orig), 0, -1), index=expected_index, dtype="int64"
+ )
- result = o.unique()
- if isinstance(o, Index):
- assert isinstance(result, type(o))
- tm.assert_index_equal(result, orig)
- assert result.dtype == orig.dtype
- elif is_datetime64tz_dtype(o):
- # datetimetz Series returns array of Timestamp
- assert result[0] == orig[0]
- for r in result:
- assert isinstance(r, Timestamp)
-
- tm.assert_numpy_array_equal(
- result.astype(object), orig._values.astype(object)
- )
- else:
- tm.assert_numpy_array_equal(result, orig.values)
- assert result.dtype == orig.dtype
+ result = obj.value_counts()
+ tm.assert_series_equal(result, expected_s)
+ assert result.index.name is None
+
+ result = obj.unique()
+ if isinstance(obj, Index):
+ assert isinstance(result, type(obj))
+ tm.assert_index_equal(result, orig)
+ assert result.dtype == orig.dtype
+ elif is_datetime64tz_dtype(obj):
+ # datetimetz Series returns array of Timestamp
+ assert result[0] == orig[0]
+ for r in result:
+ assert isinstance(r, Timestamp)
+
+ tm.assert_numpy_array_equal(
+ result.astype(object), orig._values.astype(object)
+ )
+ else:
+ tm.assert_numpy_array_equal(result, orig.values)
+ assert result.dtype == orig.dtype
- assert o.nunique() == len(np.unique(o.values))
+ # dropna=True would break for MultiIndex
+ assert obj.nunique(dropna=False) == len(np.unique(obj.values))
@pytest.mark.parametrize("null_obj", [np.nan, None])
- def test_value_counts_unique_nunique_null(self, null_obj):
-
- for orig in self.objs:
- o = orig.copy()
- klass = type(o)
- values = o._ndarray_values
-
- if not allow_na_ops(o):
- continue
-
- # special assign to the numpy array
- if is_datetime64tz_dtype(o):
- if isinstance(o, DatetimeIndex):
- v = o.asi8
- v[0:2] = iNaT
- values = o._shallow_copy(v)
- else:
- o = o.copy()
- o[0:2] = pd.NaT
- values = o._values
-
- elif needs_i8_conversion(o):
- values[0:2] = iNaT
- values = o._shallow_copy(values)
+ def test_value_counts_unique_nunique_null(self, null_obj, index_or_series_obj):
+ orig = index_or_series_obj
+ obj = orig.copy()
+ klass = type(obj)
+ values = obj._ndarray_values
+ num_values = len(orig)
+
+ if not allow_na_ops(obj):
+ pytest.skip("type doesn't allow for NA operations")
+ elif isinstance(orig, (pd.CategoricalIndex, pd.IntervalIndex)):
+ pytest.skip(f"values of {klass} cannot be changed")
+ elif isinstance(orig, pd.MultiIndex):
+ pytest.skip("MultiIndex doesn't support isna")
+
+ # special assign to the numpy array
+ if is_datetime64tz_dtype(obj):
+ if isinstance(obj, DatetimeIndex):
+ v = obj.asi8
+ v[0:2] = iNaT
+ values = obj._shallow_copy(v)
else:
- values[0:2] = null_obj
- # check values has the same dtype as the original
-
- assert values.dtype == o.dtype
+ obj = obj.copy()
+ obj[0:2] = pd.NaT
+ values = obj._values
- # create repeated values, 'n'th element is repeated by n+1
- # times
- if isinstance(o, (DatetimeIndex, PeriodIndex)):
- expected_index = o.copy()
- expected_index.name = None
+ elif needs_i8_conversion(obj):
+ values[0:2] = iNaT
+ values = obj._shallow_copy(values)
+ else:
+ values[0:2] = null_obj
- # attach name to klass
- o = klass(values.repeat(range(1, len(o) + 1)))
- o.name = "a"
- else:
- if isinstance(o, DatetimeIndex):
- expected_index = orig._values._shallow_copy(values)
- else:
- expected_index = Index(values)
- expected_index.name = None
- o = o.repeat(range(1, len(o) + 1))
- o.name = "a"
-
- # check values has the same dtype as the original
- assert o.dtype == orig.dtype
- # check values correctly have NaN
- nanloc = np.zeros(len(o), dtype=np.bool)
- nanloc[:3] = True
- if isinstance(o, Index):
- tm.assert_numpy_array_equal(pd.isna(o), nanloc)
- else:
- exp = Series(nanloc, o.index, name="a")
- tm.assert_series_equal(pd.isna(o), exp)
-
- expected_s_na = Series(
- list(range(10, 2, -1)) + [3],
- index=expected_index[9:0:-1],
- dtype="int64",
- name="a",
- )
- expected_s = Series(
- list(range(10, 2, -1)),
- index=expected_index[9:1:-1],
- dtype="int64",
- name="a",
- )
+ # check values has the same dtype as the original
+ assert values.dtype == obj.dtype
- result_s_na = o.value_counts(dropna=False)
- tm.assert_series_equal(result_s_na, expected_s_na)
- assert result_s_na.index.name is None
- assert result_s_na.name == "a"
- result_s = o.value_counts()
- tm.assert_series_equal(o.value_counts(), expected_s)
- assert result_s.index.name is None
- assert result_s.name == "a"
+ # create repeated values, 'n'th element is repeated by n+1
+ # times
+ if isinstance(obj, (DatetimeIndex, PeriodIndex)):
+ expected_index = obj.copy()
+ expected_index.name = None
- result = o.unique()
- if isinstance(o, Index):
- tm.assert_index_equal(result, Index(values[1:], name="a"))
- elif is_datetime64tz_dtype(o):
- # unable to compare NaT / nan
- tm.assert_extension_array_equal(result[1:], values[2:])
- assert result[0] is pd.NaT
+ # attach name to klass
+ obj = klass(values.repeat(range(1, len(obj) + 1)))
+ obj.name = "a"
+ else:
+ if isinstance(obj, DatetimeIndex):
+ expected_index = orig._values._shallow_copy(values)
else:
- tm.assert_numpy_array_equal(result[1:], values[2:])
-
- assert pd.isna(result[0])
- assert result.dtype == orig.dtype
+ expected_index = Index(values)
+ expected_index.name = None
+ obj = obj.repeat(range(1, len(obj) + 1))
+ obj.name = "a"
+
+ # check values has the same dtype as the original
+ assert obj.dtype == orig.dtype
+
+ # check values correctly have NaN
+ nanloc = np.zeros(len(obj), dtype=np.bool)
+ nanloc[:3] = True
+ if isinstance(obj, Index):
+ tm.assert_numpy_array_equal(pd.isna(obj), nanloc)
+ else:
+ exp = Series(nanloc, obj.index, name="a")
+ tm.assert_series_equal(pd.isna(obj), exp)
+
+ expected_data = list(range(num_values, 2, -1))
+ expected_data_na = expected_data.copy()
+ if expected_data_na:
+ expected_data_na.append(3)
+ expected_s_na = Series(
+ expected_data_na,
+ index=expected_index[num_values - 1 : 0 : -1],
+ dtype="int64",
+ name="a",
+ )
+ expected_s = Series(
+ expected_data,
+ index=expected_index[num_values - 1 : 1 : -1],
+ dtype="int64",
+ name="a",
+ )
- assert o.nunique() == 8
- assert o.nunique(dropna=False) == 9
+ result_s_na = obj.value_counts(dropna=False)
+ tm.assert_series_equal(result_s_na, expected_s_na)
+ assert result_s_na.index.name is None
+ assert result_s_na.name == "a"
+ result_s = obj.value_counts()
+ tm.assert_series_equal(obj.value_counts(), expected_s)
+ assert result_s.index.name is None
+ assert result_s.name == "a"
+
+ result = obj.unique()
+ if isinstance(obj, Index):
+ tm.assert_index_equal(result, Index(values[1:], name="a"))
+ elif is_datetime64tz_dtype(obj):
+ # unable to compare NaT / nan
+ tm.assert_extension_array_equal(result[1:], values[2:])
+ assert result[0] is pd.NaT
+ elif len(obj) > 0:
+ tm.assert_numpy_array_equal(result[1:], values[2:])
+
+ assert pd.isna(result[0])
+ assert result.dtype == orig.dtype
+
+ assert obj.nunique() == max(0, num_values - 2)
+ assert obj.nunique(dropna=False) == max(0, num_values - 1)
def test_value_counts_inferred(self, index_or_series):
klass = index_or_series
diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py
index 2073aa0727809..a7437b39872be 100644
--- a/pandas/tests/indexes/common.py
+++ b/pandas/tests/indexes/common.py
@@ -514,12 +514,12 @@ def test_union_base(self, indices):
@pytest.mark.parametrize("sort", [None, False])
def test_difference_base(self, sort, indices):
- if isinstance(indices, CategoricalIndex):
- return
-
first = indices[2:]
second = indices[:4]
- answer = indices[4:]
+ if isinstance(indices, CategoricalIndex) or indices.is_boolean():
+ answer = []
+ else:
+ answer = indices[4:]
result = first.difference(second, sort)
assert tm.equalContents(result, answer)
| part of #23877
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
**Note:** The diff is a bit inflated. Most changes are just indentation because for loops are replaced by a parametrized fixture. | https://api.github.com/repos/pandas-dev/pandas/pulls/32046 | 2020-02-17T00:50:11Z | 2020-02-23T17:06:03Z | 2020-02-23T17:06:03Z | 2020-02-23T17:46:40Z |
DOC: Mention black and PEP8 in pandas style guide | diff --git a/doc/source/development/code_style.rst b/doc/source/development/code_style.rst
index bcddc033a61f5..17f8783f71bfb 100644
--- a/doc/source/development/code_style.rst
+++ b/doc/source/development/code_style.rst
@@ -9,6 +9,12 @@ pandas code style guide
.. contents:: Table of contents:
:local:
+*pandas* follows the `PEP8 <https://www.python.org/dev/peps/pep-0008/>`_
+standard and uses `Black <https://black.readthedocs.io/en/stable/>`_
+and `Flake8 <https://flake8.pycqa.org/en/latest/>`_ to ensure a
+consistent code format throughout the project. For details see the
+:ref:`contributing guide to pandas<contributing.code-formatting>`.
+
Patterns
========
| - [ ] closes #31828
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32043 | 2020-02-16T18:51:03Z | 2020-02-19T01:46:26Z | 2020-02-19T01:46:26Z | 2020-02-19T01:46:33Z |
CLN: clean-up show_versions and consistently use null for json output | diff --git a/pandas/_typing.py b/pandas/_typing.py
index e2858441605f7..3b7392f781525 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -64,7 +64,7 @@
Label = Optional[Hashable]
Level = Union[Label, int]
Ordered = Optional[bool]
-JSONSerializable = Union[PythonScalar, List, Dict]
+JSONSerializable = Optional[Union[PythonScalar, List, Dict]]
Axes = Collection
# For functions like rename that convert one label to another
diff --git a/pandas/util/_print_versions.py b/pandas/util/_print_versions.py
index f9502cc22b0c6..7fc85a04e7d84 100644
--- a/pandas/util/_print_versions.py
+++ b/pandas/util/_print_versions.py
@@ -5,8 +5,9 @@
import platform
import struct
import sys
-from typing import List, Optional, Tuple, Union
+from typing import Dict, Optional, Union
+from pandas._typing import JSONSerializable
from pandas.compat._optional import VERSIONS, _get_version, import_optional_dependency
@@ -21,43 +22,32 @@ def _get_commit_hash() -> Optional[str]:
return versions["full-revisionid"]
-def get_sys_info() -> List[Tuple[str, Optional[Union[str, int]]]]:
+def _get_sys_info() -> Dict[str, JSONSerializable]:
"""
- Returns system information as a list
+ Returns system information as a JSON serializable dictionary.
+ """
+ uname_result = platform.uname()
+ language_code, encoding = locale.getlocale()
+ return {
+ "commit": _get_commit_hash(),
+ "python": ".".join(str(i) for i in sys.version_info),
+ "python-bits": struct.calcsize("P") * 8,
+ "OS": uname_result.system,
+ "OS-release": uname_result.release,
+ "Version": uname_result.version,
+ "machine": uname_result.machine,
+ "processor": uname_result.processor,
+ "byteorder": sys.byteorder,
+ "LC_ALL": os.environ.get("LC_ALL"),
+ "LANG": os.environ.get("LANG"),
+ "LOCALE": {"language-code": language_code, "encoding": encoding},
+ }
+
+
+def _get_dependency_info() -> Dict[str, JSONSerializable]:
+ """
+ Returns dependency information as a JSON serializable dictionary.
"""
- blob: List[Tuple[str, Optional[Union[str, int]]]] = []
-
- # get full commit hash
- commit = _get_commit_hash()
-
- blob.append(("commit", commit))
-
- try:
- (sysname, nodename, release, version, machine, processor) = platform.uname()
- blob.extend(
- [
- ("python", ".".join(map(str, sys.version_info))),
- ("python-bits", struct.calcsize("P") * 8),
- ("OS", f"{sysname}"),
- ("OS-release", f"{release}"),
- # FIXME: dont leave commented-out
- # ("Version", f"{version}"),
- ("machine", f"{machine}"),
- ("processor", f"{processor}"),
- ("byteorder", f"{sys.byteorder}"),
- ("LC_ALL", f"{os.environ.get('LC_ALL', 'None')}"),
- ("LANG", f"{os.environ.get('LANG', 'None')}"),
- ("LOCALE", ".".join(map(str, locale.getlocale()))),
- ]
- )
- except (KeyError, ValueError):
- pass
-
- return blob
-
-
-def show_versions(as_json=False):
- sys_info = get_sys_info()
deps = [
"pandas",
# required
@@ -86,39 +76,45 @@ def show_versions(as_json=False):
"IPython",
"pandas_datareader",
]
-
deps.extend(list(VERSIONS))
- deps_blob = []
+ result: Dict[str, JSONSerializable] = {}
for modname in deps:
mod = import_optional_dependency(
modname, raise_on_missing=False, on_version="ignore"
)
- ver: Optional[str]
- if mod:
- ver = _get_version(mod)
- else:
- ver = None
- deps_blob.append((modname, ver))
+ result[modname] = _get_version(mod) if mod else None
+ return result
+
+
+def show_versions(as_json: Union[str, bool] = False) -> None:
+ sys_info = _get_sys_info()
+ deps = _get_dependency_info()
if as_json:
- j = dict(system=dict(sys_info), dependencies=dict(deps_blob))
+ j = dict(system=sys_info, dependencies=deps)
if as_json is True:
print(j)
else:
+ assert isinstance(as_json, str) # needed for mypy
with codecs.open(as_json, "wb", encoding="utf8") as f:
json.dump(j, f, indent=2)
else:
+ assert isinstance(sys_info["LOCALE"], dict) # needed for mypy
+ language_code = sys_info["LOCALE"]["language-code"]
+ encoding = sys_info["LOCALE"]["encoding"]
+ sys_info["LOCALE"] = f"{language_code}.{encoding}"
+
maxlen = max(len(x) for x in deps)
print("\nINSTALLED VERSIONS")
print("------------------")
- for k, stat in sys_info:
- print(f"{k:<{maxlen}}: {stat}")
+ for k, v in sys_info.items():
+ print(f"{k:<{maxlen}}: {v}")
print("")
- for k, stat in deps_blob:
- print(f"{k:<{maxlen}}: {stat}")
+ for k, v in deps.items():
+ print(f"{k:<{maxlen}}: {v}")
def main() -> int:
| note changes to LC_ALL and LOCALE, null is already used for dependencies on master, see blosc and feather.
master
```
{
"system": {
"commit": "a7ecced88a42c426bf61016c0131cab023c0cdff",
"python": "3.7.5.final.0",
"python-bits": 64,
"OS": "Windows",
"OS-release": "10",
"machine": "AMD64",
"processor": "Intel64 Family 6 Model 58 Stepping 9, GenuineIntel",
"byteorder": "little",
"LC_ALL": "None",
"LANG": "en_GB.UTF-8",
"LOCALE": "None.None"
},
"dependencies": {
"pandas": "1.1.0.dev0+497.ga7ecced88",
"numpy": "1.17.2",
"pytz": "2019.3",
"dateutil": "2.8.0",
"pip": "19.3.1",
"setuptools": "41.6.0.post20191030",
"Cython": "0.29.13",
"pytest": "5.2.2",
"hypothesis": "4.36.2",
"sphinx": "2.2.1",
"blosc": null,
"feather": null,
...
```
this pr
```
{
"system": {
"commit": "2ac9d30f302e59de035cbea89188d5421212b000",
"python": "3.7.5.final.0",
"python-bits": 64,
"OS": "Windows",
"OS-release": "10",
"Version": "10.0.18362",
"machine": "AMD64",
"processor": "Intel64 Family 6 Model 58 Stepping 9, GenuineIntel",
"byteorder": "little",
"LC_ALL": null,
"LANG": "en_GB.UTF-8",
"LOCALE": {
"language-code": null,
"encoding": null
}
},
"dependencies": {
"pandas": "1.1.0.dev0+500.g2ac9d30f3",
"numpy": "1.17.2",
"pytz": "2019.3",
"dateutil": "2.8.0",
"pip": "19.3.1",
"setuptools": "41.6.0.post20191030",
"Cython": "0.29.13",
"pytest": "5.2.2",
"hypothesis": "4.36.2",
"sphinx": "2.2.1",
"blosc": null,
...
```
console output is unchanged
```
INSTALLED VERSIONS
------------------
commit : 2ac9d30f302e59de035cbea89188d5421212b000
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 1.1.0.dev0+500.g2ac9d30f3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.2
hypothesis : 4.36.2
sphinx : 2.2.1
blosc : None
feather : None
...
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/32042 | 2020-02-16T16:56:07Z | 2020-03-03T03:36:27Z | 2020-03-03T03:36:27Z | 2020-03-03T11:43:14Z |
REGR: show_versions | diff --git a/pandas/tests/test_optional_dependency.py b/pandas/tests/test_optional_dependency.py
index ce527214e55e7..e5ed69b7703b1 100644
--- a/pandas/tests/test_optional_dependency.py
+++ b/pandas/tests/test_optional_dependency.py
@@ -22,12 +22,12 @@ def test_xlrd_version_fallback():
import_optional_dependency("xlrd")
-def test_bad_version():
+def test_bad_version(monkeypatch):
name = "fakemodule"
module = types.ModuleType(name)
module.__version__ = "0.9.0"
sys.modules[name] = module
- VERSIONS[name] = "1.0.0"
+ monkeypatch.setitem(VERSIONS, name, "1.0.0")
match = "Pandas requires .*1.0.0.* of .fakemodule.*'0.9.0'"
with pytest.raises(ImportError, match=match):
@@ -42,11 +42,11 @@ def test_bad_version():
assert result is module
-def test_no_version_raises():
+def test_no_version_raises(monkeypatch):
name = "fakemodule"
module = types.ModuleType(name)
sys.modules[name] = module
- VERSIONS[name] = "1.0.0"
+ monkeypatch.setitem(VERSIONS, name, "1.0.0")
with pytest.raises(ImportError, match="Can't determine .* fakemodule"):
import_optional_dependency(name)
diff --git a/pandas/tests/util/test_show_versions.py b/pandas/tests/util/test_show_versions.py
new file mode 100644
index 0000000000000..0d2c81c4ea6c7
--- /dev/null
+++ b/pandas/tests/util/test_show_versions.py
@@ -0,0 +1,22 @@
+import re
+
+import pandas as pd
+
+
+def test_show_versions(capsys):
+ # gh-32041
+ pd.show_versions()
+ captured = capsys.readouterr()
+ result = captured.out
+
+ # check header
+ assert "INSTALLED VERSIONS" in result
+
+ # check full commit hash
+ assert re.search(r"commit\s*:\s[0-9a-f]{40}\n", result)
+
+ # check required dependency
+ assert re.search(r"numpy\s*:\s([0-9\.\+a-f]|dev)+\n", result)
+
+ # check optional dependency
+ assert re.search(r"pyarrow\s*:\s([0-9\.]+|None)\n", result)
diff --git a/pandas/util/_print_versions.py b/pandas/util/_print_versions.py
index fdfa436ce6536..99b2b9e9f5f6e 100644
--- a/pandas/util/_print_versions.py
+++ b/pandas/util/_print_versions.py
@@ -118,10 +118,10 @@ def show_versions(as_json=False):
print("\nINSTALLED VERSIONS")
print("------------------")
for k, stat in sys_info:
- print(f"{{k:<{maxlen}}}: {{stat}}")
+ print(f"{k:<{maxlen}}: {stat}")
print("")
for k, stat in deps_blob:
- print(f"{{k:<{maxlen}}}: {{stat}}")
+ print(f"{k:<{maxlen}}: {stat}")
def main() -> int:
| regression in #31660 (no need to backport)
master
```
INSTALLED VERSIONS
------------------
{k:<17}: {stat}
{k:<17}: {stat}
{k:<17}: {stat}
...
```
this PR
```
INSTALLED VERSIONS
------------------
commit : b11e0647d42a27c279f3c46d5ce26d79bb5f5dec
python : 3.7.4.final.0
python-bits : 64
...
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/32041 | 2020-02-16T11:49:56Z | 2020-02-19T01:47:39Z | 2020-02-19T01:47:39Z | 2020-02-19T09:39:44Z |
BUG: GroupBy aggregation of DataFrame with MultiIndex columns breaks with custom function | diff --git a/doc/source/whatsnew/v1.0.2.rst b/doc/source/whatsnew/v1.0.2.rst
index 123dfa07f4331..18a431cc2be5e 100644
--- a/doc/source/whatsnew/v1.0.2.rst
+++ b/doc/source/whatsnew/v1.0.2.rst
@@ -17,6 +17,7 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.to_excel` when ``columns`` kwarg is passed (:issue:`31677`)
- Fixed regression in :meth:`Series.align` when ``other`` is a DataFrame and ``method`` is not None (:issue:`31785`)
+- Fixed regression in :meth:`groupby(..).agg() <pandas.core.groupby.GroupBy.agg>` which was failing on frames with MultiIndex columns and a custom function (:issue:`31777`)
- Fixed regression in ``groupby(..).rolling(..).apply()`` (``RollingGroupby``) where the ``raw`` parameter was ignored (:issue:`31754`)
- Fixed regression in :meth:`rolling(..).corr() <pandas.core.window.rolling.Rolling.corr>` when using a time offset (:issue:`31789`)
- Fixed regression in :meth:`groupby(..).nunique() <pandas.core.groupby.DataFrameGroupBy.nunique>` which was modifying the original values if ``NaN`` values were present (:issue:`31950`)
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index b7ac3048631c5..fda66f68f7adc 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -955,9 +955,11 @@ def aggregate(self, func=None, *args, **kwargs):
raise
result = self._aggregate_frame(func)
else:
- result.columns = Index(
- result.columns.levels[0], name=self._selected_obj.columns.name
- )
+ # select everything except for the last level, which is the one
+ # containing the name of the function(s), see GH 32040
+ result.columns = result.columns.rename(
+ [self._selected_obj.columns.name] * result.columns.nlevels
+ ).droplevel(-1)
if not self.as_index:
self._insert_inaxis_grouper_inplace(result)
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py
index 48f8de7e51ae4..1265547653d7b 100644
--- a/pandas/tests/groupby/aggregate/test_aggregate.py
+++ b/pandas/tests/groupby/aggregate/test_aggregate.py
@@ -691,6 +691,19 @@ def test_agg_relabel_multiindex_duplicates():
tm.assert_frame_equal(result, expected)
+@pytest.mark.parametrize(
+ "func", [lambda s: s.mean(), lambda s: np.mean(s), lambda s: np.nanmean(s)]
+)
+def test_multiindex_custom_func(func):
+ # GH 31777
+ data = [[1, 4, 2], [5, 7, 1]]
+ df = pd.DataFrame(data, columns=pd.MultiIndex.from_arrays([[1, 1, 2], [3, 4, 3]]))
+ result = df.groupby(np.array([0, 1])).agg(func)
+ expected_dict = {(1, 3): {0: 1, 1: 5}, (1, 4): {0: 4, 1: 7}, (2, 3): {0: 2, 1: 1}}
+ expected = pd.DataFrame(expected_dict)
+ tm.assert_frame_equal(result, expected)
+
+
def myfunc(s):
return np.percentile(s, q=0.90)
| - [x] closes #31777
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32040 | 2020-02-16T11:32:31Z | 2020-03-12T02:32:56Z | 2020-03-12T02:32:55Z | 2020-07-12T09:24:28Z |
BUG: Preserve name in Index.astype | diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 0ebe57bfbb3a1..c8c078a4e685d 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -410,6 +410,7 @@ Reshaping
- Bug in :func:`crosstab` when inputs are two Series and have tuple names, the output will keep dummy MultiIndex as columns. (:issue:`18321`)
- :meth:`DataFrame.pivot` can now take lists for ``index`` and ``columns`` arguments (:issue:`21425`)
- Bug in :func:`concat` where the resulting indices are not copied when ``copy=True`` (:issue:`29879`)
+- Bug where :meth:`Index.astype` would lose the name attribute when converting from ``Float64Index`` to ``Int64Index``, or when casting to an ``ExtensionArray`` dtype (:issue:`32013`)
- :meth:`Series.append` will now raise a ``TypeError`` when passed a DataFrame or a sequence containing Dataframe (:issue:`31413`)
- :meth:`DataFrame.replace` and :meth:`Series.replace` will raise a ``TypeError`` if ``to_replace`` is not an expected type. Previously the ``replace`` would fail silently (:issue:`18634`)
- Bug on inplace operation of a Series that was adding a column to the DataFrame from where it was originally dropped from (using inplace=True) (:issue:`30484`)
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 5b439a851a709..507adac789fa0 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -670,7 +670,7 @@ def astype(self, dtype, copy=True):
return CategoricalIndex(self.values, name=self.name, dtype=dtype, copy=copy)
elif is_extension_array_dtype(dtype):
- return Index(np.asarray(self), dtype=dtype, copy=copy)
+ return Index(np.asarray(self), name=self.name, dtype=dtype, copy=copy)
try:
casted = self.values.astype(dtype, copy=copy)
diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py
index 3a6f3630c19e7..4dbe5ffde7e52 100644
--- a/pandas/core/indexes/numeric.py
+++ b/pandas/core/indexes/numeric.py
@@ -369,7 +369,7 @@ def astype(self, dtype, copy=True):
# TODO(jreback); this can change once we have an EA Index type
# GH 13149
arr = astype_nansafe(self._values, dtype=dtype)
- return Int64Index(arr)
+ return Int64Index(arr, name=self.name)
return super().astype(dtype, copy=copy)
# ----------------------------------------------------------------
diff --git a/pandas/tests/indexes/datetimes/test_astype.py b/pandas/tests/indexes/datetimes/test_astype.py
index 916f722247a14..34169a670c169 100644
--- a/pandas/tests/indexes/datetimes/test_astype.py
+++ b/pandas/tests/indexes/datetimes/test_astype.py
@@ -22,27 +22,32 @@
class TestDatetimeIndex:
def test_astype(self):
# GH 13149, GH 13209
- idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN])
+ idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], name="idx")
result = idx.astype(object)
- expected = Index([Timestamp("2016-05-16")] + [NaT] * 3, dtype=object)
+ expected = Index(
+ [Timestamp("2016-05-16")] + [NaT] * 3, dtype=object, name="idx"
+ )
tm.assert_index_equal(result, expected)
result = idx.astype(int)
expected = Int64Index(
- [1463356800000000000] + [-9223372036854775808] * 3, dtype=np.int64
+ [1463356800000000000] + [-9223372036854775808] * 3,
+ dtype=np.int64,
+ name="idx",
)
tm.assert_index_equal(result, expected)
- rng = date_range("1/1/2000", periods=10)
+ rng = date_range("1/1/2000", periods=10, name="idx")
result = rng.astype("i8")
- tm.assert_index_equal(result, Index(rng.asi8))
+ tm.assert_index_equal(result, Index(rng.asi8, name="idx"))
tm.assert_numpy_array_equal(result.values, rng.asi8)
def test_astype_uint(self):
- arr = date_range("2000", periods=2)
+ arr = date_range("2000", periods=2, name="idx")
expected = pd.UInt64Index(
- np.array([946684800000000000, 946771200000000000], dtype="uint64")
+ np.array([946684800000000000, 946771200000000000], dtype="uint64"),
+ name="idx",
)
tm.assert_index_equal(arr.astype("uint64"), expected)
@@ -148,7 +153,7 @@ def test_astype_str(self):
def test_astype_datetime64(self):
# GH 13149, GH 13209
- idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN])
+ idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], name="idx")
result = idx.astype("datetime64[ns]")
tm.assert_index_equal(result, idx)
@@ -158,10 +163,12 @@ def test_astype_datetime64(self):
tm.assert_index_equal(result, idx)
assert result is idx
- idx_tz = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], tz="EST")
+ idx_tz = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], tz="EST", name="idx")
result = idx_tz.astype("datetime64[ns]")
expected = DatetimeIndex(
- ["2016-05-16 05:00:00", "NaT", "NaT", "NaT"], dtype="datetime64[ns]"
+ ["2016-05-16 05:00:00", "NaT", "NaT", "NaT"],
+ dtype="datetime64[ns]",
+ name="idx",
)
tm.assert_index_equal(result, expected)
@@ -273,8 +280,8 @@ def _check_rng(rng):
def test_integer_index_astype_datetime(self, tz, dtype):
# GH 20997, 20964, 24559
val = [pd.Timestamp("2018-01-01", tz=tz).value]
- result = pd.Index(val).astype(dtype)
- expected = pd.DatetimeIndex(["2018-01-01"], tz=tz)
+ result = pd.Index(val, name="idx").astype(dtype)
+ expected = pd.DatetimeIndex(["2018-01-01"], tz=tz, name="idx")
tm.assert_index_equal(result, expected)
def test_dti_astype_period(self):
@@ -292,10 +299,11 @@ def test_dti_astype_period(self):
class TestAstype:
@pytest.mark.parametrize("tz", [None, "US/Central"])
def test_astype_category(self, tz):
- obj = pd.date_range("2000", periods=2, tz=tz)
+ obj = pd.date_range("2000", periods=2, tz=tz, name="idx")
result = obj.astype("category")
expected = pd.CategoricalIndex(
- [pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)]
+ [pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)],
+ name="idx",
)
tm.assert_index_equal(result, expected)
@@ -305,9 +313,9 @@ def test_astype_category(self, tz):
@pytest.mark.parametrize("tz", [None, "US/Central"])
def test_astype_array_fallback(self, tz):
- obj = pd.date_range("2000", periods=2, tz=tz)
+ obj = pd.date_range("2000", periods=2, tz=tz, name="idx")
result = obj.astype(bool)
- expected = pd.Index(np.array([True, True]))
+ expected = pd.Index(np.array([True, True]), name="idx")
tm.assert_index_equal(result, expected)
result = obj._data.astype(bool)
diff --git a/pandas/tests/indexes/period/test_astype.py b/pandas/tests/indexes/period/test_astype.py
index 2f10e45193d5d..b286191623ebb 100644
--- a/pandas/tests/indexes/period/test_astype.py
+++ b/pandas/tests/indexes/period/test_astype.py
@@ -27,31 +27,34 @@ def test_astype_raises(self, dtype):
def test_astype_conversion(self):
# GH#13149, GH#13209
- idx = PeriodIndex(["2016-05-16", "NaT", NaT, np.NaN], freq="D")
+ idx = PeriodIndex(["2016-05-16", "NaT", NaT, np.NaN], freq="D", name="idx")
result = idx.astype(object)
expected = Index(
[Period("2016-05-16", freq="D")] + [Period(NaT, freq="D")] * 3,
dtype="object",
+ name="idx",
)
tm.assert_index_equal(result, expected)
result = idx.astype(np.int64)
- expected = Int64Index([16937] + [-9223372036854775808] * 3, dtype=np.int64)
+ expected = Int64Index(
+ [16937] + [-9223372036854775808] * 3, dtype=np.int64, name="idx"
+ )
tm.assert_index_equal(result, expected)
result = idx.astype(str)
- expected = Index(str(x) for x in idx)
+ expected = Index([str(x) for x in idx], name="idx")
tm.assert_index_equal(result, expected)
- idx = period_range("1990", "2009", freq="A")
+ idx = period_range("1990", "2009", freq="A", name="idx")
result = idx.astype("i8")
- tm.assert_index_equal(result, Index(idx.asi8))
+ tm.assert_index_equal(result, Index(idx.asi8, name="idx"))
tm.assert_numpy_array_equal(result.values, idx.asi8)
def test_astype_uint(self):
- arr = period_range("2000", periods=2)
- expected = UInt64Index(np.array([10957, 10958], dtype="uint64"))
+ arr = period_range("2000", periods=2, name="idx")
+ expected = UInt64Index(np.array([10957, 10958], dtype="uint64"), name="idx")
tm.assert_index_equal(arr.astype("uint64"), expected)
tm.assert_index_equal(arr.astype("uint32"), expected)
@@ -116,10 +119,10 @@ def test_astype_object2(self):
assert result_list[2] is NaT
def test_astype_category(self):
- obj = period_range("2000", periods=2)
+ obj = period_range("2000", periods=2, name="idx")
result = obj.astype("category")
expected = CategoricalIndex(
- [Period("2000-01-01", freq="D"), Period("2000-01-02", freq="D")]
+ [Period("2000-01-01", freq="D"), Period("2000-01-02", freq="D")], name="idx"
)
tm.assert_index_equal(result, expected)
@@ -128,9 +131,9 @@ def test_astype_category(self):
tm.assert_categorical_equal(result, expected)
def test_astype_array_fallback(self):
- obj = period_range("2000", periods=2)
+ obj = period_range("2000", periods=2, name="idx")
result = obj.astype(bool)
- expected = Index(np.array([True, True]))
+ expected = Index(np.array([True, True]), name="idx")
tm.assert_index_equal(result, expected)
result = obj._data.astype(bool)
diff --git a/pandas/tests/indexes/test_common.py b/pandas/tests/indexes/test_common.py
index 80c577253f536..01d72670f37aa 100644
--- a/pandas/tests/indexes/test_common.py
+++ b/pandas/tests/indexes/test_common.py
@@ -369,3 +369,29 @@ def test_has_duplicates(self, indices):
idx = holder([indices[0]] * 5)
assert idx.is_unique is False
assert idx.has_duplicates is True
+
+ @pytest.mark.parametrize(
+ "dtype",
+ ["int64", "uint64", "float64", "category", "datetime64[ns]", "timedelta64[ns]"],
+ )
+ @pytest.mark.parametrize("copy", [True, False])
+ def test_astype_preserves_name(self, indices, dtype, copy):
+ # https://github.com/pandas-dev/pandas/issues/32013
+ if isinstance(indices, MultiIndex):
+ indices.names = ["idx" + str(i) for i in range(indices.nlevels)]
+ else:
+ indices.name = "idx"
+
+ try:
+ # Some of these conversions cannot succeed so we use a try / except
+ if copy:
+ result = indices.copy(dtype=dtype)
+ else:
+ result = indices.astype(dtype)
+ except (ValueError, TypeError, NotImplementedError, SystemError):
+ return
+
+ if isinstance(indices, MultiIndex):
+ assert result.names == indices.names
+ else:
+ assert result.name == indices.name
diff --git a/pandas/tests/indexes/timedeltas/test_astype.py b/pandas/tests/indexes/timedeltas/test_astype.py
index 82c9d995c9c7c..d9f24b4a35520 100644
--- a/pandas/tests/indexes/timedeltas/test_astype.py
+++ b/pandas/tests/indexes/timedeltas/test_astype.py
@@ -47,20 +47,22 @@ def test_astype_object_with_nat(self):
def test_astype(self):
# GH 13149, GH 13209
- idx = TimedeltaIndex([1e14, "NaT", NaT, np.NaN])
+ idx = TimedeltaIndex([1e14, "NaT", NaT, np.NaN], name="idx")
result = idx.astype(object)
- expected = Index([Timedelta("1 days 03:46:40")] + [NaT] * 3, dtype=object)
+ expected = Index(
+ [Timedelta("1 days 03:46:40")] + [NaT] * 3, dtype=object, name="idx"
+ )
tm.assert_index_equal(result, expected)
result = idx.astype(int)
expected = Int64Index(
- [100000000000000] + [-9223372036854775808] * 3, dtype=np.int64
+ [100000000000000] + [-9223372036854775808] * 3, dtype=np.int64, name="idx"
)
tm.assert_index_equal(result, expected)
result = idx.astype(str)
- expected = Index(str(x) for x in idx)
+ expected = Index([str(x) for x in idx], name="idx")
tm.assert_index_equal(result, expected)
rng = timedelta_range("1 days", periods=10)
| - [x] closes #32013
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32036 | 2020-02-16T03:15:52Z | 2020-03-27T00:00:35Z | 2020-03-27T00:00:34Z | 2020-03-27T01:06:31Z |
CLN: Clean reductions/test_reductions.py | diff --git a/pandas/tests/reductions/test_reductions.py b/pandas/tests/reductions/test_reductions.py
index 0b312fe2f8990..211d0d52d8357 100644
--- a/pandas/tests/reductions/test_reductions.py
+++ b/pandas/tests/reductions/test_reductions.py
@@ -66,60 +66,64 @@ def test_ops(self, opname, obj):
expected = expected.astype("M8[ns]").astype("int64")
assert result.value == expected
- def test_nanops(self):
+ @pytest.mark.parametrize("opname", ["max", "min"])
+ def test_nanops(self, opname, index_or_series):
# GH#7261
- for opname in ["max", "min"]:
- for klass in [Index, Series]:
- arg_op = "arg" + opname if klass is Index else "idx" + opname
-
- obj = klass([np.nan, 2.0])
- assert getattr(obj, opname)() == 2.0
-
- obj = klass([np.nan])
- assert pd.isna(getattr(obj, opname)())
- assert pd.isna(getattr(obj, opname)(skipna=False))
-
- obj = klass([], dtype=object)
- assert pd.isna(getattr(obj, opname)())
- assert pd.isna(getattr(obj, opname)(skipna=False))
-
- obj = klass([pd.NaT, datetime(2011, 11, 1)])
- # check DatetimeIndex monotonic path
- assert getattr(obj, opname)() == datetime(2011, 11, 1)
- assert getattr(obj, opname)(skipna=False) is pd.NaT
-
- assert getattr(obj, arg_op)() == 1
- result = getattr(obj, arg_op)(skipna=False)
- if klass is Series:
- assert np.isnan(result)
- else:
- assert result == -1
-
- obj = klass([pd.NaT, datetime(2011, 11, 1), pd.NaT])
- # check DatetimeIndex non-monotonic path
- assert getattr(obj, opname)(), datetime(2011, 11, 1)
- assert getattr(obj, opname)(skipna=False) is pd.NaT
-
- assert getattr(obj, arg_op)() == 1
- result = getattr(obj, arg_op)(skipna=False)
- if klass is Series:
- assert np.isnan(result)
- else:
- assert result == -1
-
- for dtype in ["M8[ns]", "datetime64[ns, UTC]"]:
- # cases with empty Series/DatetimeIndex
- obj = klass([], dtype=dtype)
-
- assert getattr(obj, opname)() is pd.NaT
- assert getattr(obj, opname)(skipna=False) is pd.NaT
-
- with pytest.raises(ValueError, match="empty sequence"):
- getattr(obj, arg_op)()
- with pytest.raises(ValueError, match="empty sequence"):
- getattr(obj, arg_op)(skipna=False)
-
- # argmin/max
+ klass = index_or_series
+ arg_op = "arg" + opname if klass is Index else "idx" + opname
+
+ obj = klass([np.nan, 2.0])
+ assert getattr(obj, opname)() == 2.0
+
+ obj = klass([np.nan])
+ assert pd.isna(getattr(obj, opname)())
+ assert pd.isna(getattr(obj, opname)(skipna=False))
+
+ obj = klass([], dtype=object)
+ assert pd.isna(getattr(obj, opname)())
+ assert pd.isna(getattr(obj, opname)(skipna=False))
+
+ obj = klass([pd.NaT, datetime(2011, 11, 1)])
+ # check DatetimeIndex monotonic path
+ assert getattr(obj, opname)() == datetime(2011, 11, 1)
+ assert getattr(obj, opname)(skipna=False) is pd.NaT
+
+ assert getattr(obj, arg_op)() == 1
+ result = getattr(obj, arg_op)(skipna=False)
+ if klass is Series:
+ assert np.isnan(result)
+ else:
+ assert result == -1
+
+ obj = klass([pd.NaT, datetime(2011, 11, 1), pd.NaT])
+ # check DatetimeIndex non-monotonic path
+ assert getattr(obj, opname)(), datetime(2011, 11, 1)
+ assert getattr(obj, opname)(skipna=False) is pd.NaT
+
+ assert getattr(obj, arg_op)() == 1
+ result = getattr(obj, arg_op)(skipna=False)
+ if klass is Series:
+ assert np.isnan(result)
+ else:
+ assert result == -1
+
+ @pytest.mark.parametrize("opname", ["max", "min"])
+ @pytest.mark.parametrize("dtype", ["M8[ns]", "datetime64[ns, UTC]"])
+ def test_nanops_empty_object(self, opname, index_or_series, dtype):
+ klass = index_or_series
+ arg_op = "arg" + opname if klass is Index else "idx" + opname
+
+ obj = klass([], dtype=dtype)
+
+ assert getattr(obj, opname)() is pd.NaT
+ assert getattr(obj, opname)(skipna=False) is pd.NaT
+
+ with pytest.raises(ValueError, match="empty sequence"):
+ getattr(obj, arg_op)()
+ with pytest.raises(ValueError, match="empty sequence"):
+ getattr(obj, arg_op)(skipna=False)
+
+ def test_argminmax(self):
obj = Index(np.arange(5, dtype="int64"))
assert obj.argmin() == 0
assert obj.argmax() == 4
@@ -224,16 +228,17 @@ def test_minmax_timedelta64(self):
assert idx.argmin() == 0
assert idx.argmax() == 2
- for op in ["min", "max"]:
- # Return NaT
- obj = TimedeltaIndex([])
- assert pd.isna(getattr(obj, op)())
+ @pytest.mark.parametrize("op", ["min", "max"])
+ def test_minmax_timedelta_empty_or_na(self, op):
+ # Return NaT
+ obj = TimedeltaIndex([])
+ assert getattr(obj, op)() is pd.NaT
- obj = TimedeltaIndex([pd.NaT])
- assert pd.isna(getattr(obj, op)())
+ obj = TimedeltaIndex([pd.NaT])
+ assert getattr(obj, op)() is pd.NaT
- obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT])
- assert pd.isna(getattr(obj, op)())
+ obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT])
+ assert getattr(obj, op)() is pd.NaT
def test_numpy_minmax_timedelta64(self):
td = timedelta_range("16815 days", "16820 days", freq="D")
| Some more parameterizing / splitting up of tests | https://api.github.com/repos/pandas-dev/pandas/pulls/32035 | 2020-02-16T01:27:07Z | 2020-02-17T20:03:06Z | 2020-02-17T20:03:06Z | 2020-02-17T20:05:00Z |
CLN: 29547 replace old string formatting | diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index ab3ee5bbcdc3a..b11736248c12a 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -1127,8 +1127,8 @@ def __arrow_array__(self, type=None):
subtype = pyarrow.from_numpy_dtype(self.dtype.subtype)
except TypeError:
raise TypeError(
- "Conversion to arrow with subtype '{}' "
- "is not supported".format(self.dtype.subtype)
+ f"Conversion to arrow with subtype '{self.dtype.subtype}' "
+ "is not supported"
)
interval_type = ArrowIntervalType(subtype, self.closed)
storage_array = pyarrow.StructArray.from_arrays(
@@ -1157,14 +1157,12 @@ def __arrow_array__(self, type=None):
if not type.equals(interval_type):
raise TypeError(
"Not supported to convert IntervalArray to type with "
- "different 'subtype' ({0} vs {1}) and 'closed' ({2} vs {3}) "
- "attributes".format(
- self.dtype.subtype, type.subtype, self.closed, type.closed
- )
+ f"different 'subtype' ({self.dtype.subtype} vs {type.subtype}) "
+ f"and 'closed' ({self.closed} vs {type.closed}) attributes"
)
else:
raise TypeError(
- "Not supported to convert IntervalArray to '{0}' type".format(type)
+ f"Not supported to convert IntervalArray to '{type}' type"
)
return pyarrow.ExtensionArray.from_storage(interval_type, storage_array)
diff --git a/pandas/core/util/hashing.py b/pandas/core/util/hashing.py
index 160d328ec16ec..d9c8611c94cdb 100644
--- a/pandas/core/util/hashing.py
+++ b/pandas/core/util/hashing.py
@@ -294,7 +294,7 @@ def hash_array(
elif issubclass(dtype.type, (np.datetime64, np.timedelta64)):
vals = vals.view("i8").astype("u8", copy=False)
elif issubclass(dtype.type, np.number) and dtype.itemsize <= 8:
- vals = vals.view("u{}".format(vals.dtype.itemsize)).astype("u8")
+ vals = vals.view(f"u{vals.dtype.itemsize}").astype("u8")
else:
# With repeated values, its MUCH faster to categorize object dtypes,
# then hash and rename categories. We allow skipping the categorization
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 0693c083c9ddc..b5ddd15c1312a 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -187,7 +187,7 @@ def _get_footer(self) -> str:
if self.length:
if footer:
footer += ", "
- footer += "Length: {length}".format(length=len(self.categorical))
+ footer += f"Length: {len(self.categorical)}"
level_info = self.categorical._repr_categories_info()
@@ -217,7 +217,6 @@ def to_string(self) -> str:
fmt_values = self._get_formatted_values()
- fmt_values = ["{i}".format(i=i) for i in fmt_values]
fmt_values = [i.strip() for i in fmt_values]
values = ", ".join(fmt_values)
result = ["[" + values + "]"]
@@ -301,28 +300,26 @@ def _get_footer(self) -> str:
assert isinstance(
self.series.index, (ABCDatetimeIndex, ABCPeriodIndex, ABCTimedeltaIndex)
)
- footer += "Freq: {freq}".format(freq=self.series.index.freqstr)
+ footer += f"Freq: {self.series.index.freqstr}"
if self.name is not False and name is not None:
if footer:
footer += ", "
series_name = pprint_thing(name, escape_chars=("\t", "\r", "\n"))
- footer += (
- ("Name: {sname}".format(sname=series_name)) if name is not None else ""
- )
+ footer += f"Name: {series_name}"
if self.length is True or (self.length == "truncate" and self.truncate_v):
if footer:
footer += ", "
- footer += "Length: {length}".format(length=len(self.series))
+ footer += f"Length: {len(self.series)}"
if self.dtype is not False and self.dtype is not None:
- name = getattr(self.tr_series.dtype, "name", None)
- if name:
+ dtype_name = getattr(self.tr_series.dtype, "name", None)
+ if dtype_name:
if footer:
footer += ", "
- footer += "dtype: {typ}".format(typ=pprint_thing(name))
+ footer += f"dtype: {pprint_thing(dtype_name)}"
# level infos are added to the end and in a new line, like it is done
# for Categoricals
@@ -359,9 +356,7 @@ def to_string(self) -> str:
footer = self._get_footer()
if len(series) == 0:
- return "{name}([], {footer})".format(
- name=type(self.series).__name__, footer=footer
- )
+ return f"{type(self.series).__name__}([], {footer})"
fmt_index, have_header = self._get_formatted_index()
fmt_values = self._get_formatted_values()
@@ -584,10 +579,8 @@ def __init__(
self.formatters = formatters
else:
raise ValueError(
- (
- "Formatters length({flen}) should match "
- "DataFrame number of columns({dlen})"
- ).format(flen=len(formatters), dlen=len(frame.columns))
+ f"Formatters length({len(formatters)}) should match "
+ f"DataFrame number of columns({len(frame.columns)})"
)
self.na_rep = na_rep
self.decimal = decimal
@@ -816,10 +809,10 @@ def write_result(self, buf: IO[str]) -> None:
frame = self.frame
if len(frame.columns) == 0 or len(frame.index) == 0:
- info_line = "Empty {name}\nColumns: {col}\nIndex: {idx}".format(
- name=type(self.frame).__name__,
- col=pprint_thing(frame.columns),
- idx=pprint_thing(frame.index),
+ info_line = (
+ f"Empty {type(self.frame).__name__}\n"
+ f"Columns: {pprint_thing(frame.columns)}\n"
+ f"Index: {pprint_thing(frame.index)}"
)
text = info_line
else:
@@ -865,11 +858,7 @@ def write_result(self, buf: IO[str]) -> None:
buf.writelines(text)
if self.should_show_dimensions:
- buf.write(
- "\n\n[{nrows} rows x {ncols} columns]".format(
- nrows=len(frame), ncols=len(frame.columns)
- )
- )
+ buf.write(f"\n\n[{len(frame)} rows x {len(frame.columns)} columns]")
def _join_multiline(self, *args) -> str:
lwidth = self.line_width
@@ -1074,7 +1063,7 @@ def _get_formatted_index(self, frame: "DataFrame") -> List[str]:
# empty space for columns
if self.show_col_idx_names:
- col_header = ["{x}".format(x=x) for x in self._get_column_name_list()]
+ col_header = [str(x) for x in self._get_column_name_list()]
else:
col_header = [""] * columns.nlevels
@@ -1209,10 +1198,8 @@ def _format_strings(self) -> List[str]:
if self.float_format is None:
float_format = get_option("display.float_format")
if float_format is None:
- fmt_str = "{{x: .{prec:d}g}}".format(
- prec=get_option("display.precision")
- )
- float_format = lambda x: fmt_str.format(x=x)
+ precision = get_option("display.precision")
+ float_format = lambda x: f"{x: .{precision:d}g}"
else:
float_format = self.float_format
@@ -1238,10 +1225,10 @@ def _format(x):
pass
return self.na_rep
elif isinstance(x, PandasObject):
- return "{x}".format(x=x)
+ return str(x)
else:
# object dtype
- return "{x}".format(x=formatter(x))
+ return str(formatter(x))
vals = self.values
if isinstance(vals, Index):
@@ -1257,7 +1244,7 @@ def _format(x):
fmt_values = []
for i, v in enumerate(vals):
if not is_float_type[i] and leading_space:
- fmt_values.append(" {v}".format(v=_format(v)))
+ fmt_values.append(f" {_format(v)}")
elif is_float_type[i]:
fmt_values.append(float_format(v))
else:
@@ -1437,7 +1424,7 @@ def _format_strings(self) -> List[str]:
class IntArrayFormatter(GenericArrayFormatter):
def _format_strings(self) -> List[str]:
- formatter = self.formatter or (lambda x: "{x: d}".format(x=x))
+ formatter = self.formatter or (lambda x: f"{x: d}")
fmt_values = [formatter(x) for x in self.values]
return fmt_values
@@ -1716,7 +1703,7 @@ def _formatter(x):
x = Timedelta(x)
result = x._repr_base(format=format)
if box:
- result = "'{res}'".format(res=result)
+ result = f"'{result}'"
return result
return _formatter
@@ -1880,16 +1867,16 @@ def __call__(self, num: Union[int, float]) -> str:
prefix = self.ENG_PREFIXES[int_pow10]
else:
if int_pow10 < 0:
- prefix = "E-{pow10:02d}".format(pow10=-int_pow10)
+ prefix = f"E-{-int_pow10:02d}"
else:
- prefix = "E+{pow10:02d}".format(pow10=int_pow10)
+ prefix = f"E+{int_pow10:02d}"
mant = sign * dnum / (10 ** pow10)
if self.accuracy is None: # pragma: no cover
format_str = "{mant: g}{prefix}"
else:
- format_str = "{{mant: .{acc:d}f}}{{prefix}}".format(acc=self.accuracy)
+ format_str = f"{{mant: .{self.accuracy:d}f}}{{prefix}}"
formatted = format_str.format(mant=mant, prefix=prefix)
diff --git a/pandas/io/formats/html.py b/pandas/io/formats/html.py
index e3161415fe2bc..585e1af3dbc01 100644
--- a/pandas/io/formats/html.py
+++ b/pandas/io/formats/html.py
@@ -56,7 +56,7 @@ def __init__(
self.table_id = self.fmt.table_id
self.render_links = self.fmt.render_links
if isinstance(self.fmt.col_space, int):
- self.fmt.col_space = "{colspace}px".format(colspace=self.fmt.col_space)
+ self.fmt.col_space = f"{self.fmt.col_space}px"
@property
def show_row_idx_names(self) -> bool:
@@ -124,7 +124,7 @@ def write_th(
"""
if header and self.fmt.col_space is not None:
tags = tags or ""
- tags += 'style="min-width: {colspace};"'.format(colspace=self.fmt.col_space)
+ tags += f'style="min-width: {self.fmt.col_space};"'
self._write_cell(s, kind="th", indent=indent, tags=tags)
@@ -135,9 +135,9 @@ def _write_cell(
self, s: Any, kind: str = "td", indent: int = 0, tags: Optional[str] = None
) -> None:
if tags is not None:
- start_tag = "<{kind} {tags}>".format(kind=kind, tags=tags)
+ start_tag = f"<{kind} {tags}>"
else:
- start_tag = "<{kind}>".format(kind=kind)
+ start_tag = f"<{kind}>"
if self.escape:
# escape & first to prevent double escaping of &
@@ -149,17 +149,12 @@ def _write_cell(
if self.render_links and is_url(rs):
rs_unescaped = pprint_thing(s, escape_chars={}).strip()
- start_tag += '<a href="{url}" target="_blank">'.format(url=rs_unescaped)
+ start_tag += f'<a href="{rs_unescaped}" target="_blank">'
end_a = "</a>"
else:
end_a = ""
- self.write(
- "{start}{rs}{end_a}</{kind}>".format(
- start=start_tag, rs=rs, end_a=end_a, kind=kind
- ),
- indent,
- )
+ self.write(f"{start_tag}{rs}{end_a}</{kind}>", indent)
def write_tr(
self,
@@ -177,7 +172,7 @@ def write_tr(
if align is None:
self.write("<tr>", indent)
else:
- self.write('<tr style="text-align: {align};">'.format(align=align), indent)
+ self.write(f'<tr style="text-align: {align};">', indent)
indent += indent_delta
for i, s in enumerate(line):
@@ -196,9 +191,7 @@ def render(self) -> List[str]:
if self.should_show_dimensions:
by = chr(215) # ×
self.write(
- "<p>{rows} rows {by} {cols} columns</p>".format(
- rows=len(self.frame), by=by, cols=len(self.frame.columns)
- )
+ f"<p>{len(self.frame)} rows {by} {len(self.frame.columns)} columns</p>"
)
return self.elements
@@ -224,12 +217,10 @@ def _write_table(self, indent: int = 0) -> None:
if self.table_id is None:
id_section = ""
else:
- id_section = ' id="{table_id}"'.format(table_id=self.table_id)
+ id_section = f' id="{self.table_id}"'
self.write(
- '<table border="{border}" class="{cls}"{id_section}>'.format(
- border=self.border, cls=" ".join(_classes), id_section=id_section
- ),
+ f'<table border="{self.border}" class="{" ".join(_classes)}"{id_section}>',
indent,
)
diff --git a/pandas/io/formats/latex.py b/pandas/io/formats/latex.py
index 935762598f78a..3a3ca84642d51 100644
--- a/pandas/io/formats/latex.py
+++ b/pandas/io/formats/latex.py
@@ -58,10 +58,10 @@ def write_result(self, buf: IO[str]) -> None:
"""
# string representation of the columns
if len(self.frame.columns) == 0 or len(self.frame.index) == 0:
- info_line = "Empty {name}\nColumns: {col}\nIndex: {idx}".format(
- name=type(self.frame).__name__,
- col=self.frame.columns,
- idx=self.frame.index,
+ info_line = (
+ f"Empty {type(self.frame).__name__}\n"
+ f"Columns: {self.frame.columns}\n"
+ f"Index: {self.frame.index}"
)
strcols = [[info_line]]
else:
@@ -140,8 +140,8 @@ def pad_empties(x):
buf.write("\\endhead\n")
buf.write("\\midrule\n")
buf.write(
- "\\multicolumn{{{n}}}{{r}}{{{{Continued on next "
- "page}}}} \\\\\n".format(n=len(row))
+ f"\\multicolumn{{{len(row)}}}{{r}}"
+ "{{Continued on next page}} \\\\\n"
)
buf.write("\\midrule\n")
buf.write("\\endfoot\n\n")
@@ -171,7 +171,7 @@ def pad_empties(x):
if self.bold_rows and self.fmt.index:
# bold row labels
crow = [
- "\\textbf{{{x}}}".format(x=x)
+ f"\\textbf{{{x}}}"
if j < ilevels and x.strip() not in ["", "{}"]
else x
for j, x in enumerate(crow)
@@ -210,9 +210,8 @@ def append_col():
# write multicolumn if needed
if ncol > 1:
row2.append(
- "\\multicolumn{{{ncol:d}}}{{{fmt:s}}}{{{txt:s}}}".format(
- ncol=ncol, fmt=self.multicolumn_format, txt=coltext.strip()
- )
+ f"\\multicolumn{{{ncol:d}}}{{{self.multicolumn_format}}}"
+ f"{{{coltext.strip()}}}"
)
# don't modify where not needed
else:
@@ -255,9 +254,7 @@ def _format_multirow(
break
if nrow > 1:
# overwrite non-multirow entry
- row[j] = "\\multirow{{{nrow:d}}}{{*}}{{{row:s}}}".format(
- nrow=nrow, row=row[j].strip()
- )
+ row[j] = f"\\multirow{{{nrow:d}}}{{*}}{{{row[j].strip()}}}"
# save when to end the current block with \cline
self.clinebuf.append([i + nrow - 1, j + 1])
return row
@@ -268,7 +265,7 @@ def _print_cline(self, buf: IO[str], i: int, icol: int) -> None:
"""
for cl in self.clinebuf:
if cl[0] == i:
- buf.write("\\cline{{{cl:d}-{icol:d}}}\n".format(cl=cl[1], icol=icol))
+ buf.write(f"\\cline{{{cl[1]:d}-{icol:d}}}\n")
# remove entries that have been written to buffer
self.clinebuf = [x for x in self.clinebuf if x[0] != i]
@@ -292,19 +289,19 @@ def _write_tabular_begin(self, buf, column_format: str):
if self.caption is None:
caption_ = ""
else:
- caption_ = "\n\\caption{{{}}}".format(self.caption)
+ caption_ = f"\n\\caption{{{self.caption}}}"
if self.label is None:
label_ = ""
else:
- label_ = "\n\\label{{{}}}".format(self.label)
+ label_ = f"\n\\label{{{self.label}}}"
- buf.write("\\begin{{table}}\n\\centering{}{}\n".format(caption_, label_))
+ buf.write(f"\\begin{{table}}\n\\centering{caption_}{label_}\n")
else:
# then write output only in a tabular environment
pass
- buf.write("\\begin{{tabular}}{{{fmt}}}\n".format(fmt=column_format))
+ buf.write(f"\\begin{{tabular}}{{{column_format}}}\n")
def _write_tabular_end(self, buf):
"""
@@ -340,18 +337,18 @@ def _write_longtable_begin(self, buf, column_format: str):
<https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g 'rcl'
for 3 columns
"""
- buf.write("\\begin{{longtable}}{{{fmt}}}\n".format(fmt=column_format))
+ buf.write(f"\\begin{{longtable}}{{{column_format}}}\n")
if self.caption is not None or self.label is not None:
if self.caption is None:
pass
else:
- buf.write("\\caption{{{}}}".format(self.caption))
+ buf.write(f"\\caption{{{self.caption}}}")
if self.label is None:
pass
else:
- buf.write("\\label{{{}}}".format(self.label))
+ buf.write(f"\\label{{{self.label}}}")
# a double-backslash is required at the end of the line
# as discussed here:
diff --git a/pandas/io/formats/printing.py b/pandas/io/formats/printing.py
index 13b18a0b5fb6f..36e774305b577 100644
--- a/pandas/io/formats/printing.py
+++ b/pandas/io/formats/printing.py
@@ -229,7 +229,7 @@ def as_escaped_string(
max_seq_items=max_seq_items,
)
elif isinstance(thing, str) and quote_strings:
- result = "'{thing}'".format(thing=as_escaped_string(thing))
+ result = f"'{as_escaped_string(thing)}'"
else:
result = as_escaped_string(thing)
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index d7eb69d3a6048..8a3ad6cb45b57 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -3605,8 +3605,8 @@ def get_rows(self, infer_nrows, skiprows=None):
def detect_colspecs(self, infer_nrows=100, skiprows=None):
# Regex escape the delimiters
- delimiters = "".join(r"\{}".format(x) for x in self.delimiter)
- pattern = re.compile("([^{}]+)".format(delimiters))
+ delimiters = "".join(fr"\{x}" for x in self.delimiter)
+ pattern = re.compile(f"([^{delimiters}]+)")
rows = self.get_rows(infer_nrows, skiprows)
if not rows:
raise EmptyDataError("No rows from which to infer column width")
diff --git a/pandas/tests/arrays/categorical/test_dtypes.py b/pandas/tests/arrays/categorical/test_dtypes.py
index 19746d7d72162..9922a8863ebc2 100644
--- a/pandas/tests/arrays/categorical/test_dtypes.py
+++ b/pandas/tests/arrays/categorical/test_dtypes.py
@@ -92,22 +92,20 @@ def test_codes_dtypes(self):
result = Categorical(["foo", "bar", "baz"])
assert result.codes.dtype == "int8"
- result = Categorical(["foo{i:05d}".format(i=i) for i in range(400)])
+ result = Categorical([f"foo{i:05d}" for i in range(400)])
assert result.codes.dtype == "int16"
- result = Categorical(["foo{i:05d}".format(i=i) for i in range(40000)])
+ result = Categorical([f"foo{i:05d}" for i in range(40000)])
assert result.codes.dtype == "int32"
# adding cats
result = Categorical(["foo", "bar", "baz"])
assert result.codes.dtype == "int8"
- result = result.add_categories(["foo{i:05d}".format(i=i) for i in range(400)])
+ result = result.add_categories([f"foo{i:05d}" for i in range(400)])
assert result.codes.dtype == "int16"
# removing cats
- result = result.remove_categories(
- ["foo{i:05d}".format(i=i) for i in range(300)]
- )
+ result = result.remove_categories([f"foo{i:05d}" for i in range(300)])
assert result.codes.dtype == "int8"
@pytest.mark.parametrize("ordered", [True, False])
diff --git a/pandas/tests/arrays/categorical/test_operators.py b/pandas/tests/arrays/categorical/test_operators.py
index 0c830c65e0f8b..6ea003c122eea 100644
--- a/pandas/tests/arrays/categorical/test_operators.py
+++ b/pandas/tests/arrays/categorical/test_operators.py
@@ -338,7 +338,7 @@ def test_compare_unordered_different_order(self):
def test_numeric_like_ops(self):
df = DataFrame({"value": np.random.randint(0, 10000, 100)})
- labels = ["{0} - {1}".format(i, i + 499) for i in range(0, 10000, 500)]
+ labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
cat_labels = Categorical(labels, labels)
df = df.sort_values(by=["value"], ascending=True)
@@ -353,9 +353,7 @@ def test_numeric_like_ops(self):
("__mul__", r"\*"),
("__truediv__", "/"),
]:
- msg = r"Series cannot perform the operation {}|unsupported operand".format(
- str_rep
- )
+ msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
with pytest.raises(TypeError, match=msg):
getattr(df, op)(df)
@@ -363,7 +361,7 @@ def test_numeric_like_ops(self):
# min/max)
s = df["value_group"]
for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]:
- msg = "Categorical cannot perform the operation {}".format(op)
+ msg = f"Categorical cannot perform the operation {op}"
with pytest.raises(TypeError, match=msg):
getattr(s, op)(numeric_only=False)
@@ -383,9 +381,7 @@ def test_numeric_like_ops(self):
("__mul__", r"\*"),
("__truediv__", "/"),
]:
- msg = r"Series cannot perform the operation {}|unsupported operand".format(
- str_rep
- )
+ msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
with pytest.raises(TypeError, match=msg):
getattr(s, op)(2)
| I splitted PR #31844 in batches, this is the **last**
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ x ] Ref #29547
| https://api.github.com/repos/pandas-dev/pandas/pulls/32034 | 2020-02-16T00:34:51Z | 2020-02-20T23:32:42Z | 2020-02-20T23:32:41Z | 2020-02-21T01:29:53Z |
CLN: 29547 replace old string formatting 8 | diff --git a/pandas/tests/test_downstream.py b/pandas/tests/test_downstream.py
index 02898988ca8aa..122ef1f47968e 100644
--- a/pandas/tests/test_downstream.py
+++ b/pandas/tests/test_downstream.py
@@ -19,7 +19,7 @@ def import_module(name):
try:
return importlib.import_module(name)
except ModuleNotFoundError: # noqa
- pytest.skip("skipping as {} not available".format(name))
+ pytest.skip(f"skipping as {name} not available")
@pytest.fixture
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index b377ca2869bd3..efaedfad1e093 100644
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -896,10 +896,10 @@ def test_stack_unstack_unordered_multiindex(self):
values = np.arange(5)
data = np.vstack(
[
- ["b{}".format(x) for x in values], # b0, b1, ..
- ["a{}".format(x) for x in values],
+ [f"b{x}" for x in values], # b0, b1, ..
+ [f"a{x}" for x in values], # a0, a1, ..
]
- ) # a0, a1, ..
+ )
df = pd.DataFrame(data.T, columns=["b", "a"])
df.columns.name = "first"
second_level_dict = {"x": df}
diff --git a/pandas/tests/tools/test_numeric.py b/pandas/tests/tools/test_numeric.py
index 2fd39d5a7b703..19385e797467c 100644
--- a/pandas/tests/tools/test_numeric.py
+++ b/pandas/tests/tools/test_numeric.py
@@ -308,7 +308,7 @@ def test_really_large_in_arr_consistent(large_val, signed, multiple_elts, errors
if errors in (None, "raise"):
index = int(multiple_elts)
- msg = "Integer out of range. at position {index}".format(index=index)
+ msg = f"Integer out of range. at position {index}"
with pytest.raises(ValueError, match=msg):
to_numeric(arr, **kwargs)
diff --git a/pandas/tests/tslibs/test_parse_iso8601.py b/pandas/tests/tslibs/test_parse_iso8601.py
index a58f227c20c7f..1c01e826d9794 100644
--- a/pandas/tests/tslibs/test_parse_iso8601.py
+++ b/pandas/tests/tslibs/test_parse_iso8601.py
@@ -51,7 +51,7 @@ def test_parsers_iso8601(date_str, exp):
],
)
def test_parsers_iso8601_invalid(date_str):
- msg = 'Error parsing datetime string "{s}"'.format(s=date_str)
+ msg = f'Error parsing datetime string "{date_str}"'
with pytest.raises(ValueError, match=msg):
tslib._test_parse_iso8601(date_str)
diff --git a/pandas/tests/window/moments/test_moments_rolling.py b/pandas/tests/window/moments/test_moments_rolling.py
index fd18d37ab13b6..f3a14971ef2e7 100644
--- a/pandas/tests/window/moments/test_moments_rolling.py
+++ b/pandas/tests/window/moments/test_moments_rolling.py
@@ -860,7 +860,7 @@ def get_result(obj, window, min_periods=None, center=False):
tm.assert_series_equal(result, expected)
# shifter index
- s = ["x{x:d}".format(x=x) for x in range(12)]
+ s = [f"x{x:d}" for x in range(12)]
if has_min_periods:
minp = 10
@@ -1437,13 +1437,9 @@ def test_rolling_median_memory_error(self):
def test_rolling_min_max_numeric_types(self):
# GH12373
- types_test = [np.dtype("f{}".format(width)) for width in [4, 8]]
+ types_test = [np.dtype(f"f{width}") for width in [4, 8]]
types_test.extend(
- [
- np.dtype("{}{}".format(sign, width))
- for width in [1, 2, 4, 8]
- for sign in "ui"
- ]
+ [np.dtype(f"{sign}{width}") for width in [1, 2, 4, 8] for sign in "ui"]
)
for data_type in types_test:
# Just testing that these don't throw exceptions and that
| I splitted PR #31844 in batches, this is the eighth
For this PR I ran the command `grep -l -R -e '%s' -e '%d' -e '\.format(' --include=*.{py,pyx} pandas/` and checked all the files that were returned for `.format(` and changed the old string format for the corresponding `fstrings` to attempt a full clean of, [#29547](https://github.com/pandas-dev/pandas/issues/29547). I may have missed something so is a good idea to double check just in case
- [ x ] tests added / passed
- [ x ] passes `black pandas`
- [ x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | https://api.github.com/repos/pandas-dev/pandas/pulls/32032 | 2020-02-15T19:52:37Z | 2020-02-15T23:24:24Z | 2020-02-15T23:24:24Z | 2020-02-21T01:29:55Z |
CI: change np-dev xfails to not strict | diff --git a/pandas/tests/frame/test_cumulative.py b/pandas/tests/frame/test_cumulative.py
index 2466547e2948b..486cbfb2761e0 100644
--- a/pandas/tests/frame/test_cumulative.py
+++ b/pandas/tests/frame/test_cumulative.py
@@ -75,7 +75,9 @@ def test_cumprod(self, datetime_frame):
df.cumprod(1)
@pytest.mark.xfail(
- _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
)
def test_cummin(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
@@ -101,7 +103,9 @@ def test_cummin(self, datetime_frame):
assert np.shape(cummin_xs) == np.shape(datetime_frame)
@pytest.mark.xfail(
- _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
)
def test_cummax(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 8830b84a52421..176c0272ca527 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -687,7 +687,9 @@ def test_numpy_compat(func):
@pytest.mark.xfail(
- _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
)
def test_cummin_cummax():
# GH 15048
diff --git a/pandas/tests/scalar/timedelta/test_arithmetic.py b/pandas/tests/scalar/timedelta/test_arithmetic.py
index f0ad5fa70471b..230a14aeec60a 100644
--- a/pandas/tests/scalar/timedelta/test_arithmetic.py
+++ b/pandas/tests/scalar/timedelta/test_arithmetic.py
@@ -386,6 +386,7 @@ def test_td_div_numeric_scalar(self):
marks=pytest.mark.xfail(
_is_numpy_dev,
reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
),
),
float("nan"),
diff --git a/pandas/tests/series/test_cumulative.py b/pandas/tests/series/test_cumulative.py
index b0065992b850a..0cb1c038478f5 100644
--- a/pandas/tests/series/test_cumulative.py
+++ b/pandas/tests/series/test_cumulative.py
@@ -39,7 +39,9 @@ def test_cumprod(self, datetime_series):
_check_accum_op("cumprod", datetime_series)
@pytest.mark.xfail(
- _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
)
def test_cummin(self, datetime_series):
tm.assert_numpy_array_equal(
@@ -54,7 +56,9 @@ def test_cummin(self, datetime_series):
tm.assert_series_equal(result, expected)
@pytest.mark.xfail(
- _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
)
def test_cummax(self, datetime_series):
tm.assert_numpy_array_equal(
| follow-up to #32025 | https://api.github.com/repos/pandas-dev/pandas/pulls/32031 | 2020-02-15T19:52:07Z | 2020-02-16T17:43:56Z | 2020-02-16T17:43:56Z | 2020-02-18T10:56:10Z |
CLN: remove unused from MultiIndex | diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 02e11c0e71cbe..0a79df0cc9744 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -58,7 +58,6 @@
indexer_from_factorized,
lexsort_indexer,
)
-from pandas.core.util.hashing import hash_tuple, hash_tuples
from pandas.io.formats.printing import (
format_object_attrs,
@@ -247,6 +246,7 @@ class MultiIndex(Index):
rename = Index.set_names
_tuples = None
+ sortorder: Optional[int]
# --------------------------------------------------------------------
# Constructors
@@ -1430,55 +1430,11 @@ def is_monotonic_decreasing(self) -> bool:
# monotonic decreasing if and only if reverse is monotonic increasing
return self[::-1].is_monotonic_increasing
- @cache_readonly
- def _have_mixed_levels(self):
- """ return a boolean list indicated if we have mixed levels """
- return ["mixed" in l for l in self._inferred_type_levels]
-
@cache_readonly
def _inferred_type_levels(self):
""" return a list of the inferred types, one for each level """
return [i.inferred_type for i in self.levels]
- @cache_readonly
- def _hashed_values(self):
- """ return a uint64 ndarray of my hashed values """
- return hash_tuples(self)
-
- def _hashed_indexing_key(self, key):
- """
- validate and return the hash for the provided key
-
- *this is internal for use for the cython routines*
-
- Parameters
- ----------
- key : string or tuple
-
- Returns
- -------
- np.uint64
-
- Notes
- -----
- we need to stringify if we have mixed levels
- """
- if not isinstance(key, tuple):
- return hash_tuples(key)
-
- if not len(key) == self.nlevels:
- raise KeyError
-
- def f(k, stringify):
- if stringify and not isinstance(k, str):
- k = str(k)
- return k
-
- key = tuple(
- f(k, stringify) for k, stringify in zip(key, self._have_mixed_levels)
- )
- return hash_tuple(key)
-
@Appender(Index.duplicated.__doc__)
def duplicated(self, keep="first"):
shape = map(len, self.levels)
@@ -1858,27 +1814,6 @@ def __reduce__(self):
)
return ibase._new_Index, (type(self), d), None
- def __setstate__(self, state):
- """Necessary for making this object picklable"""
- if isinstance(state, dict):
- levels = state.get("levels")
- codes = state.get("codes")
- sortorder = state.get("sortorder")
- names = state.get("names")
-
- elif isinstance(state, tuple):
-
- nd_state, own_state = state
- levels, codes, sortorder, names = own_state
-
- self._set_levels([Index(x) for x in levels], validate=False)
- self._set_codes(codes)
- new_codes = self._verify_integrity()
- self._set_codes(new_codes)
- self._set_names(names)
- self.sortorder = sortorder
- self._reset_identity()
-
# --------------------------------------------------------------------
def __getitem__(self, key):
diff --git a/pandas/tests/util/test_hashing.py b/pandas/tests/util/test_hashing.py
index c856585f20138..6411b9ab654f1 100644
--- a/pandas/tests/util/test_hashing.py
+++ b/pandas/tests/util/test_hashing.py
@@ -178,23 +178,6 @@ def test_multiindex_objects():
assert mi.equals(recons)
assert Index(mi.values).equals(Index(recons.values))
- # _hashed_values and hash_pandas_object(..., index=False) equivalency.
- expected = hash_pandas_object(mi, index=False).values
- result = mi._hashed_values
-
- tm.assert_numpy_array_equal(result, expected)
-
- expected = hash_pandas_object(recons, index=False).values
- result = recons._hashed_values
-
- tm.assert_numpy_array_equal(result, expected)
-
- expected = mi._hashed_values
- result = recons._hashed_values
-
- # Values should match, but in different order.
- tm.assert_numpy_array_equal(np.sort(result), np.sort(expected))
-
@pytest.mark.parametrize(
"obj",
| https://api.github.com/repos/pandas-dev/pandas/pulls/32030 | 2020-02-15T19:39:51Z | 2020-02-18T12:38:28Z | 2020-02-18T12:38:28Z | 2020-02-18T15:30:59Z | |
CLN: GH29547 replace old string formatting | diff --git a/pandas/tests/arrays/categorical/test_analytics.py b/pandas/tests/arrays/categorical/test_analytics.py
index 90fcf12093909..0ff7d3e59abb3 100644
--- a/pandas/tests/arrays/categorical/test_analytics.py
+++ b/pandas/tests/arrays/categorical/test_analytics.py
@@ -15,10 +15,10 @@ class TestCategoricalAnalytics:
def test_min_max_not_ordered_raises(self, aggregation):
# unordered cats have no min/max
cat = Categorical(["a", "b", "c", "d"], ordered=False)
- msg = "Categorical is not ordered for operation {}"
+ msg = f"Categorical is not ordered for operation {aggregation}"
agg_func = getattr(cat, aggregation)
- with pytest.raises(TypeError, match=msg.format(aggregation)):
+ with pytest.raises(TypeError, match=msg):
agg_func()
def test_min_max_ordered(self):
| Hi there! This is my first contribution. Please let me know if there are any issues, thank you.
- [x] tests passed
- [x] passes `black pandas`
| https://api.github.com/repos/pandas-dev/pandas/pulls/32029 | 2020-02-15T18:27:21Z | 2020-02-15T19:05:32Z | 2020-02-15T19:05:32Z | 2020-02-15T20:10:57Z |
CLN: Clean groupby/test_function.py | diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 176c0272ca527..6205dfb87bbd0 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -26,6 +26,26 @@
from pandas.util import _test_decorators as td
+@pytest.fixture(
+ params=[np.int32, np.int64, np.float32, np.float64],
+ ids=["np.int32", "np.int64", "np.float32", "np.float64"],
+)
+def numpy_dtypes_for_minmax(request):
+ """
+ Fixture of numpy dtypes with min and max values used for testing
+ cummin and cummax
+ """
+ dtype = request.param
+ min_val = (
+ np.iinfo(dtype).min if np.dtype(dtype).kind == "i" else np.finfo(dtype).min
+ )
+ max_val = (
+ np.iinfo(dtype).max if np.dtype(dtype).kind == "i" else np.finfo(dtype).max
+ )
+
+ return (dtype, min_val, max_val)
+
+
@pytest.mark.parametrize("agg_func", ["any", "all"])
@pytest.mark.parametrize("skipna", [True, False])
@pytest.mark.parametrize(
@@ -174,11 +194,10 @@ def test_arg_passthru():
)
for attr in ["mean", "median"]:
- f = getattr(df.groupby("group"), attr)
- result = f()
+ result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns_numeric)
- result = f(numeric_only=False)
+ result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_frame_equal(result.reindex_like(expected), expected)
# TODO: min, max *should* handle
@@ -195,11 +214,10 @@ def test_arg_passthru():
]
)
for attr in ["min", "max"]:
- f = getattr(df.groupby("group"), attr)
- result = f()
+ result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns)
- result = f(numeric_only=False)
+ result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
expected_columns = Index(
@@ -215,29 +233,26 @@ def test_arg_passthru():
]
)
for attr in ["first", "last"]:
- f = getattr(df.groupby("group"), attr)
- result = f()
+ result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns)
- result = f(numeric_only=False)
+ result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
expected_columns = Index(["int", "float", "string", "category_int", "timedelta"])
- for attr in ["sum"]:
- f = getattr(df.groupby("group"), attr)
- result = f()
- tm.assert_index_equal(result.columns, expected_columns_numeric)
- result = f(numeric_only=False)
- tm.assert_index_equal(result.columns, expected_columns)
+ result = df.groupby("group").sum()
+ tm.assert_index_equal(result.columns, expected_columns_numeric)
+
+ result = df.groupby("group").sum(numeric_only=False)
+ tm.assert_index_equal(result.columns, expected_columns)
expected_columns = Index(["int", "float", "category_int"])
for attr in ["prod", "cumprod"]:
- f = getattr(df.groupby("group"), attr)
- result = f()
+ result = getattr(df.groupby("group"), attr)()
tm.assert_index_equal(result.columns, expected_columns_numeric)
- result = f(numeric_only=False)
+ result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
# like min, max, but don't include strings
@@ -245,22 +260,20 @@ def test_arg_passthru():
["int", "float", "category_int", "datetime", "datetimetz", "timedelta"]
)
for attr in ["cummin", "cummax"]:
- f = getattr(df.groupby("group"), attr)
- result = f()
+ result = getattr(df.groupby("group"), attr)()
# GH 15561: numeric_only=False set by default like min/max
tm.assert_index_equal(result.columns, expected_columns)
- result = f(numeric_only=False)
+ result = getattr(df.groupby("group"), attr)(numeric_only=False)
tm.assert_index_equal(result.columns, expected_columns)
expected_columns = Index(["int", "float", "category_int", "timedelta"])
- for attr in ["cumsum"]:
- f = getattr(df.groupby("group"), attr)
- result = f()
- tm.assert_index_equal(result.columns, expected_columns_numeric)
- result = f(numeric_only=False)
- tm.assert_index_equal(result.columns, expected_columns)
+ result = getattr(df.groupby("group"), "cumsum")()
+ tm.assert_index_equal(result.columns, expected_columns_numeric)
+
+ result = getattr(df.groupby("group"), "cumsum")(numeric_only=False)
+ tm.assert_index_equal(result.columns, expected_columns)
def test_non_cython_api():
@@ -691,59 +704,31 @@ def test_numpy_compat(func):
reason="https://github.com/pandas-dev/pandas/issues/31992",
strict=False,
)
-def test_cummin_cummax():
+def test_cummin(numpy_dtypes_for_minmax):
+ dtype = numpy_dtypes_for_minmax[0]
+ min_val = numpy_dtypes_for_minmax[1]
+
# GH 15048
- num_types = [np.int32, np.int64, np.float32, np.float64]
- num_mins = [
- np.iinfo(np.int32).min,
- np.iinfo(np.int64).min,
- np.finfo(np.float32).min,
- np.finfo(np.float64).min,
- ]
- num_max = [
- np.iinfo(np.int32).max,
- np.iinfo(np.int64).max,
- np.finfo(np.float32).max,
- np.finfo(np.float64).max,
- ]
base_df = pd.DataFrame(
{"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]}
)
expected_mins = [3, 3, 3, 2, 2, 2, 2, 1]
- expected_maxs = [3, 4, 4, 4, 2, 3, 3, 3]
- for dtype, min_val, max_val in zip(num_types, num_mins, num_max):
- df = base_df.astype(dtype)
+ df = base_df.astype(dtype)
- # cummin
- expected = pd.DataFrame({"B": expected_mins}).astype(dtype)
- result = df.groupby("A").cummin()
- tm.assert_frame_equal(result, expected)
- result = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame()
- tm.assert_frame_equal(result, expected)
-
- # Test cummin w/ min value for dtype
- df.loc[[2, 6], "B"] = min_val
- expected.loc[[2, 3, 6, 7], "B"] = min_val
- result = df.groupby("A").cummin()
- tm.assert_frame_equal(result, expected)
- expected = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame()
- tm.assert_frame_equal(result, expected)
-
- # cummax
- expected = pd.DataFrame({"B": expected_maxs}).astype(dtype)
- result = df.groupby("A").cummax()
- tm.assert_frame_equal(result, expected)
- result = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
- tm.assert_frame_equal(result, expected)
+ expected = pd.DataFrame({"B": expected_mins}).astype(dtype)
+ result = df.groupby("A").cummin()
+ tm.assert_frame_equal(result, expected)
+ result = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame()
+ tm.assert_frame_equal(result, expected)
- # Test cummax w/ max value for dtype
- df.loc[[2, 6], "B"] = max_val
- expected.loc[[2, 3, 6, 7], "B"] = max_val
- result = df.groupby("A").cummax()
- tm.assert_frame_equal(result, expected)
- expected = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
- tm.assert_frame_equal(result, expected)
+ # Test w/ min value for dtype
+ df.loc[[2, 6], "B"] = min_val
+ expected.loc[[2, 3, 6, 7], "B"] = min_val
+ result = df.groupby("A").cummin()
+ tm.assert_frame_equal(result, expected)
+ expected = df.groupby("A").B.apply(lambda x: x.cummin()).to_frame()
+ tm.assert_frame_equal(result, expected)
# Test nan in some values
base_df.loc[[0, 2, 4, 6], "B"] = np.nan
@@ -753,30 +738,80 @@ def test_cummin_cummax():
expected = base_df.groupby("A").B.apply(lambda x: x.cummin()).to_frame()
tm.assert_frame_equal(result, expected)
- expected = pd.DataFrame({"B": [np.nan, 4, np.nan, 4, np.nan, 3, np.nan, 3]})
- result = base_df.groupby("A").cummax()
- tm.assert_frame_equal(result, expected)
- expected = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
- tm.assert_frame_equal(result, expected)
+ # GH 15561
+ df = pd.DataFrame(dict(a=[1], b=pd.to_datetime(["2001"])))
+ expected = pd.Series(pd.to_datetime("2001"), index=[0], name="b")
+
+ result = df.groupby("a")["b"].cummin()
+ tm.assert_series_equal(expected, result)
+
+ # GH 15635
+ df = pd.DataFrame(dict(a=[1, 2, 1], b=[1, 2, 2]))
+ result = df.groupby("a").b.cummin()
+ expected = pd.Series([1, 2, 1], name="b")
+ tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+)
+def test_cummin_all_nan_column():
+ base_df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [np.nan] * 8})
- # Test nan in entire column
- base_df["B"] = np.nan
expected = pd.DataFrame({"B": [np.nan] * 8})
result = base_df.groupby("A").cummin()
tm.assert_frame_equal(expected, result)
result = base_df.groupby("A").B.apply(lambda x: x.cummin()).to_frame()
tm.assert_frame_equal(expected, result)
+
+
+@pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+)
+def test_cummax(numpy_dtypes_for_minmax):
+ dtype = numpy_dtypes_for_minmax[0]
+ max_val = numpy_dtypes_for_minmax[2]
+
+ # GH 15048
+ base_df = pd.DataFrame(
+ {"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]}
+ )
+ expected_maxs = [3, 4, 4, 4, 2, 3, 3, 3]
+
+ df = base_df.astype(dtype)
+
+ expected = pd.DataFrame({"B": expected_maxs}).astype(dtype)
+ result = df.groupby("A").cummax()
+ tm.assert_frame_equal(result, expected)
+ result = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
+ tm.assert_frame_equal(result, expected)
+
+ # Test w/ max value for dtype
+ df.loc[[2, 6], "B"] = max_val
+ expected.loc[[2, 3, 6, 7], "B"] = max_val
+ result = df.groupby("A").cummax()
+ tm.assert_frame_equal(result, expected)
+ expected = df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
+ tm.assert_frame_equal(result, expected)
+
+ # Test nan in some values
+ base_df.loc[[0, 2, 4, 6], "B"] = np.nan
+ expected = pd.DataFrame({"B": [np.nan, 4, np.nan, 4, np.nan, 3, np.nan, 3]})
result = base_df.groupby("A").cummax()
- tm.assert_frame_equal(expected, result)
- result = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
- tm.assert_frame_equal(expected, result)
+ tm.assert_frame_equal(result, expected)
+ expected = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
+ tm.assert_frame_equal(result, expected)
# GH 15561
df = pd.DataFrame(dict(a=[1], b=pd.to_datetime(["2001"])))
expected = pd.Series(pd.to_datetime("2001"), index=[0], name="b")
- for method in ["cummax", "cummin"]:
- result = getattr(df.groupby("a")["b"], method)()
- tm.assert_series_equal(expected, result)
+
+ result = df.groupby("a")["b"].cummax()
+ tm.assert_series_equal(expected, result)
# GH 15635
df = pd.DataFrame(dict(a=[1, 2, 1], b=[2, 1, 1]))
@@ -784,10 +819,20 @@ def test_cummin_cummax():
expected = pd.Series([2, 1, 2], name="b")
tm.assert_series_equal(result, expected)
- df = pd.DataFrame(dict(a=[1, 2, 1], b=[1, 2, 2]))
- result = df.groupby("a").b.cummin()
- expected = pd.Series([1, 2, 1], name="b")
- tm.assert_series_equal(result, expected)
+
+@pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ strict=False,
+)
+def test_cummax_all_nan_column():
+ base_df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [np.nan] * 8})
+
+ expected = pd.DataFrame({"B": [np.nan] * 8})
+ result = base_df.groupby("A").cummax()
+ tm.assert_frame_equal(expected, result)
+ result = base_df.groupby("A").B.apply(lambda x: x.cummax()).to_frame()
+ tm.assert_frame_equal(expected, result)
@pytest.mark.parametrize(
| Some small cleanups (removing unnecessary for loops / adding parameterization) | https://api.github.com/repos/pandas-dev/pandas/pulls/32027 | 2020-02-15T15:56:52Z | 2020-02-20T04:09:21Z | 2020-02-20T04:09:21Z | 2020-02-20T14:21:20Z |
CI: silence numpy-dev failures | diff --git a/pandas/__init__.py b/pandas/__init__.py
index d526531b159b2..2d3d3f7d92a9c 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -25,6 +25,7 @@
_np_version_under1p16,
_np_version_under1p17,
_np_version_under1p18,
+ _is_numpy_dev,
)
try:
diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py
index 406d5f055797d..5aab5b814bae7 100644
--- a/pandas/tests/api/test_api.py
+++ b/pandas/tests/api/test_api.py
@@ -198,6 +198,7 @@ class TestPDApi(Base):
"_np_version_under1p16",
"_np_version_under1p17",
"_np_version_under1p18",
+ "_is_numpy_dev",
"_testing",
"_tslib",
"_typing",
diff --git a/pandas/tests/frame/test_cumulative.py b/pandas/tests/frame/test_cumulative.py
index b545d6aa8afd3..2466547e2948b 100644
--- a/pandas/tests/frame/test_cumulative.py
+++ b/pandas/tests/frame/test_cumulative.py
@@ -7,8 +7,9 @@
"""
import numpy as np
+import pytest
-from pandas import DataFrame, Series
+from pandas import DataFrame, Series, _is_numpy_dev
import pandas._testing as tm
@@ -73,6 +74,9 @@ def test_cumprod(self, datetime_frame):
df.cumprod(0)
df.cumprod(1)
+ @pytest.mark.xfail(
+ _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ )
def test_cummin(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
@@ -96,6 +100,9 @@ def test_cummin(self, datetime_frame):
cummin_xs = datetime_frame.cummin(axis=1)
assert np.shape(cummin_xs) == np.shape(datetime_frame)
+ @pytest.mark.xfail(
+ _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ )
def test_cummax(self, datetime_frame):
datetime_frame.loc[5:10, 0] = np.nan
datetime_frame.loc[10:15, 1] = np.nan
diff --git a/pandas/tests/groupby/test_function.py b/pandas/tests/groupby/test_function.py
index 73e36cb5e6c84..8830b84a52421 100644
--- a/pandas/tests/groupby/test_function.py
+++ b/pandas/tests/groupby/test_function.py
@@ -17,6 +17,7 @@
NaT,
Series,
Timestamp,
+ _is_numpy_dev,
date_range,
isna,
)
@@ -685,6 +686,9 @@ def test_numpy_compat(func):
getattr(g, func)(foo=1)
+@pytest.mark.xfail(
+ _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+)
def test_cummin_cummax():
# GH 15048
num_types = [np.int32, np.int64, np.float32, np.float64]
diff --git a/pandas/tests/groupby/test_transform.py b/pandas/tests/groupby/test_transform.py
index 0ad829dd4de7a..740103eec185a 100644
--- a/pandas/tests/groupby/test_transform.py
+++ b/pandas/tests/groupby/test_transform.py
@@ -15,6 +15,7 @@
MultiIndex,
Series,
Timestamp,
+ _is_numpy_dev,
concat,
date_range,
)
@@ -329,6 +330,8 @@ def test_transform_transformation_func(transformation_func):
if transformation_func in ["pad", "backfill", "tshift", "corrwith", "cumcount"]:
# These transformation functions are not yet covered in this test
pytest.xfail("See GH 31269 and GH 31270")
+ elif _is_numpy_dev and transformation_func in ["cummin"]:
+ pytest.xfail("https://github.com/pandas-dev/pandas/issues/31992")
elif transformation_func == "fillna":
test_op = lambda x: x.transform("fillna", value=0)
mock_op = lambda x: x.fillna(value=0)
diff --git a/pandas/tests/scalar/timedelta/test_arithmetic.py b/pandas/tests/scalar/timedelta/test_arithmetic.py
index 60e278f47d0f8..f0ad5fa70471b 100644
--- a/pandas/tests/scalar/timedelta/test_arithmetic.py
+++ b/pandas/tests/scalar/timedelta/test_arithmetic.py
@@ -8,7 +8,7 @@
import pytest
import pandas as pd
-from pandas import NaT, Timedelta, Timestamp, offsets
+from pandas import NaT, Timedelta, Timestamp, _is_numpy_dev, offsets
import pandas._testing as tm
from pandas.core import ops
@@ -377,18 +377,28 @@ def test_td_div_numeric_scalar(self):
assert isinstance(result, Timedelta)
assert result == Timedelta(days=2)
- @pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")])
+ @pytest.mark.parametrize(
+ "nan",
+ [
+ np.nan,
+ pytest.param(
+ np.float64("NaN"),
+ marks=pytest.mark.xfail(
+ _is_numpy_dev,
+ reason="https://github.com/pandas-dev/pandas/issues/31992",
+ ),
+ ),
+ float("nan"),
+ ],
+ )
def test_td_div_nan(self, nan):
# np.float64('NaN') has a 'dtype' attr, avoid treating as array
td = Timedelta(10, unit="d")
result = td / nan
assert result is NaT
- # TODO: Don't leave commented, this is just a temporary fix for
- # https://github.com/pandas-dev/pandas/issues/31992
-
- # result = td // nan
- # assert result is NaT
+ result = td // nan
+ assert result is NaT
# ---------------------------------------------------------------
# Timedelta.__rdiv__
diff --git a/pandas/tests/series/test_cumulative.py b/pandas/tests/series/test_cumulative.py
index 885b5bf0476f2..b0065992b850a 100644
--- a/pandas/tests/series/test_cumulative.py
+++ b/pandas/tests/series/test_cumulative.py
@@ -11,6 +11,7 @@
import pytest
import pandas as pd
+from pandas import _is_numpy_dev
import pandas._testing as tm
@@ -37,6 +38,9 @@ def test_cumsum(self, datetime_series):
def test_cumprod(self, datetime_series):
_check_accum_op("cumprod", datetime_series)
+ @pytest.mark.xfail(
+ _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ )
def test_cummin(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummin().values,
@@ -49,6 +53,9 @@ def test_cummin(self, datetime_series):
tm.assert_series_equal(result, expected)
+ @pytest.mark.xfail(
+ _is_numpy_dev, reason="https://github.com/pandas-dev/pandas/issues/31992"
+ )
def test_cummax(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummax().values,
| xref #31992 | https://api.github.com/repos/pandas-dev/pandas/pulls/32025 | 2020-02-15T14:43:34Z | 2020-02-15T18:34:58Z | 2020-02-15T18:34:58Z | 2020-02-18T10:54:50Z |
WEB: Add greeting note to CoC | diff --git a/web/pandas/community/coc.md b/web/pandas/community/coc.md
index bf62f4e00f847..d2af9c3fdd25b 100644
--- a/web/pandas/community/coc.md
+++ b/web/pandas/community/coc.md
@@ -20,6 +20,9 @@ Examples of unacceptable behavior by participants include:
addresses, without explicit permission
* Other unethical or unprofessional conduct
+Furthermore, we encourage inclusive behavior - for example,
+please don’t say “hey guys!” but “hey everyone!”.
+
Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
| I noticed something similar in the [CPython contributing docs](https://cpython-core-tutorial.readthedocs.io/en/latest/diversity.html):
> For example, don’t say “hey guys!” but “hey everyone!”.
and thought it was really nice.
Pushing directly rather than opening as good first issue to avoid invoking a s*******m | https://api.github.com/repos/pandas-dev/pandas/pulls/32024 | 2020-02-15T13:40:06Z | 2020-02-27T23:08:16Z | 2020-02-27T23:08:16Z | 2020-03-26T16:13:32Z |
DOC: Add missing period to parameter description | diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index d0c64d54f30d6..018441dacd9a8 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -452,7 +452,7 @@ def format(self, formatter, subset=None, na_rep: Optional[str] = None) -> "Style
``formatter`` is applied to.
na_rep : str, optional
Representation for missing values.
- If ``na_rep`` is None, no special formatting is applied
+ If ``na_rep`` is None, no special formatting is applied.
.. versionadded:: 1.0.0
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index 0eb7deb5574ec..e97872d880dee 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -361,7 +361,7 @@ def read_sql(
Using SQLAlchemy makes it possible to use any DB supported by that
library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible
for engine disposal and connection closure for the SQLAlchemy connectable. See
- `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_
+ `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_.
index_col : str or list of str, optional, default: None
Column(s) to set as index(MultiIndex).
coerce_float : bool, default True
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32022 | 2020-02-15T12:05:33Z | 2020-02-15T21:50:43Z | 2020-02-15T21:50:42Z | 2020-02-15T21:50:54Z |
DOC SS06 Make the summery in one line on offsets.py | diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py
index df1e750b32138..959dd19a50d90 100644
--- a/pandas/tseries/offsets.py
+++ b/pandas/tseries/offsets.py
@@ -1017,8 +1017,7 @@ def __init__(
class CustomBusinessDay(_CustomMixin, BusinessDay):
"""
- DateOffset subclass representing possibly n custom business days,
- excluding holidays.
+ DateOffset subclass representing custom business days excluding holidays.
Parameters
----------
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32021 | 2020-02-15T12:01:20Z | 2020-02-15T19:07:14Z | 2020-02-15T19:07:14Z | 2020-02-15T19:07:25Z |
DOC: Fix errors in pandas.Series.argmax | diff --git a/pandas/core/base.py b/pandas/core/base.py
index 85424e35fa0e0..b9aeb32eea5c1 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -927,22 +927,50 @@ def max(self, axis=None, skipna=True, *args, **kwargs):
def argmax(self, axis=None, skipna=True, *args, **kwargs):
"""
- Return an ndarray of the maximum argument indexer.
+ Return int position of the largest value in the Series.
+
+ If the maximum is achieved in multiple locations,
+ the first row position is returned.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series.
skipna : bool, default True
+ Exclude NA/null values when showing the result.
+ *args, **kwargs
+ Additional arguments and keywords for compatibility with NumPy.
Returns
-------
- numpy.ndarray
- Indices of the maximum values.
+ int
+ Row position of the maximum values.
See Also
--------
- numpy.ndarray.argmax
+ numpy.ndarray.argmax : Equivalent method for numpy arrays.
+ Series.argmin : Similar method, but returning the minimum.
+ Series.idxmax : Return index label of the maximum values.
+ Series.idxmin : Return index label of the minimum values.
+
+ Examples
+ --------
+ Consider dataset containing cereal calories
+
+ >>> s = pd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0,
+ ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0})
+ >>> s
+ Corn Flakes 100.0
+ Almond Delight 110.0
+ Cinnamon Toast Crunch 120.0
+ Cocoa Puff 110.0
+ dtype: float64
+
+ >>> s.argmax()
+ 2
+
+ The maximum cereal calories is in the third element,
+ since series is zero-indexed.
"""
nv.validate_minmax_axis(axis)
nv.validate_argmax_with_skipna(skipna, args, kwargs)
| - [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/18
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
output of `python scripts/validate_docstrings.py pandas.Series.argmax`:
```
################################################################################
################################## Validation ##################################
################################################################################
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/32019 | 2020-02-15T11:42:12Z | 2020-02-26T20:53:54Z | 2020-02-26T20:53:54Z | 2020-02-27T07:35:55Z |
DOC add extended summary, update parameter types desc, update return types desc, and add whitespaces after commas in list declarations to DataFrame.first in core/generic.py | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index e6c5ac9dbf733..b039630efc989 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8014,15 +8014,21 @@ def resample(
def first(self: FrameOrSeries, offset) -> FrameOrSeries:
"""
- Method to subset initial periods of time series data based on a date offset.
+ Select initial periods of time series data based on a date offset.
+
+ When having a DataFrame with dates as index, this function can
+ select the first few rows based on a date offset.
Parameters
----------
- offset : str, DateOffset, dateutil.relativedelta
+ offset : str, DateOffset or dateutil.relativedelta
+ The offset length of the data that will be selected. For instance,
+ '1M' will display all the rows having their index within the first month.
Returns
-------
- subset : same type as caller
+ Series or DataFrame
+ A subset of the caller.
Raises
------
@@ -8038,7 +8044,7 @@ def first(self: FrameOrSeries, offset) -> FrameOrSeries:
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
- >>> ts = pd.DataFrame({'A': [1,2,3,4]}, index=i)
+ >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 1
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/32018 | 2020-02-15T11:32:50Z | 2020-03-10T21:30:06Z | 2020-03-10T21:30:06Z | 2020-03-11T08:48:54Z |
DOC: Improve docstring of Index.delete | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 3d549405592d6..14ee21ea5614c 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -5135,9 +5135,30 @@ def delete(self, loc):
"""
Make new Index with passed location(-s) deleted.
+ Parameters
+ ----------
+ loc : int or list of int
+ Location of item(-s) which will be deleted.
+ Use a list of locations to delete more than one value at the same time.
+
Returns
-------
- new_index : Index
+ Index
+ New Index with passed location(-s) deleted.
+
+ See Also
+ --------
+ numpy.delete : Delete any rows and column from NumPy array (ndarray).
+
+ Examples
+ --------
+ >>> idx = pd.Index(['a', 'b', 'c'])
+ >>> idx.delete(1)
+ Index(['a', 'c'], dtype='object')
+
+ >>> idx = pd.Index(['a', 'b', 'c'])
+ >>> idx.delete([0, 2])
+ Index(['b'], dtype='object')
"""
return self._shallow_copy(np.delete(self._data, loc))
| - [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/13
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output from validate_docstrings.py
################################################################################
####################### Docstring (pandas.Index.delete) #######################
################################################################################
Make new Index with passed location(-s) deleted.
Use array of integer as loc parameter to delete multiple locations.
Parameters
----------
loc : int
Location of item(-s) which will be deleted.
Returns
-------
Index
New Index with passed location(-s) deleted.
See Also
--------
numpy.delete : Delete any rows and column from NumPy array (ndarray).
Examples
--------
>>> idx = pd.Index(['a', 'b', 'c'])
>>> idx.delete(1) # Deleting 'b'
Index(['a', 'c'], dtype='object')
>>> idx = pd.Index(['a', 'b', 'c'])
>>> idx.delete([0, 2])
Index(['b'], dtype='object')
################################################################################
################################## Validation ##################################
################################################################################
| https://api.github.com/repos/pandas-dev/pandas/pulls/32015 | 2020-02-15T11:12:36Z | 2020-02-15T19:08:13Z | 2020-02-15T19:08:13Z | 2020-02-24T15:11:04Z |
DOC: Update pandas.Series.between_time docstring params | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 04e8b78fb1b87..e996c47ac7ad3 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -7538,16 +7538,22 @@ def between_time(
Parameters
----------
start_time : datetime.time or str
+ Initial time as a time filter limit.
end_time : datetime.time or str
+ End time as a time filter limit.
include_start : bool, default True
+ Whether the start time needs to be included in the result.
include_end : bool, default True
+ Whether the end time needs to be included in the result.
axis : {0 or 'index', 1 or 'columns'}, default 0
+ Determine range time on index or columns value.
.. versionadded:: 0.24.0
Returns
-------
Series or DataFrame
+ Data from the original object filtered to the specified dates range.
Raises
------
| - [x] closes [https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/20](https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/20)
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
output from python scripts/validate_docstrings.py pandas.Series.between_time:
################################################################################
#################### Docstring (pandas.Series.between_time) ####################
################################################################################
Select values between particular times of the day (e.g., 9:00-9:30 AM).
By setting ``start_time`` to be later than ``end_time``,
you can get the times that are *not* between the two times.
Parameters
----------
start_time : datetime.time or str
The first time value.
end_time : datetime.time or str
The second time value.
include_start : bool, default True
Adding start_time value in the result.
include_end : bool, default True
Adding end_time value in the result.
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine range time on index or columns value.
.. versionadded:: 0.24.0
Returns
-------
Series or DataFrame
If axis set on columns, it will return DataFrame format, vice versa.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
at_time : Select values at a particular time of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_between_time : Get just the index locations for
values between particular times of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
2018-04-12 01:00:00 4
>>> ts.between_time('0:15', '0:45')
A
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
You get the times that are *not* between two times by setting
``start_time`` later than ``end_time``:
>>> ts.between_time('0:45', '0:15')
A
2018-04-09 00:00:00 1
2018-04-12 01:00:00 4
################################################################################
################################## Validation ##################################
################################################################################ | https://api.github.com/repos/pandas-dev/pandas/pulls/32014 | 2020-02-15T11:05:41Z | 2020-02-15T19:10:24Z | 2020-02-15T19:10:24Z | 2020-02-15T19:10:34Z |
PERF: improved performance of multiindex slicing | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 168fd803c5f8a..65b5266380d96 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -63,6 +63,8 @@ Performance Improvements
- 4x improvement in ``timedelta`` string parsing (:issue:`6755`)
- 8x improvement in ``timedelta64`` and ``datetime64`` ops (:issue:`6755`)
+- Significantly improved performance of indexing ``MultiIndex`` with slicers (:issue:`10287`)
+- Improved performance of ``Series.isin`` for datetimelike/integer Series (:issue:`10287`)
.. _whatsnew_0170.bug_fixes:
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 990eec08d0bd6..76deb773c06c4 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -2497,6 +2497,10 @@ def is_integer_dtype(arr_or_dtype):
return (issubclass(tipo, np.integer) and
not issubclass(tipo, (np.datetime64, np.timedelta64)))
+def is_int64_dtype(arr_or_dtype):
+ tipo = _get_dtype_type(arr_or_dtype)
+ return issubclass(tipo, np.int64)
+
def is_int_or_datetime_dtype(arr_or_dtype):
tipo = _get_dtype_type(arr_or_dtype)
diff --git a/pandas/core/index.py b/pandas/core/index.py
index fad71c94cc417..35cf2c5aec3d5 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -105,6 +105,7 @@ class Index(IndexOpsMixin, PandasObject):
_is_numeric_dtype = False
_engine_type = _index.ObjectEngine
+ _isin_type = lib.ismember
def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False,
tupleize_cols=True, **kwargs):
@@ -1838,7 +1839,7 @@ def isin(self, values, level=None):
value_set = set(values)
if level is not None:
self._validate_index_level(level)
- return lib.ismember(np.array(self), value_set)
+ return self._isin_type(np.array(self), value_set)
def _can_reindex(self, indexer):
"""
@@ -3381,6 +3382,7 @@ class Int64Index(NumericIndex):
_outer_indexer = _algos.outer_join_indexer_int64
_engine_type = _index.Int64Engine
+ _isin_type = lib.ismember_int64
def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False, **kwargs):
@@ -5237,13 +5239,39 @@ def partial_selection(key, indexer=None):
indexer = self._get_level_indexer(key, level=level)
return indexer, maybe_droplevels(indexer, [level], drop_level)
- def _get_level_indexer(self, key, level=0):
- # return a boolean indexer or a slice showing where the key is
+ def _get_level_indexer(self, key, level=0, indexer=None):
+ # return an indexer, boolean array or a slice showing where the key is
# in the totality of values
+ # if the indexer is provided, then use this
level_index = self.levels[level]
labels = self.labels[level]
+ def convert_indexer(start, stop, step, indexer=indexer, labels=labels):
+ # given the inputs and the labels/indexer, compute an indexer set
+ # if we have a provided indexer, then this need not consider
+ # the entire labels set
+
+ r = np.arange(start,stop,step)
+ if indexer is not None and len(indexer) != len(labels):
+
+ # we have an indexer which maps the locations in the labels that we
+ # have already selected (and is not an indexer for the entire set)
+ # otherwise this is wasteful
+ # so we only need to examine locations that are in this set
+ # the only magic here is that the result are the mappings to the
+ # set that we have selected
+ from pandas import Series
+ mapper = Series(indexer)
+ result = Series(Index(labels.take(indexer)).isin(r).nonzero()[0])
+ m = result.map(mapper).values
+
+ else:
+ m = np.zeros(len(labels),dtype=bool)
+ m[np.in1d(labels,r,assume_unique=True)] = True
+
+ return m
+
if isinstance(key, slice):
# handle a slice, returnig a slice if we can
# otherwise a boolean indexer
@@ -5269,17 +5297,13 @@ def _get_level_indexer(self, key, level=0):
# a partial date slicer on a DatetimeIndex generates a slice
# note that the stop ALREADY includes the stopped point (if
# it was a string sliced)
- m = np.zeros(len(labels),dtype=bool)
- m[np.in1d(labels,np.arange(start.start,stop.stop,step))] = True
- return m
+ return convert_indexer(start.start,stop.stop,step)
elif level > 0 or self.lexsort_depth == 0 or step is not None:
# need to have like semantics here to right
# searching as when we are using a slice
# so include the stop+1 (so we include stop)
- m = np.zeros(len(labels),dtype=bool)
- m[np.in1d(labels,np.arange(start,stop+1,step))] = True
- return m
+ return convert_indexer(start,stop+1,step)
else:
# sorted, so can return slice object -> view
i = labels.searchsorted(start, side='left')
@@ -5317,59 +5341,73 @@ def get_locs(self, tup):
raise KeyError('MultiIndex Slicing requires the index to be fully lexsorted'
' tuple len ({0}), lexsort depth ({1})'.format(len(tup), self.lexsort_depth))
- def _convert_indexer(r):
+ # indexer
+ # this is the list of all values that we want to select
+ n = len(self)
+ indexer = None
+
+ def _convert_to_indexer(r):
+ # return an indexer
if isinstance(r, slice):
- m = np.zeros(len(self),dtype=bool)
+ m = np.zeros(n,dtype=bool)
m[r] = True
- return m
- return r
+ r = m.nonzero()[0]
+ elif is_bool_indexer(r):
+ if len(r) != n:
+ raise ValueError("cannot index with a boolean indexer that is"
+ " not the same length as the index")
+ r = r.nonzero()[0]
+ return Int64Index(r)
+
+ def _update_indexer(idxr, indexer=indexer):
+ if indexer is None:
+ indexer = Index(np.arange(n))
+ if idxr is None:
+ return indexer
+ return indexer & idxr
- ranges = []
for i,k in enumerate(tup):
if is_bool_indexer(k):
# a boolean indexer, must be the same length!
k = np.asarray(k)
- if len(k) != len(self):
- raise ValueError("cannot index with a boolean indexer that is"
- " not the same length as the index")
- ranges.append(k)
+ indexer = _update_indexer(_convert_to_indexer(k), indexer=indexer)
+
elif is_list_like(k):
# a collection of labels to include from this level (these are or'd)
- indexers = []
+ indexers = None
for x in k:
try:
- indexers.append(_convert_indexer(self._get_level_indexer(x, level=i)))
+ idxrs = _convert_to_indexer(self._get_level_indexer(x, level=i, indexer=indexer))
+ indexers = idxrs if indexers is None else indexers | idxrs
except (KeyError):
# ignore not founds
continue
- if len(k):
- ranges.append(reduce(np.logical_or, indexers))
+
+ if indexers is not None:
+ indexer = _update_indexer(indexers, indexer=indexer)
else:
- ranges.append(np.zeros(self.labels[i].shape, dtype=bool))
+
+ # no matches we are done
+ return Int64Index([]).values
elif is_null_slice(k):
# empty slice
- pass
+ indexer = _update_indexer(None, indexer=indexer)
elif isinstance(k,slice):
# a slice, include BOTH of the labels
- ranges.append(self._get_level_indexer(k,level=i))
+ indexer = _update_indexer(_convert_to_indexer(self._get_level_indexer(k,level=i,indexer=indexer)), indexer=indexer)
else:
# a single label
- ranges.append(self.get_loc_level(k,level=i,drop_level=False)[0])
-
- # identity
- if len(ranges) == 0:
- return slice(0,len(self))
-
- elif len(ranges) == 1:
- return ranges[0]
+ indexer = _update_indexer(_convert_to_indexer(self.get_loc_level(k,level=i,drop_level=False)[0]), indexer=indexer)
- # construct a boolean indexer if we have a slice or boolean indexer
- return reduce(np.logical_and,[ _convert_indexer(r) for r in ranges ])
+ # empty indexer
+ if indexer is None:
+ return Int64Index([]).values
+ return indexer.values
def truncate(self, before=None, after=None):
"""
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 02309e6e4e3b5..6bc505127f872 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -509,7 +509,7 @@ def can_do_equal_len():
def _align_series(self, indexer, ser):
# indexer to assign Series can be tuple, slice, scalar
- if isinstance(indexer, (slice, np.ndarray, list)):
+ if isinstance(indexer, (slice, np.ndarray, list, Index)):
indexer = tuple([indexer])
if isinstance(indexer, tuple):
@@ -1719,7 +1719,7 @@ def maybe_convert_ix(*args):
ixify = True
for arg in args:
- if not isinstance(arg, (np.ndarray, list, ABCSeries)):
+ if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)):
ixify = False
if ixify:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index dfbc5dbf84572..d1ddd086bf8b7 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -19,6 +19,7 @@
is_list_like, _values_from_object,
_possibly_cast_to_datetime, _possibly_castable,
_possibly_convert_platform, _try_sort,
+ is_int64_dtype,
ABCSparseArray, _maybe_match_name,
_coerce_to_dtype, SettingWithCopyError,
_maybe_box_datetimelike, ABCDataFrame,
@@ -2250,17 +2251,22 @@ def isin(self, values):
# may need i8 conversion for proper membership testing
comps = _values_from_object(self)
+ f = lib.ismember
if com.is_datetime64_dtype(self):
from pandas.tseries.tools import to_datetime
values = Series(to_datetime(values)).values.view('i8')
comps = comps.view('i8')
+ f = lib.ismember_int64
elif com.is_timedelta64_dtype(self):
from pandas.tseries.timedeltas import to_timedelta
values = Series(to_timedelta(values)).values.view('i8')
comps = comps.view('i8')
+ f = lib.ismember_int64
+ elif is_int64_dtype(self):
+ f = lib.ismember_int64
value_set = set(values)
- result = lib.ismember(comps, value_set)
+ result = f(comps, value_set)
return self._constructor(result, index=self.index).__finalize__(self)
def between(self, left, right, inclusive=True):
diff --git a/pandas/lib.pyx b/pandas/lib.pyx
index cc4c43494176e..27ba6f953306d 100644
--- a/pandas/lib.pyx
+++ b/pandas/lib.pyx
@@ -156,6 +156,31 @@ def ismember(ndarray arr, set values):
return result.view(np.bool_)
+def ismember_int64(ndarray[int64_t] arr, set values):
+ '''
+ Checks whether
+
+ Parameters
+ ----------
+ arr : ndarray of int64
+ values : set
+
+ Returns
+ -------
+ ismember : ndarray (boolean dtype)
+ '''
+ cdef:
+ Py_ssize_t i, n
+ ndarray[uint8_t] result
+ int64_t v
+
+ n = len(arr)
+ result = np.empty(n, dtype=np.uint8)
+ for i in range(n):
+ result[i] = arr[i] in values
+
+ return result.view(np.bool_)
+
#----------------------------------------------------------------------
# datetime / io related
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index 710367bf04605..94bb2c9f8ea81 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -2293,6 +2293,7 @@ def f():
index=pd.MultiIndex.from_product([['A','B','C'],['foo']],
names=['one','two'])
).sortlevel()
+
result = s.loc[idx[:,['foo']]]
assert_series_equal(result,expected)
result = s.loc[idx[:,['foo','bah']]]
@@ -2304,9 +2305,9 @@ def f():
df = DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index)
df = df.sortlevel(0, axis=1)
+ expected = DataFrame(index=range(5),columns=multi_index.reindex([])[0])
result1 = df.loc[:, ([], slice(None))]
result2 = df.loc[:, (['foo'], [])]
- expected = DataFrame(index=range(5),columns=multi_index.reindex([])[0])
assert_frame_equal(result1, expected)
assert_frame_equal(result2, expected)
diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py
index 15f69b38febce..ae869ce9bd794 100644
--- a/pandas/tseries/base.py
+++ b/pandas/tseries/base.py
@@ -449,7 +449,7 @@ def isin(self, values):
return self.asobject.isin(values)
value_set = set(values.asi8)
- return lib.ismember(self.asi8, value_set)
+ return lib.ismember_int64(self.asi8, value_set)
def shift(self, n, freq=None):
"""
diff --git a/vb_suite/indexing.py b/vb_suite/indexing.py
index 012eb462fcc48..9fbc070ac3b9d 100644
--- a/vb_suite/indexing.py
+++ b/vb_suite/indexing.py
@@ -235,3 +235,33 @@
series_ix_slice = Benchmark("s.ix[:800000]", setup)
series_ix_list_like = Benchmark("s.ix[[800000]]", setup)
series_ix_array = Benchmark("s.ix[np.arange(10000)]", setup)
+
+
+# multi-index slicing
+setup = common_setup + """
+np.random.seed(1234)
+idx=pd.IndexSlice
+n=100000
+mdt = pandas.DataFrame()
+mdt['A'] = np.random.choice(range(10000,45000,1000), n)
+mdt['B'] = np.random.choice(range(10,400), n)
+mdt['C'] = np.random.choice(range(1,150), n)
+mdt['D'] = np.random.choice(range(10000,45000), n)
+mdt['x'] = np.random.choice(range(400), n)
+mdt['y'] = np.random.choice(range(25), n)
+
+
+test_A = 25000
+test_B = 25
+test_C = 40
+test_D = 35000
+
+eps_A = 5000
+eps_B = 5
+eps_C = 5
+eps_D = 5000
+mdt2 = mdt.set_index(['A','B','C','D']).sortlevel()
+"""
+
+multiindex_slicers = Benchmark('mdt2.loc[idx[test_A-eps_A:test_A+eps_A,test_B-eps_B:test_B+eps_B,test_C-eps_C:test_C+eps_C,test_D-eps_D:test_D+eps_D],:]', setup,
+ start_date=datetime(2015, 1, 1))
diff --git a/vb_suite/series_methods.py b/vb_suite/series_methods.py
index 1659340cfe050..d0c31cb04ca6a 100644
--- a/vb_suite/series_methods.py
+++ b/vb_suite/series_methods.py
@@ -7,6 +7,9 @@
setup = common_setup + """
s1 = Series(np.random.randn(10000))
s2 = Series(np.random.randint(1, 10, 10000))
+s3 = Series(np.random.randint(1, 10, 100000)).astype('int64')
+values = [1,2]
+s4 = s3.astype('object')
"""
series_nlargest1 = Benchmark('s1.nlargest(3, take_last=True);'
@@ -27,3 +30,10 @@
's2.nsmallest(3, take_last=False)',
setup,
start_date=datetime(2014, 1, 25))
+
+series_isin_int64 = Benchmark('s3.isin(values)',
+ setup,
+ start_date=datetime(2014, 1, 25))
+series_isin_object = Benchmark('s4.isin(values)',
+ setup,
+ start_date=datetime(2014, 1, 25))
| closes #10287
master
```
In [22]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A],:].loc[idx[:,test_B-eps_B:test_B+eps_B],:].loc[idx[:,:,test_C-eps_C:test_C+eps_C],:].loc[idx[:,:,:,test_D-eps_D:test_D+eps_D],:]
10 loops, best of 3: 141 ms per loop
In [23]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A,test_B-eps_B:test_B+eps_B,test_C-eps_C:test_C+eps_C,test_D-eps_D:test_D+eps_D],:]
1 loops, best of 3: 4.23 s per loop
```
PR
```
# this actually is a bit faster in master. but this repeated chain indexing is frowned upon anyhow
In [22]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A],:].loc[idx[:,test_B-eps_B:test_B+eps_B],:].loc[idx[:,:,test_C-eps_C:test_C+eps_C],:].loc[idx[:,:,:,test_D-eps_D:test_D+eps_D],:]
1 loops, best of 3: 210 ms per loop
# this is the prefered method (as you can set with this and such), and such a huge diff.
In [23]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A,test_B-eps_B:test_B+eps_B,test_C-eps_C:test_C+eps_C,test_D-eps_D:test_D+eps_D],:]
1 loops, best of 3: 425 ms per loop
```
master
```
In [10]: %timeit s3.isin([1,2])
100 loops, best of 3: 8.83 ms per loop
```
PR
```
In [2]: s3 = Series(np.random.randint(1, 10, 100000)).astype('int64')
In [5]: %timeit s3.isin([1,2])
100 loops, best of 3: 2.47 ms per loop
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10290 | 2015-06-05T19:16:08Z | 2015-06-24T20:59:59Z | 2015-06-24T20:59:59Z | 2015-06-24T20:59:59Z |
BUG: DataFrame.where does not handle Series slice correctly (#10218) | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index d30b7875e44b7..c1c8f619d3166 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -785,3 +785,4 @@ Bug Fixes
- Bug in ``read_msgpack`` where encoding is not respected (:issue:`10580`)
- Bug preventing access to the first index when using ``iloc`` with a list containing the appropriate negative integer (:issue:`10547`, :issue:`10779`)
- Bug in ``TimedeltaIndex`` formatter causing error while trying to save ``DataFrame`` with ``TimedeltaIndex`` using ``to_csv`` (:issue:`10833`)
+- BUG in ``DataFrame.where`` when handling Series slicing (:issue:`10218`, :issue:`9558`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 1f222f9f99cbe..449ac239cf7d0 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2596,6 +2596,14 @@ def _reindex_multi(self, axes, copy, fill_value):
copy=copy,
fill_value=fill_value)
+ @Appender(_shared_docs['align'] % _shared_doc_kwargs)
+ def align(self, other, join='outer', axis=None, level=None, copy=True,
+ fill_value=None, method=None, limit=None, fill_axis=0,
+ broadcast_axis=None):
+ return super(DataFrame, self).align(other, join=join, axis=axis, level=level, copy=copy,
+ fill_value=fill_value, method=method, limit=limit,
+ fill_axis=fill_axis, broadcast_axis=broadcast_axis)
+
@Appender(_shared_docs['reindex'] % _shared_doc_kwargs)
def reindex(self, index=None, columns=None, **kwargs):
return super(DataFrame, self).reindex(index=index, columns=columns,
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index bc49e9dd79e6a..c21d7588ec7d1 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3447,8 +3447,7 @@ def last(self, offset):
start = self.index.searchsorted(start_date, side='right')
return self.ix[start:]
- def align(self, other, join='outer', axis=None, level=None, copy=True,
- fill_value=None, method=None, limit=None, fill_axis=0):
+ _shared_docs['align'] = (
"""
Align two object on their axes with the
specified join method for each axis Index
@@ -3470,17 +3469,46 @@ def align(self, other, join='outer', axis=None, level=None, copy=True,
"compatible" value
method : str, default None
limit : int, default None
- fill_axis : {0, 1}, default 0
+ fill_axis : %(axes_single_arg)s, default 0
Filling axis, method and limit
+ broadcast_axis : %(axes_single_arg)s, default None
+ Broadcast values along this axis, if aligning two objects of
+ different dimensions
+
+ .. versionadded:: 0.17.0
Returns
-------
- (left, right) : (type of input, type of other)
+ (left, right) : (%(klass)s, type of other)
Aligned objects
"""
+ )
+
+ @Appender(_shared_docs['align'] % _shared_doc_kwargs)
+ def align(self, other, join='outer', axis=None, level=None, copy=True,
+ fill_value=None, method=None, limit=None, fill_axis=0,
+ broadcast_axis=None):
from pandas import DataFrame, Series
method = com._clean_fill_method(method)
+ if broadcast_axis == 1 and self.ndim != other.ndim:
+ if isinstance(self, Series):
+ # this means other is a DataFrame, and we need to broadcast self
+ df = DataFrame(dict((c, self) for c in other.columns),
+ **other._construct_axes_dict())
+ return df._align_frame(other, join=join, axis=axis, level=level,
+ copy=copy, fill_value=fill_value,
+ method=method, limit=limit,
+ fill_axis=fill_axis)
+ elif isinstance(other, Series):
+ # this means self is a DataFrame, and we need to broadcast other
+ df = DataFrame(dict((c, other) for c in self.columns),
+ **self._construct_axes_dict())
+ return self._align_frame(df, join=join, axis=axis, level=level,
+ copy=copy, fill_value=fill_value,
+ method=method, limit=limit,
+ fill_axis=fill_axis)
+
if axis is not None:
axis = self._get_axis_number(axis)
if isinstance(other, DataFrame):
@@ -3516,11 +3544,11 @@ def _align_frame(self, other, join='outer', axis=None, level=None,
self.columns.join(other.columns, how=join, level=level,
return_indexers=True)
- left = self._reindex_with_indexers({0: [join_index, ilidx],
+ left = self._reindex_with_indexers({0: [join_index, ilidx],
1: [join_columns, clidx]},
copy=copy, fill_value=fill_value,
allow_dups=True)
- right = other._reindex_with_indexers({0: [join_index, iridx],
+ right = other._reindex_with_indexers({0: [join_index, iridx],
1: [join_columns, cridx]},
copy=copy, fill_value=fill_value,
allow_dups=True)
@@ -3624,7 +3652,7 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
try_cast=False, raise_on_error=True):
if isinstance(cond, NDFrame):
- cond = cond.reindex(**self._construct_axes_dict())
+ cond, _ = cond.align(self, join='right', broadcast_axis=1)
else:
if not hasattr(cond, 'shape'):
raise ValueError('where requires an ndarray like object for '
diff --git a/pandas/core/panel.py b/pandas/core/panel.py
index bc342d5919bb8..919c416c347cd 100644
--- a/pandas/core/panel.py
+++ b/pandas/core/panel.py
@@ -628,6 +628,9 @@ def _needs_reindex_multi(self, axes, method, level):
""" don't allow a multi reindex on Panel or above ndim """
return False
+ def align(self, other, **kwargs):
+ raise NotImplementedError
+
def dropna(self, axis=0, how='any', inplace=False):
"""
Drop 2D from panel, holding passed axis constant
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 8768d0e139e7b..3006984a9915c 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2164,6 +2164,14 @@ def _needs_reindex_multi(self, axes, method, level):
"""
return False
+ @Appender(generic._shared_docs['align'] % _shared_doc_kwargs)
+ def align(self, other, join='outer', axis=None, level=None, copy=True,
+ fill_value=None, method=None, limit=None, fill_axis=0,
+ broadcast_axis=None):
+ return super(Series, self).align(other, join=join, axis=axis, level=level, copy=copy,
+ fill_value=fill_value, method=method, limit=limit,
+ fill_axis=fill_axis, broadcast_axis=broadcast_axis)
+
@Appender(generic._shared_docs['rename'] % _shared_doc_kwargs)
def rename(self, index=None, **kwargs):
return super(Series, self).rename(index=index, **kwargs)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 022594e296c2a..a9dedac74add5 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -10065,6 +10065,34 @@ def test_align(self):
self.assertRaises(ValueError, self.frame.align, af.ix[0, :3],
join='inner', axis=2)
+ # align dataframe to series with broadcast or not
+ idx = self.frame.index
+ s = Series(range(len(idx)), index=idx)
+
+ left, right = self.frame.align(s, axis=0)
+ tm.assert_index_equal(left.index, self.frame.index)
+ tm.assert_index_equal(right.index, self.frame.index)
+ self.assertTrue(isinstance(right, Series))
+
+ left, right = self.frame.align(s, broadcast_axis=1)
+ tm.assert_index_equal(left.index, self.frame.index)
+ expected = {}
+ for c in self.frame.columns:
+ expected[c] = s
+ expected = DataFrame(expected, index=self.frame.index,
+ columns=self.frame.columns)
+ assert_frame_equal(right, expected)
+
+ # GH 9558
+ df = DataFrame({'a':[1,2,3], 'b':[4,5,6]})
+ result = df[df['a'] == 2]
+ expected = DataFrame([[2, 5]], index=[1], columns=['a', 'b'])
+ assert_frame_equal(result, expected)
+
+ result = df.where(df['a'] == 2, 0)
+ expected = DataFrame({'a':[0, 2, 0], 'b':[0, 5, 0]})
+ assert_frame_equal(result, expected)
+
def _check_align(self, a, b, axis, fill_axis, how, method, limit=None):
aa, ab = a.align(b, axis=axis, join=how, method=method, limit=limit,
fill_axis=fill_axis)
@@ -10310,6 +10338,13 @@ def _check_set(df, cond, check_dtypes = True):
cond = (df >= 0)[1:]
_check_set(df, cond)
+ # GH 10218
+ # test DataFrame.where with Series slicing
+ df = DataFrame({'a': range(3), 'b': range(4, 7)})
+ result = df.where(df['a'] == 1)
+ expected = df[df['a'] == 1].reindex(df.index)
+ assert_frame_equal(result, expected)
+
def test_where_bug(self):
# GH 2793
| closes #10218
| https://api.github.com/repos/pandas-dev/pandas/pulls/10283 | 2015-06-05T03:00:16Z | 2015-08-26T01:41:14Z | 2015-08-26T01:41:14Z | 2015-08-30T18:08:39Z |
PERF/CLN: Improve datetime-like index ops perf | diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index c1b7ff82f4c76..6db2cb409df85 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -139,6 +139,8 @@ Performance Improvements
- Improved performance of ``andrews_curves`` (:issue:`11534`)
+- Improved huge ``DatetimeIndex``, ``PeriodIndex`` and ``TimedeltaIndex``'s ops performance including ``NaT`` (:issue:`10277`)
+
diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py
index 4f0780ef2d660..ed9bf8d3862ce 100644
--- a/pandas/tseries/base.py
+++ b/pandas/tseries/base.py
@@ -281,12 +281,11 @@ def _maybe_mask_results(self, result, fill_value=None, convert=None):
"""
if self.hasnans:
- mask = self.asi8 == tslib.iNaT
if convert:
result = result.astype(convert)
if fill_value is None:
fill_value = np.nan
- result[mask] = fill_value
+ result[self._isnan] = fill_value
return result
def tolist(self):
@@ -312,8 +311,7 @@ def min(self, axis=None):
return self._box_func(i8[0])
if self.hasnans:
- mask = i8 == tslib.iNaT
- min_stamp = i8[~mask].min()
+ min_stamp = self[~self._isnan].asi8.min()
else:
min_stamp = i8.min()
return self._box_func(min_stamp)
@@ -331,7 +329,7 @@ def argmin(self, axis=None):
i8 = self.asi8
if self.hasnans:
- mask = i8 == tslib.iNaT
+ mask = self._isnan
if mask.all():
return -1
i8 = i8.copy()
@@ -355,8 +353,7 @@ def max(self, axis=None):
return self._box_func(i8[-1])
if self.hasnans:
- mask = i8 == tslib.iNaT
- max_stamp = i8[~mask].max()
+ max_stamp = self[~self._isnan].asi8.max()
else:
max_stamp = i8.max()
return self._box_func(max_stamp)
@@ -374,7 +371,7 @@ def argmax(self, axis=None):
i8 = self.asi8
if self.hasnans:
- mask = i8 == tslib.iNaT
+ mask = self._isnan
if mask.all():
return -1
i8 = i8.copy()
@@ -498,9 +495,9 @@ def _add_delta_td(self, other):
# return the i8 result view
inc = tslib._delta_to_nanoseconds(other)
- mask = self.asi8 == tslib.iNaT
new_values = (self.asi8 + inc).view('i8')
- new_values[mask] = tslib.iNaT
+ if self.hasnans:
+ new_values[self._isnan] = tslib.iNaT
return new_values.view('i8')
def _add_delta_tdi(self, other):
@@ -513,9 +510,10 @@ def _add_delta_tdi(self, other):
self_i8 = self.asi8
other_i8 = other.asi8
- mask = (self_i8 == tslib.iNaT) | (other_i8 == tslib.iNaT)
new_values = self_i8 + other_i8
- new_values[mask] = tslib.iNaT
+ if self.hasnans or other.hasnans:
+ mask = (self._isnan) | (other._isnan)
+ new_values[mask] = tslib.iNaT
return new_values.view(self.dtype)
def isin(self, values):
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index 88816fd0c0dad..14acfb57afe56 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -93,9 +93,8 @@ def wrapper(self, other):
if o_mask.any():
result[o_mask] = nat_result
- mask = self.asi8 == tslib.iNaT
- if mask.any():
- result[mask] = nat_result
+ if self.hasnans:
+ result[self._isnan] = nat_result
# support of bool dtype indexers
if com.is_bool_dtype(result):
diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py
index 3f4bba0344ca0..534804900c5e6 100644
--- a/pandas/tseries/period.py
+++ b/pandas/tseries/period.py
@@ -589,9 +589,9 @@ def shift(self, n):
-------
shifted : PeriodIndex
"""
- mask = self.values == tslib.iNaT
values = self.values + n * self.freq.n
- values[mask] = tslib.iNaT
+ if self.hasnans:
+ values[self._isnan] = tslib.iNaT
return PeriodIndex(data=values, name=self.name, freq=self.freq)
@cache_readonly
diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py
index 3f884ee32dd76..ea61e4f247e58 100644
--- a/pandas/tseries/tdi.py
+++ b/pandas/tseries/tdi.py
@@ -51,9 +51,8 @@ def wrapper(self, other):
if o_mask.any():
result[o_mask] = nat_result
- mask = self.asi8 == tslib.iNaT
- if mask.any():
- result[mask] = nat_result
+ if self.hasnans:
+ result[self._isnan] = nat_result
# support of bool dtype indexers
if com.is_bool_dtype(result):
@@ -334,7 +333,7 @@ def _get_field(self, m):
hasnans = self.hasnans
if hasnans:
result = np.empty(len(self), dtype='float64')
- mask = values == tslib.iNaT
+ mask = self._isnan
imask = ~mask
result.flat[imask] = np.array([ getattr(Timedelta(val),m) for val in values[imask] ])
result[mask] = np.nan
diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py
index 4d353eccba972..bf37bd4afe1da 100644
--- a/pandas/tseries/tests/test_base.py
+++ b/pandas/tseries/tests/test_base.py
@@ -124,6 +124,8 @@ def test_minmax(self):
for idx in [idx1, idx2]:
self.assertEqual(idx.min(), pd.Timestamp('2011-01-01', tz=tz))
self.assertEqual(idx.max(), pd.Timestamp('2011-01-03', tz=tz))
+ self.assertEqual(idx.argmin(), 0)
+ self.assertEqual(idx.argmax(), 2)
for op in ['min', 'max']:
# Return NaT
@@ -579,6 +581,8 @@ def test_minmax(self):
for idx in [idx1, idx2]:
self.assertEqual(idx.min(), Timedelta('1 days')),
self.assertEqual(idx.max(), Timedelta('3 days')),
+ self.assertEqual(idx.argmin(), 0)
+ self.assertEqual(idx.argmax(), 2)
for op in ['min', 'max']:
# Return NaT
@@ -1209,6 +1213,10 @@ def test_minmax(self):
for idx in [idx1, idx2]:
self.assertEqual(idx.min(), pd.Period('2011-01-01', freq='D'))
self.assertEqual(idx.max(), pd.Period('2011-01-03', freq='D'))
+ self.assertEqual(idx1.argmin(), 1)
+ self.assertEqual(idx2.argmin(), 0)
+ self.assertEqual(idx1.argmax(), 3)
+ self.assertEqual(idx2.argmax(), 2)
for op in ['min', 'max']:
# Return NaT
| Add `_isnan` to `DatetimeIndexOpsMixin` to cache `NaT` mask.
This leads to some perf improvement which is noticeable in larger data.
### after fix
```
import pandas as pd
import numpy as np
np.random.seed(1)
idx = pd.DatetimeIndex(np.append(np.array([pd.tslib.iNaT]),
np.random.randint(500, 1000, size=100000000)))
%timeit idx.max()
1 loops, best of 3: 599 ms per loop
%timeit idx.min()
1 loops, best of 3: 608 ms per loop
```
### before fix:
```
%timeit idx.max()
1 loops, best of 3: 940 ms per loop
%timeit idx.min()
1 loops, best of 3: 883 ms per loop
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10277 | 2015-06-04T15:19:00Z | 2015-12-10T12:22:48Z | 2015-12-10T12:22:48Z | 2022-10-13T00:16:35Z |
TST: Check series names | diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py
index 0c07dc5c214b4..8f82e2eaea711 100644
--- a/pandas/computation/tests/test_eval.py
+++ b/pandas/computation/tests/test_eval.py
@@ -1207,7 +1207,9 @@ def f():
a = 1
old_a = df.a.copy()
df.eval('a = a + b')
- assert_series_equal(old_a + df.b, df.a)
+ result = old_a + df.b
+ assert_series_equal(result, df.a, check_names=False)
+ self.assertTrue(result.name is None)
f()
@@ -1251,7 +1253,7 @@ def test_date_boolean(self):
res = self.eval('df.dates1 < 20130101', local_dict={'df': df},
engine=self.engine, parser=self.parser)
expec = df.dates1 < '20130101'
- assert_series_equal(res, expec)
+ assert_series_equal(res, expec, check_names=False)
def test_simple_in_ops(self):
if self.parser != 'python':
diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/test_json/test_pandas.py
index 26fae0717f956..39f645aef0154 100644
--- a/pandas/io/tests/test_json/test_pandas.py
+++ b/pandas/io/tests/test_json/test_pandas.py
@@ -409,12 +409,10 @@ def _check_orient(series, orient, dtype=None, numpy=False):
if orient == "records" or orient == "values":
assert_almost_equal(series.values, unser.values)
else:
- try:
- assert_series_equal(series, unser)
- except:
- raise
if orient == "split":
- self.assertEqual(series.name, unser.name)
+ assert_series_equal(series, unser)
+ else:
+ assert_series_equal(series, unser, check_names=False)
def _check_all_orients(series, dtype=None):
_check_orient(series, "columns", dtype=dtype)
@@ -491,7 +489,8 @@ def test_axis_dates(self):
# series
json = self.ts.to_json()
result = read_json(json, typ='series')
- assert_series_equal(result, self.ts)
+ assert_series_equal(result, self.ts, check_names=False)
+ self.assertTrue(result.name is None)
def test_convert_dates(self):
diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py
index 7d52c6ad4cb3b..1177149e7efa6 100755
--- a/pandas/io/tests/test_parsers.py
+++ b/pandas/io/tests/test_parsers.py
@@ -272,7 +272,8 @@ def test_squeeze(self):
b,2
c,3
"""
- expected = Series([1, 2, 3], index=Index(['a', 'b', 'c'], name=0))
+ idx = Index(['a', 'b', 'c'], name=0)
+ expected = Series([1, 2, 3], name=1, index=idx)
result = self.read_table(StringIO(data), sep=',', index_col=0,
header=None, squeeze=True)
tm.assert_isinstance(result, Series)
@@ -2638,7 +2639,7 @@ def test_iteration_open_handle(self):
result = read_table(f, squeeze=True, header=None,
engine='python')
- expected = Series(['DDD', 'EEE', 'FFF', 'GGG'])
+ expected = Series(['DDD', 'EEE', 'FFF', 'GGG'], name=0)
tm.assert_series_equal(result, expected)
def test_iterator(self):
diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index 9576f80696350..54d53c909d9ca 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -1256,7 +1256,7 @@ def test_transactions(self):
self._transaction_test()
def test_get_schema_create_table(self):
- # Use a dataframe without a bool column, since MySQL converts bool to
+ # Use a dataframe without a bool column, since MySQL converts bool to
# TINYINT (which read_sql_table returns as an int and causes a dtype
# mismatch)
@@ -2025,7 +2025,7 @@ def test_tquery(self):
frame = tm.makeTimeDataFrame()
sql.write_frame(frame, name='test_table', con=self.db)
result = sql.tquery("select A from test_table", self.db)
- expected = Series(frame.A, frame.index) # not to have name
+ expected = Series(frame.A.values, frame.index) # not to have name
result = Series(result, frame.index)
tm.assert_series_equal(result, expected)
@@ -2370,7 +2370,7 @@ def test_tquery(self):
cur.execute(drop_sql)
sql.write_frame(frame, name='test_table', con=self.db, flavor='mysql')
result = sql.tquery("select A from test_table", self.db)
- expected = Series(frame.A, frame.index) # not to have name
+ expected = Series(frame.A.values, frame.index) # not to have name
result = Series(result, frame.index)
tm.assert_series_equal(result, expected)
diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py
index 96e5ff87fbb0c..b506758355228 100644
--- a/pandas/sparse/tests/test_sparse.py
+++ b/pandas/sparse/tests/test_sparse.py
@@ -12,7 +12,8 @@
dec = np.testing.dec
from pandas.util.testing import (assert_almost_equal, assert_series_equal,
- assert_frame_equal, assert_panel_equal, assertRaisesRegexp, assert_array_equal)
+ assert_frame_equal, assert_panel_equal, assertRaisesRegexp,
+ assert_array_equal, assert_attr_equal)
from numpy.testing import assert_equal
from pandas import Series, DataFrame, bdate_range, Panel, MultiIndex
@@ -76,9 +77,11 @@ def _test_data2_zero():
return arr, index
-def assert_sp_series_equal(a, b, exact_indices=True):
+def assert_sp_series_equal(a, b, exact_indices=True, check_names=True):
assert(a.index.equals(b.index))
assert_sp_array_equal(a, b)
+ if check_names:
+ assert_attr_equal('name', a, b)
def assert_sp_frame_equal(left, right, exact_indices=True):
@@ -130,7 +133,6 @@ def setUp(self):
self.bseries = SparseSeries(arr, index=index, kind='block',
name='bseries')
-
self.ts = self.bseries
self.btseries = SparseSeries(arr, index=date_index, kind='block')
@@ -168,10 +170,10 @@ def test_construct_DataFrame_with_sp_series(self):
df.dtypes
str(df)
- assert_sp_series_equal(df['col'], self.bseries)
+ assert_sp_series_equal(df['col'], self.bseries, check_names=False)
result = df.iloc[:, 0]
- assert_sp_series_equal(result, self.bseries)
+ assert_sp_series_equal(result, self.bseries, check_names=False)
# blocking
expected = Series({'col': 'float64:sparse'})
@@ -209,14 +211,16 @@ def test_dense_to_sparse(self):
bseries = series.to_sparse(kind='block')
iseries = series.to_sparse(kind='integer')
assert_sp_series_equal(bseries, self.bseries)
- assert_sp_series_equal(iseries, self.iseries)
+ assert_sp_series_equal(iseries, self.iseries, check_names=False)
+ self.assertEqual(iseries.name, self.bseries.name)
# non-NaN fill value
series = self.zbseries.to_dense()
zbseries = series.to_sparse(kind='block', fill_value=0)
ziseries = series.to_sparse(kind='integer', fill_value=0)
assert_sp_series_equal(zbseries, self.zbseries)
- assert_sp_series_equal(ziseries, self.ziseries)
+ assert_sp_series_equal(ziseries, self.ziseries, check_names=False)
+ self.assertEqual(ziseries.name, self.zbseries.name)
def test_to_dense_preserve_name(self):
assert(self.bseries.name is not None)
@@ -244,7 +248,7 @@ def _check_const(sparse, name):
# use passed name
result = SparseSeries(sparse, name='x')
- assert_sp_series_equal(result, sparse)
+ assert_sp_series_equal(result, sparse, check_names=False)
self.assertEqual(result.name, 'x')
_check_const(self.bseries, 'bseries')
@@ -1301,7 +1305,7 @@ def _check_frame(frame):
# insert SparseSeries
frame['E'] = frame['A']
tm.assert_isinstance(frame['E'], SparseSeries)
- assert_sp_series_equal(frame['E'], frame['A'])
+ assert_sp_series_equal(frame['E'], frame['A'], check_names=False)
# insert SparseSeries differently-indexed
to_insert = frame['A'][::2]
@@ -1315,13 +1319,14 @@ def _check_frame(frame):
# insert Series
frame['F'] = frame['A'].to_dense()
tm.assert_isinstance(frame['F'], SparseSeries)
- assert_sp_series_equal(frame['F'], frame['A'])
+ assert_sp_series_equal(frame['F'], frame['A'], check_names=False)
# insert Series differently-indexed
to_insert = frame['A'].to_dense()[::2]
frame['G'] = to_insert
expected = to_insert.reindex(
frame.index).fillna(frame.default_fill_value)
+ expected.name = 'G'
assert_series_equal(frame['G'].to_dense(), expected)
# insert ndarray
@@ -1349,18 +1354,18 @@ def _check_frame(frame):
def test_setitem_corner(self):
self.frame['a'] = self.frame['B']
- assert_sp_series_equal(self.frame['a'], self.frame['B'])
+ assert_sp_series_equal(self.frame['a'], self.frame['B'], check_names=False)
def test_setitem_array(self):
arr = self.frame['B']
self.frame['E'] = arr
- assert_sp_series_equal(self.frame['E'], self.frame['B'])
+ assert_sp_series_equal(self.frame['E'], self.frame['B'], check_names=False)
self.frame['F'] = arr[:-1]
index = self.frame.index[:-1]
- assert_sp_series_equal(
- self.frame['E'].reindex(index), self.frame['F'].reindex(index))
+ assert_sp_series_equal(self.frame['E'].reindex(index),
+ self.frame['F'].reindex(index), check_names=False)
def test_delitem(self):
A = self.frame['A']
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 21b64378cfc24..bec688db99114 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -1233,7 +1233,7 @@ def test_codes_dtypes(self):
def test_basic(self):
# test basic creation / coercion of categoricals
- s = Series(self.factor,name='A')
+ s = Series(self.factor, name='A')
self.assertEqual(s.dtype,'category')
self.assertEqual(len(s),len(self.factor))
str(s.values)
@@ -1260,8 +1260,9 @@ def test_basic(self):
df = DataFrame({'A' : s, 'B' : s, 'C' : 1})
result1 = df['A']
result2 = df['B']
- tm.assert_series_equal(result1,s)
- tm.assert_series_equal(result2,s)
+ tm.assert_series_equal(result1, s)
+ tm.assert_series_equal(result2, s, check_names=False)
+ self.assertEqual(result2.name, 'B')
self.assertEqual(len(df),len(self.factor))
str(df.values)
str(df)
@@ -1344,23 +1345,23 @@ def test_construction_frame(self):
# GH8626
# dict creation
- df = DataFrame({ 'A' : list('abc') },dtype='category')
- expected = Series(list('abc'),dtype='category')
- tm.assert_series_equal(df['A'],expected)
+ df = DataFrame({ 'A' : list('abc') }, dtype='category')
+ expected = Series(list('abc'), dtype='category', name='A')
+ tm.assert_series_equal(df['A'], expected)
# to_frame
- s = Series(list('abc'),dtype='category')
+ s = Series(list('abc'), dtype='category')
result = s.to_frame()
- expected = Series(list('abc'),dtype='category')
- tm.assert_series_equal(result[0],expected)
+ expected = Series(list('abc'), dtype='category', name=0)
+ tm.assert_series_equal(result[0], expected)
result = s.to_frame(name='foo')
- expected = Series(list('abc'),dtype='category')
- tm.assert_series_equal(result['foo'],expected)
+ expected = Series(list('abc'), dtype='category', name='foo')
+ tm.assert_series_equal(result['foo'], expected)
# list-like creation
- df = DataFrame(list('abc'),dtype='category')
- expected = Series(list('abc'),dtype='category')
- tm.assert_series_equal(df[0],expected)
+ df = DataFrame(list('abc'), dtype='category')
+ expected = Series(list('abc'), dtype='category', name=0)
+ tm.assert_series_equal(df[0], expected)
# ndim != 1
df = DataFrame([pd.Categorical(list('abc'))])
@@ -1833,7 +1834,11 @@ def f(x):
# Monotonic
df = DataFrame({"a": [5, 15, 25]})
c = pd.cut(df.a, bins=[0,10,20,30,40])
- tm.assert_series_equal(df.a.groupby(c).transform(sum), df['a'])
+
+ result = df.a.groupby(c).transform(sum)
+ tm.assert_series_equal(result, df['a'], check_names=False)
+ self.assertTrue(result.name is None)
+
tm.assert_series_equal(df.a.groupby(c).transform(lambda xs: np.sum(xs)), df['a'])
tm.assert_frame_equal(df.groupby(c).transform(sum), df[['a']])
tm.assert_frame_equal(df.groupby(c).transform(lambda xs: np.max(xs)), df[['a']])
@@ -1845,7 +1850,11 @@ def f(x):
# Non-monotonic
df = DataFrame({"a": [5, 15, 25, -5]})
c = pd.cut(df.a, bins=[-10, 0,10,20,30,40])
- tm.assert_series_equal(df.a.groupby(c).transform(sum), df['a'])
+
+ result = df.a.groupby(c).transform(sum)
+ tm.assert_series_equal(result, df['a'], check_names=False)
+ self.assertTrue(result.name is None)
+
tm.assert_series_equal(df.a.groupby(c).transform(lambda xs: np.sum(xs)), df['a'])
tm.assert_frame_equal(df.groupby(c).transform(sum), df[['a']])
tm.assert_frame_equal(df.groupby(c).transform(lambda xs: np.sum(xs)), df[['a']])
@@ -1983,19 +1992,19 @@ def test_slicing(self):
df = DataFrame({'value': (np.arange(100)+1).astype('int64')})
df['D'] = pd.cut(df.value, bins=[0,25,50,75,100])
- expected = Series([11,'(0, 25]'],index=['value','D'])
+ expected = Series([11,'(0, 25]'], index=['value','D'], name=10)
result = df.iloc[10]
- tm.assert_series_equal(result,expected)
+ tm.assert_series_equal(result, expected)
expected = DataFrame({'value': np.arange(11,21).astype('int64')},
index=np.arange(10,20).astype('int64'))
expected['D'] = pd.cut(expected.value, bins=[0,25,50,75,100])
result = df.iloc[10:20]
- tm.assert_frame_equal(result,expected)
+ tm.assert_frame_equal(result, expected)
- expected = Series([9,'(0, 25]'],index=['value','D'])
+ expected = Series([9,'(0, 25]'],index=['value', 'D'], name=8)
result = df.loc[8]
- tm.assert_series_equal(result,expected)
+ tm.assert_series_equal(result, expected)
def test_slicing_and_getting_ops(self):
@@ -2151,7 +2160,8 @@ def test_slicing_doc_examples(self):
tm.assert_series_equal(result, expected)
result = df.loc["h":"j","cats"]
- expected = Series(Categorical(['a','b','b'],categories=['a','b','c']),index=['h','i','j'])
+ expected = Series(Categorical(['a','b','b'], name='cats',
+ categories=['a','b','c']), index=['h','i','j'])
tm.assert_series_equal(result, expected)
result = df.ix["h":"j",0:1]
@@ -2832,21 +2842,21 @@ def test_to_records(self):
# GH8626
# dict creation
- df = DataFrame({ 'A' : list('abc') },dtype='category')
- expected = Series(list('abc'),dtype='category')
- tm.assert_series_equal(df['A'],expected)
+ df = DataFrame({ 'A' : list('abc') }, dtype='category')
+ expected = Series(list('abc'), dtype='category', name='A')
+ tm.assert_series_equal(df['A'], expected)
# list-like creation
- df = DataFrame(list('abc'),dtype='category')
- expected = Series(list('abc'),dtype='category')
- tm.assert_series_equal(df[0],expected)
+ df = DataFrame(list('abc'), dtype='category')
+ expected = Series(list('abc'), dtype='category', name=0)
+ tm.assert_series_equal(df[0], expected)
# to record array
# this coerces
result = df.to_records()
expected = np.rec.array([(0, 'a'), (1, 'b'), (2, 'c')],
dtype=[('index', '<i8'), ('0', 'O')])
- tm.assert_almost_equal(result,expected)
+ tm.assert_almost_equal(result, expected)
def test_numeric_like_ops(self):
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index f74cb07557342..33614b7d87c0f 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -189,8 +189,8 @@ def test_setitem_list(self):
data = self.frame[['A', 'B']]
self.frame[['B', 'A']] = data
- assert_series_equal(self.frame['B'], data['A'])
- assert_series_equal(self.frame['A'], data['B'])
+ assert_series_equal(self.frame['B'], data['A'], check_names=False)
+ assert_series_equal(self.frame['A'], data['B'], check_names=False)
with assertRaisesRegexp(ValueError, 'Columns must be same length as key'):
data[['A']] = self.frame[['A', 'B']]
@@ -202,13 +202,13 @@ def test_setitem_list(self):
df.ix[1, ['tt1', 'tt2']] = [1, 2]
result = df.ix[1, ['tt1', 'tt2']]
- expected = Series([1, 2], df.columns, dtype=np.int_)
+ expected = Series([1, 2], df.columns, dtype=np.int_, name=1)
assert_series_equal(result, expected)
df['tt1'] = df['tt2'] = '0'
df.ix[1, ['tt1', 'tt2']] = ['1', '2']
result = df.ix[1, ['tt1', 'tt2']]
- expected = Series(['1', '2'], df.columns)
+ expected = Series(['1', '2'], df.columns, name=1)
assert_series_equal(result, expected)
def test_setitem_list_not_dataframe(self):
@@ -221,7 +221,7 @@ def test_setitem_list_of_tuples(self):
self.frame['tuples'] = tuples
result = self.frame['tuples']
- expected = Series(tuples, index=self.frame.index)
+ expected = Series(tuples, index=self.frame.index, name='tuples')
assert_series_equal(result, expected)
def test_setitem_mulit_index(self):
@@ -505,7 +505,9 @@ def test_getitem_setitem_ix_negative_integers(self):
a = DataFrame(randn(20, 2), index=[chr(x + 65) for x in range(20)])
a.ix[-1] = a.ix[-2]
- assert_series_equal(a.ix[-1], a.ix[-2])
+ assert_series_equal(a.ix[-1], a.ix[-2], check_names=False)
+ self.assertEqual(a.ix[-1].name, 'T')
+ self.assertEqual(a.ix[-2].name, 'S')
def test_getattr(self):
tm.assert_series_equal(self.frame.A, self.frame['A'])
@@ -574,7 +576,7 @@ def f():
def test_setitem_tuple(self):
self.frame['A', 'B'] = self.frame['A']
- assert_series_equal(self.frame['A', 'B'], self.frame['A'])
+ assert_series_equal(self.frame['A', 'B'], self.frame['A'], check_names=False)
def test_setitem_always_copy(self):
s = self.frame['A'].copy()
@@ -772,16 +774,16 @@ def test_setitem_clear_caches(self):
df.ix[2:, 'z'] = 42
- expected = Series([np.nan, np.nan, 42, 42], index=df.index)
+ expected = Series([np.nan, np.nan, 42, 42], index=df.index, name='z')
self.assertIsNot(df['z'], foo)
assert_series_equal(df['z'], expected)
def test_setitem_None(self):
# GH #766
self.frame[None] = self.frame['A']
- assert_series_equal(self.frame.iloc[:,-1], self.frame['A'])
- assert_series_equal(self.frame.loc[:,None], self.frame['A'])
- assert_series_equal(self.frame[None], self.frame['A'])
+ assert_series_equal(self.frame.iloc[:,-1], self.frame['A'], check_names=False)
+ assert_series_equal(self.frame.loc[:,None], self.frame['A'], check_names=False)
+ assert_series_equal(self.frame[None], self.frame['A'], check_names=False)
repr(self.frame)
def test_setitem_empty(self):
@@ -1077,7 +1079,8 @@ def test_setitem_fancy_mixed_2d(self):
self.assertTrue(isnull(self.mixed_frame.ix[5]).all())
self.mixed_frame.ix[5] = self.mixed_frame.ix[6]
- assert_series_equal(self.mixed_frame.ix[5], self.mixed_frame.ix[6])
+ assert_series_equal(self.mixed_frame.ix[5], self.mixed_frame.ix[6],
+ check_names=False)
# #1432
df = DataFrame({1: [1., 2., 3.],
@@ -1092,7 +1095,7 @@ def test_setitem_fancy_mixed_2d(self):
assert_frame_equal(df, expected)
def test_ix_align(self):
- b = Series(randn(10))
+ b = Series(randn(10), name=0)
b.sort()
df_orig = DataFrame(randn(10, 4))
df = df_orig.copy()
@@ -2040,14 +2043,15 @@ def test_setitem_with_sparse_value(self):
df = pd.DataFrame({'c_1':['a', 'b', 'c'], 'n_1': [1., 2., 3.]})
sp_series = pd.Series([0, 0, 1]).to_sparse(fill_value=0)
df['new_column'] = sp_series
- tm.assert_series_equal(df['new_column'], sp_series)
+ tm.assert_series_equal(df['new_column'], sp_series, check_names=False)
def test_setitem_with_unaligned_sparse_value(self):
df = pd.DataFrame({'c_1':['a', 'b', 'c'], 'n_1': [1., 2., 3.]})
sp_series = (pd.Series([0, 0, 1], index=[2, 1, 0])
.to_sparse(fill_value=0))
df['new_column'] = sp_series
- tm.assert_series_equal(df['new_column'], pd.Series([1, 0, 0]))
+ exp = pd.Series([1, 0, 0], name='new_column')
+ tm.assert_series_equal(df['new_column'], exp)
_seriesd = tm.getSeriesData()
@@ -2452,7 +2456,8 @@ def test_set_index_cast_datetimeindex(self):
# assignt to frame
df['B'] = i
result = df['B']
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
+ self.assertEqual(result.name, 'B')
# keep the timezone
result = i.to_series(keep_tz=True)
@@ -2468,7 +2473,8 @@ def test_set_index_cast_datetimeindex(self):
# list of datetimes with a tz
df['D'] = i.to_pydatetime()
result = df['D']
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
+ self.assertEqual(result.name, 'D')
# GH 6785
# set the index manually
@@ -5929,7 +5935,13 @@ def test_combineSeries(self):
added = self.tsframe + ts
for key, col in compat.iteritems(self.tsframe):
- assert_series_equal(added[key], col + ts)
+ result = col + ts
+ assert_series_equal(added[key], result, check_names=False)
+ self.assertEqual(added[key].name, key)
+ if col.name == ts.name:
+ self.assertEqual(result.name, 'A')
+ else:
+ self.assertTrue(result.name is None)
smaller_frame = self.tsframe[:-5]
smaller_added = smaller_frame + ts
@@ -7592,7 +7604,7 @@ def test_dropEmptyRows(self):
mat[:5] = nan
frame = DataFrame({'foo': mat}, index=self.frame.index)
- original = Series(mat, index=self.frame.index)
+ original = Series(mat, index=self.frame.index, name='foo')
expected = original.dropna()
inplace_frame1, inplace_frame2 = frame.copy(), frame.copy()
@@ -7615,7 +7627,7 @@ def test_dropIncompleteRows(self):
frame = DataFrame({'foo': mat}, index=self.frame.index)
frame['bar'] = 5
- original = Series(mat, index=self.frame.index)
+ original = Series(mat, index=self.frame.index, name='foo')
inp_frame1, inp_frame2 = frame.copy(), frame.copy()
smaller_frame = frame.dropna()
@@ -7692,8 +7704,8 @@ def test_dropna(self):
def test_drop_and_dropna_caching(self):
# tst that cacher updates
- original = Series([1, 2, np.nan])
- expected = Series([1, 2], dtype=original.dtype)
+ original = Series([1, 2, np.nan], name='A')
+ expected = Series([1, 2], dtype=original.dtype, name='A')
df = pd.DataFrame({'A': original.values.copy()})
df2 = df.copy()
df['A'].dropna()
@@ -9299,7 +9311,7 @@ def test_xs_corner(self):
# no columns but index
df = DataFrame(index=['a', 'b', 'c'])
result = df.xs('a')
- expected = Series([])
+ expected = Series([], name='a')
assert_series_equal(result, expected)
def test_xs_duplicates(self):
@@ -9738,7 +9750,8 @@ def _check_get(df, cond, check_dtypes = True):
rs = df.where(cond, other1)
rs2 = df.where(cond.values, other1)
for k, v in rs.iteritems():
- assert_series_equal(v, Series(np.where(cond[k], df[k], other1[k]),index=v.index))
+ exp = Series(np.where(cond[k], df[k], other1[k]),index=v.index)
+ assert_series_equal(v, exp, check_names=False)
assert_frame_equal(rs, rs2)
# dtypes
@@ -9779,7 +9792,7 @@ def _check_align(df, cond, other, check_dtypes = True):
o = other[k].values
new_values = d if c.all() else np.where(c, d, o)
- expected = Series(new_values,index=result.index)
+ expected = Series(new_values, index=result.index, name=k)
# since we can't always have the correct numpy dtype
# as numpy doesn't know how to downcast, don't check
@@ -10226,7 +10239,7 @@ def test_shift(self):
d = self.tsframe.index[0]
shifted_d = d + datetools.BDay(5)
assert_series_equal(self.tsframe.xs(d),
- shiftedFrame.xs(shifted_d))
+ shiftedFrame.xs(shifted_d), check_names=False)
# shift int frame
int_shifted = self.intframe.shift(1)
@@ -11264,27 +11277,27 @@ def test_combine_first_mixed_bug(self):
df2 = DataFrame([[-42.6, np.nan, True], [-5., 1.6, False]], index=[1, 2])
result = df1.combine_first(df2)[2]
- expected = Series([True,True,False])
- assert_series_equal(result,expected)
+ expected = Series([True, True, False], name=2)
+ assert_series_equal(result, expected)
# GH 3593, converting datetime64[ns] incorrecly
df0 = DataFrame({"a":[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]})
df1 = DataFrame({"a":[None, None, None]})
df2 = df1.combine_first(df0)
- assert_frame_equal(df2,df0)
+ assert_frame_equal(df2, df0)
df2 = df0.combine_first(df1)
- assert_frame_equal(df2,df0)
+ assert_frame_equal(df2, df0)
df0 = DataFrame({"a":[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]})
df1 = DataFrame({"a":[datetime(2000, 1, 2), None, None]})
df2 = df1.combine_first(df0)
result = df0.copy()
result.iloc[0,:] = df1.iloc[0,:]
- assert_frame_equal(df2,result)
+ assert_frame_equal(df2, result)
df2 = df0.combine_first(df1)
- assert_frame_equal(df2,df0)
+ assert_frame_equal(df2, df0)
def test_update(self):
df = DataFrame([[1.5, nan, 3.],
@@ -11497,8 +11510,14 @@ def test_clip_against_series(self):
ub_mask = df.iloc[:, i] >= ub
mask = ~lb_mask & ~ub_mask
- assert_series_equal(clipped_df.loc[lb_mask, i], lb[lb_mask])
- assert_series_equal(clipped_df.loc[ub_mask, i], ub[ub_mask])
+ result = clipped_df.loc[lb_mask, i]
+ assert_series_equal(result, lb[lb_mask], check_names=False)
+ self.assertEqual(result.name, i)
+
+ result = clipped_df.loc[ub_mask, i]
+ assert_series_equal(result, ub[ub_mask], check_names=False)
+ self.assertEqual(result.name, i)
+
assert_series_equal(clipped_df.loc[mask, i], df.loc[mask, i])
def test_clip_against_frame(self):
@@ -11837,7 +11856,12 @@ def alt(x):
[0, 1, 2, 0, 1, 2],
[0, 1, 0, 1, 0, 1]])
df = DataFrame(np.random.randn(6, 3), index=index)
- assert_series_equal(df.kurt(), df.kurt(level=0).xs('bar'))
+
+ kurt = df.kurt()
+ kurt2 = df.kurt(level=0).xs('bar')
+ assert_series_equal(kurt, kurt2, check_names=False)
+ self.assertTrue(kurt.name is None)
+ self.assertEqual(kurt2.name, 'bar')
def _check_stat_op(self, name, alternative, frame=None, has_skipna=True,
has_numeric_only=False, check_dtype=True, check_dates=False,
@@ -13118,63 +13142,64 @@ def test_constructor_with_convert(self):
# #2845
df = DataFrame({'A' : [2**63-1] })
result = df['A']
- expected = Series(np.asarray([2**63-1], np.int64))
+ expected = Series(np.asarray([2**63-1], np.int64), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [2**63] })
result = df['A']
- expected = Series(np.asarray([2**63], np.object_))
+ expected = Series(np.asarray([2**63], np.object_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [datetime(2005, 1, 1), True] })
result = df['A']
- expected = Series(np.asarray([datetime(2005, 1, 1), True], np.object_))
+ expected = Series(np.asarray([datetime(2005, 1, 1), True], np.object_),
+ name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [None, 1] })
result = df['A']
- expected = Series(np.asarray([np.nan, 1], np.float_))
+ expected = Series(np.asarray([np.nan, 1], np.float_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [1.0, 2] })
result = df['A']
- expected = Series(np.asarray([1.0, 2], np.float_))
+ expected = Series(np.asarray([1.0, 2], np.float_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [1.0+2.0j, 3] })
result = df['A']
- expected = Series(np.asarray([1.0+2.0j, 3], np.complex_))
+ expected = Series(np.asarray([1.0+2.0j, 3], np.complex_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [1.0+2.0j, 3.0] })
result = df['A']
- expected = Series(np.asarray([1.0+2.0j, 3.0], np.complex_))
+ expected = Series(np.asarray([1.0+2.0j, 3.0], np.complex_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [1.0+2.0j, True] })
result = df['A']
- expected = Series(np.asarray([1.0+2.0j, True], np.object_))
+ expected = Series(np.asarray([1.0+2.0j, True], np.object_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [1.0, None] })
result = df['A']
- expected = Series(np.asarray([1.0, np.nan], np.float_))
+ expected = Series(np.asarray([1.0, np.nan], np.float_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [1.0+2.0j, None] })
result = df['A']
- expected = Series(np.asarray([1.0+2.0j, np.nan], np.complex_))
+ expected = Series(np.asarray([1.0+2.0j, np.nan], np.complex_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [2.0, 1, True, None] })
result = df['A']
- expected = Series(np.asarray([2.0, 1, True, None], np.object_))
+ expected = Series(np.asarray([2.0, 1, True, None], np.object_), name='A')
assert_series_equal(result, expected)
df = DataFrame({'A' : [2.0, 1, datetime(2006, 1, 1), None] })
result = df['A']
expected = Series(np.asarray([2.0, 1, datetime(2006, 1, 1),
- None], np.object_))
+ None], np.object_), name='A')
assert_series_equal(result, expected)
def test_construction_with_mixed(self):
@@ -13282,8 +13307,8 @@ def test_assign_columns(self):
frame = self.frame.copy()
frame.columns = ['foo', 'bar', 'baz', 'quux', 'foo2']
- assert_series_equal(self.frame['C'], frame['baz'])
- assert_series_equal(self.frame['hi'], frame['foo2'])
+ assert_series_equal(self.frame['C'], frame['baz'], check_names=False)
+ assert_series_equal(self.frame['hi'], frame['foo2'], check_names=False)
def test_columns_with_dups(self):
@@ -13593,9 +13618,12 @@ def test_dot(self):
# Check series argument
result = a.dot(b['one'])
- assert_series_equal(result, expected['one'])
+ assert_series_equal(result, expected['one'], check_names=False)
+ self.assertTrue(result.name is None)
+
result = a.dot(b1['one'])
- assert_series_equal(result, expected['one'])
+ assert_series_equal(result, expected['one'], check_names=False)
+ self.assertTrue(result.name is None)
# can pass correct-length arrays
row = a.ix[0].values
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index 3f751310438e4..a03fe3c2241a3 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -921,8 +921,8 @@ def test_describe_empty(self):
def test_describe_none(self):
noneSeries = Series([None])
noneSeries.name = 'None'
- assert_series_equal(noneSeries.describe(),
- Series([0, 0], index=['count', 'unique']))
+ expected = Series([0, 0], index=['count', 'unique'], name='None')
+ assert_series_equal(noneSeries.describe(), expected)
class TestDataFrame(tm.TestCase, Generic):
@@ -980,11 +980,11 @@ def test_interp_combo(self):
'C': [1, 2, 3, 5], 'D': list('abcd')})
result = df['A'].interpolate()
- expected = Series([1., 2., 3., 4.])
+ expected = Series([1., 2., 3., 4.], name='A')
assert_series_equal(result, expected)
result = df['A'].interpolate(downcast='infer')
- expected = Series([1, 2, 3, 4])
+ expected = Series([1, 2, 3, 4], name='A')
assert_series_equal(result, expected)
def test_interp_nan_idx(self):
@@ -1519,7 +1519,7 @@ def test_set_attribute(self):
df.y = 5
assert_equal(df.y, 5)
- assert_series_equal(df['y'], Series([2, 4, 6]))
+ assert_series_equal(df['y'], Series([2, 4, 6], name='y'))
class TestPanel(tm.TestCase, Generic):
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index 82f4b8c05ca06..433645448fe2b 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -454,12 +454,12 @@ def is_grid_on():
self.plt.subplot(1,4*len(kinds),spndx); spndx+=1
mpl.rc('axes',grid=False)
obj.plot(kind=kind, **kws)
- self.assertFalse(is_grid_on())
+ self.assertFalse(is_grid_on())
self.plt.subplot(1,4*len(kinds),spndx); spndx+=1
mpl.rc('axes',grid=True)
obj.plot(kind=kind, grid=False, **kws)
- self.assertFalse(is_grid_on())
+ self.assertFalse(is_grid_on())
if kind != 'pie':
self.plt.subplot(1,4*len(kinds),spndx); spndx+=1
@@ -1143,7 +1143,7 @@ def test_table(self):
@slow
def test_series_grid_settings(self):
# Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
- self._check_grid_settings(Series([1,2,3]),
+ self._check_grid_settings(Series([1,2,3]),
plotting._series_kinds + plotting._common_kinds)
@@ -1376,7 +1376,7 @@ def test_unsorted_index(self):
ax = df.plot()
l = ax.get_lines()[0]
rs = l.get_xydata()
- rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64)
+ rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64, name='y')
tm.assert_series_equal(rs, df.y)
@slow
@@ -3467,9 +3467,9 @@ def test_sharey_and_ax(self):
@slow
def test_df_grid_settings(self):
# Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
- self._check_grid_settings(DataFrame({'a':[1,2,3],'b':[2,3,4]}),
+ self._check_grid_settings(DataFrame({'a':[1,2,3],'b':[2,3,4]}),
plotting._dataframe_kinds, kws={'x':'a','y':'b'})
-
+
@tm.mplskip
class TestDataFrameGroupByPlots(TestPlotBase):
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 0789e20df3945..e1ae65c95b965 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -286,7 +286,9 @@ def test_nth(self):
g = df[0]
expected = s.groupby(g).first()
expected2 = s.groupby(g).apply(lambda x: x.iloc[0])
- assert_series_equal(expected2,expected)
+ assert_series_equal(expected2, expected, check_names=False)
+ self.assertTrue(expected.name, 0)
+ self.assertEqual(expected.name, 1)
# validate first
v = s[g==1].iloc[0]
@@ -1098,7 +1100,7 @@ def nsum(x):
df.groupby('col1').transform(nsum)['col2'],
df.groupby('col1')['col2'].transform(nsum)]
for result in results:
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
def test_with_na(self):
index = Index(np.arange(10))
@@ -1251,9 +1253,9 @@ def test_series_describe_multikey(self):
ts = tm.makeTimeSeries()
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.describe().unstack()
- assert_series_equal(result['mean'], grouped.mean())
- assert_series_equal(result['std'], grouped.std())
- assert_series_equal(result['min'], grouped.min())
+ assert_series_equal(result['mean'], grouped.mean(), check_names=False)
+ assert_series_equal(result['std'], grouped.std(), check_names=False)
+ assert_series_equal(result['min'], grouped.min(), check_names=False)
def test_series_describe_single(self):
ts = tm.makeTimeSeries()
@@ -1304,7 +1306,7 @@ def test_frame_describe_multikey(self):
for col in self.tsframe:
expected = grouped[col].describe()
- assert_series_equal(result[col], expected)
+ assert_series_equal(result[col], expected, check_names=False)
groupedT = self.tsframe.groupby({'A': 0, 'B': 0,
'C': 1, 'D': 1}, axis=1)
@@ -1901,7 +1903,6 @@ def test_builtins_apply(self): # GH8155
df = pd.DataFrame(np.random.randint(1, 50, (1000, 2)),
columns=['jim', 'joe'])
df['jolie'] = np.random.randn(1000)
- print(df.head())
for keys in ['jim', ['jim', 'joe']]: # single key & multi-key
if keys == 'jim': continue
@@ -1949,6 +1950,7 @@ def _testit(op):
expd.setdefault(cat1, {})[cat2] = op(group['C'])
exp = DataFrame(expd).T.stack(dropna=False)
exp.index.names = ['A', 'B']
+ exp.name = 'C'
result = op(grouped)['C']
assert_series_equal(result, exp)
@@ -2207,7 +2209,8 @@ def trans2(group):
result = df.groupby('A').apply(trans)
exp = df.groupby('A')['C'].apply(trans2)
- assert_series_equal(result, exp)
+ assert_series_equal(result, exp, check_names=False)
+ self.assertEqual(result.name, 'C')
def test_apply_transform(self):
grouped = self.ts.groupby(lambda x: x.month)
@@ -3106,7 +3109,8 @@ def test_rank_apply(self):
expected.append(piece.value.rank())
expected = concat(expected, axis=0)
expected = expected.reindex(result.index)
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
+ self.assertTrue(result.name is None)
result = df.groupby(['key1', 'key2']).value.rank(pct=True)
@@ -3115,7 +3119,8 @@ def test_rank_apply(self):
expected.append(piece.value.rank(pct=True))
expected = concat(expected, axis=0)
expected = expected.reindex(result.index)
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
+ self.assertTrue(result.name is None)
def test_dont_clobber_name_column(self):
df = DataFrame({'key': ['a', 'a', 'a', 'b', 'b', 'b'],
@@ -3147,7 +3152,8 @@ def test_skip_group_keys(self):
pieces.append(group.order()[:3])
expected = concat(pieces)
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
+ self.assertTrue(result.name is None)
def test_no_nonsense_name(self):
# GH #995
@@ -4328,7 +4334,7 @@ def test_filter_using_len(self):
s = df['B']
grouped = s.groupby(s)
actual = grouped.filter(lambda x: len(x) > 2)
- expected = Series(4*['b'], index=np.arange(2, 6))
+ expected = Series(4*['b'], index=np.arange(2, 6), name='B')
assert_series_equal(actual, expected)
actual = grouped.filter(lambda x: len(x) > 4)
@@ -4410,7 +4416,7 @@ def test_filter_and_transform_with_non_unique_int_index(self):
# Transform Series
actual = grouped_ser.transform(len)
- expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index)
+ expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name='pid')
assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
@@ -4450,7 +4456,7 @@ def test_filter_and_transform_with_multiple_non_unique_int_index(self):
# Transform Series
actual = grouped_ser.transform(len)
- expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index)
+ expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name='pid')
assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
@@ -4530,7 +4536,7 @@ def test_filter_and_transform_with_non_unique_float_index(self):
# Transform Series
actual = grouped_ser.transform(len)
- expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index)
+ expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name='pid')
assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
@@ -4573,7 +4579,7 @@ def test_filter_and_transform_with_non_unique_timestamp_index(self):
# Transform Series
actual = grouped_ser.transform(len)
- expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index)
+ expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name='pid')
assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
@@ -4613,7 +4619,7 @@ def test_filter_and_transform_with_non_unique_string_index(self):
# Transform Series
actual = grouped_ser.transform(len)
- expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index)
+ expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name='pid')
assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index c998ce65791a3..c6d31fb8d0449 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -373,8 +373,8 @@ def test_imethods_with_dups(self):
df = s.to_frame()
result = df.iloc[2]
- expected = Series(2,index=[0])
- assert_series_equal(result,expected)
+ expected = Series(2, index=[0], name=2)
+ assert_series_equal(result, expected)
result = df.iat[2,0]
expected = 2
@@ -512,7 +512,7 @@ def test_iloc_getitem_dups(self):
self.assertTrue(isnull(result))
result = df.iloc[0,:]
- expected = Series([np.nan,1,3,3],index=['A','B','A','B'])
+ expected = Series([np.nan, 1, 3, 3], index=['A','B','A','B'], name=0)
assert_series_equal(result,expected)
def test_iloc_getitem_array(self):
@@ -1021,8 +1021,8 @@ def test_loc_general(self):
# mixed type
result = DataFrame({ 'a' : [Timestamp('20130101')], 'b' : [1] }).iloc[0]
- expected = Series([ Timestamp('20130101'), 1],index=['a','b'])
- assert_series_equal(result,expected)
+ expected = Series([ Timestamp('20130101'), 1], index=['a','b'], name=0)
+ assert_series_equal(result, expected)
self.assertEqual(result.dtype, object)
def test_loc_setitem_consistency(self):
@@ -1126,12 +1126,12 @@ def test_loc_setitem_frame(self):
# setting with mixed labels
df = DataFrame({1:[1,2],2:[3,4],'a':['a','b']})
- result = df.loc[0,[1,2]]
- expected = Series([1,3],index=[1,2],dtype=object)
- assert_series_equal(result,expected)
+ result = df.loc[0, [1,2]]
+ expected = Series([1,3],index=[1,2],dtype=object, name=0)
+ assert_series_equal(result, expected)
expected = DataFrame({1:[5,2],2:[6,4],'a':['a','b']})
- df.loc[0,[1,2]] = [5,6]
+ df.loc[0, [1,2]] = [5,6]
assert_frame_equal(df, expected)
def test_loc_setitem_frame_multiples(self):
@@ -1404,8 +1404,8 @@ def f():
df.ix[2:5, 'bar'] = np.array([2.33j, 1.23+0.1j, 2.2, 1.0])
result = df.ix[2:5, 'bar']
- expected = Series([2.33j, 1.23+0.1j, 2.2, 1.0],index=[2,3,4,5])
- assert_series_equal(result,expected)
+ expected = Series([2.33j, 1.23+0.1j, 2.2, 1.0], index=[2,3,4,5], name='bar')
+ assert_series_equal(result, expected)
# dtype getting changed?
df = DataFrame(index=Index(lrange(1,11)))
@@ -1473,7 +1473,9 @@ def test_iloc_getitem_multiindex(self):
# the first row
rs = mi_int.iloc[0]
xp = mi_int.ix[4].ix[8]
- assert_series_equal(rs, xp)
+ assert_series_equal(rs, xp, check_names=False)
+ self.assertEqual(rs.name, (4, 8))
+ self.assertEqual(xp.name, 8)
# 2nd (last) columns
rs = mi_int.iloc[:,2]
@@ -2955,14 +2957,14 @@ def test_mi_access(self):
# GH 4146, not returning a block manager when selecting a unique index
# from a duplicate index
# as of 4879, this returns a Series (which is similar to what happens with a non-unique)
- expected = Series(['a',1,1],index=['h1','h3','h5'])
+ expected = Series(['a',1,1], index=['h1','h3','h5'], name='A1')
result = df2['A']['A1']
- assert_series_equal(result,expected)
+ assert_series_equal(result, expected)
# selecting a non_unique from the 2nd level
expected = DataFrame([['d',4,4],['e',5,5]],index=Index(['B2','B2'],name='sub'),columns=['h1','h3','h5'],).T
result = df2['A']['B2']
- assert_frame_equal(result,expected)
+ assert_frame_equal(result, expected)
def test_non_unique_loc_memory_error(self):
@@ -3057,15 +3059,16 @@ def test_dups_loc(self):
# GH4726
# dup indexing with iloc/loc
- df = DataFrame([[1,2,'foo','bar',Timestamp('20130101')]],
- columns=['a','a','a','a','a'],index=[1])
- expected = Series([1,2,'foo','bar',Timestamp('20130101')],index=['a','a','a','a','a'])
+ df = DataFrame([[1, 2, 'foo', 'bar', Timestamp('20130101')]],
+ columns=['a','a','a','a','a'], index=[1])
+ expected = Series([1, 2, 'foo', 'bar', Timestamp('20130101')],
+ index=['a','a','a','a','a'], name=1)
result = df.iloc[0]
- assert_series_equal(result,expected)
+ assert_series_equal(result, expected)
result = df.loc[1]
- assert_series_equal(result,expected)
+ assert_series_equal(result, expected)
def test_partial_setting(self):
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index b2efc20aa0694..9460c6373d0d2 100644
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -149,7 +149,7 @@ def test_reindex_level(self):
# Series
result = month_sums['A'].reindex(self.ymd.index, level=1)
expected = self.ymd['A'].groupby(level='month').transform(np.sum)
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
# axis=1
month_sums = self.ymd.T.sum(axis=1, level='month')
@@ -173,6 +173,7 @@ def _check_op(opname):
broadcasted = self.ymd['A'].groupby(
level='month').transform(np.sum)
expected = op(self.ymd['A'], broadcasted)
+ expected.name = 'A'
assert_series_equal(result, expected)
_check_op('sub')
@@ -1209,7 +1210,8 @@ def test_groupby_transform(self):
applied = grouped.apply(lambda x: x * 2)
expected = grouped.transform(lambda x: x * 2)
- assert_series_equal(applied.reindex(expected.index), expected)
+ result = applied.reindex(expected.index)
+ assert_series_equal(result, expected, check_names=False)
def test_unstack_sparse_keyspace(self):
# memory problems with naive impl #2278
diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py
index 57fd465993e14..e86551c6b9158 100644
--- a/pandas/tests/test_panel.py
+++ b/pandas/tests/test_panel.py
@@ -1700,7 +1700,8 @@ def test_compound(self):
compounded = self.panel.compound()
assert_series_equal(compounded['ItemA'],
- (1 + self.panel['ItemA']).product(0) - 1)
+ (1 + self.panel['ItemA']).product(0) - 1,
+ check_names=False)
def test_shift(self):
# major
@@ -2186,7 +2187,7 @@ def test_ops_differently_indexed(self):
# careful, mutation
self.panel['foo'] = lp2['ItemA']
assert_series_equal(self.panel['foo'].reindex(lp2.index),
- lp2['ItemA'])
+ lp2['ItemA'], check_names=False)
def test_ops_scalar(self):
result = self.panel.mul(2)
diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py
index fff3b0c5450b1..7a72200077225 100644
--- a/pandas/tests/test_panel4d.py
+++ b/pandas/tests/test_panel4d.py
@@ -471,7 +471,7 @@ def test_major_xs(self):
idx = self.panel4d.major_axis[5]
xs = self.panel4d.major_xs(idx)
- assert_series_equal(xs['l1'].T['ItemA'], ref.xs(idx))
+ assert_series_equal(xs['l1'].T['ItemA'], ref.xs(idx), check_names=False)
# not contained
idx = self.panel4d.major_axis[0] - bday
@@ -489,7 +489,7 @@ def test_minor_xs(self):
idx = self.panel4d.minor_axis[1]
xs = self.panel4d.minor_xs(idx)
- assert_series_equal(xs['l1'].T['ItemA'], ref[idx])
+ assert_series_equal(xs['l1'].T['ItemA'], ref[idx], check_names=False)
# not contained
self.assertRaises(Exception, self.panel4d.minor_xs, 'E')
@@ -1099,6 +1099,5 @@ def test_to_excel(self):
if __name__ == '__main__':
import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure',
- '--with-timer'],
+ nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index eb583f17f3ace..30566c703d4ec 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -1064,7 +1064,7 @@ def test_pop(self):
result = k.pop('B')
self.assertEqual(result, 4)
- expected = Series([0,0],index=['A','C'])
+ expected = Series([0, 0], index=['A', 'C'], name=4)
assert_series_equal(k, expected)
def test_not_hashable(self):
@@ -1453,8 +1453,10 @@ def test_setitem(self):
# set item that's not contained
s = self.series.copy()
s['foobar'] = 1
- expected = self.series.append(Series([1],index=['foobar']))
- assert_series_equal(s,expected)
+
+ app = Series([1], index=['foobar'], name='series')
+ expected = self.series.append(app)
+ assert_series_equal(s, expected)
def test_setitem_dtypes(self):
@@ -2717,7 +2719,8 @@ def test_round(self):
# numpy.round doesn't preserve metadata, probably a numpy bug,
# re: GH #314
result = np.round(self.ts, 2)
- expected = Series(np.round(self.ts.values, 2), index=self.ts.index)
+ expected = Series(np.round(self.ts.values, 2), index=self.ts.index,
+ name='ts')
assert_series_equal(result, expected)
self.assertEqual(result.name, self.ts.name)
@@ -2866,7 +2869,7 @@ def test_modulo(self):
assert_series_equal(result, expected)
result = p['first'] % 0
- expected = Series(np.nan, index=p.index)
+ expected = Series(np.nan, index=p.index, name='first')
assert_series_equal(result, expected)
p = p.astype('float64')
@@ -2895,13 +2898,13 @@ def test_div(self):
# no longer do integer div for any ops, but deal with the 0's
p = DataFrame({'first': [3, 4, 5, 8], 'second': [0, 0, 0, 3]})
result = p['first'] / p['second']
- expected = Series(
- p['first'].values.astype(float) / p['second'].values, dtype='float64')
+ expected = Series(p['first'].values.astype(float) / p['second'].values,
+ dtype='float64')
expected.iloc[0:3] = np.inf
assert_series_equal(result, expected)
result = p['first'] / 0
- expected = Series(np.inf, index=p.index)
+ expected = Series(np.inf, index=p.index, name='first')
assert_series_equal(result, expected)
p = p.astype('float64')
@@ -2911,7 +2914,8 @@ def test_div(self):
p = DataFrame({'first': [3, 4, 5, 8], 'second': [1, 1, 1, 1]})
result = p['first'] / p['second']
- assert_series_equal(result, p['first'].astype('float64'))
+ assert_series_equal(result, p['first'].astype('float64'), check_names=False)
+ self.assertTrue(result.name is None)
self.assertFalse(np.array_equal(result, p['second'] / p['first']))
# inf signing
@@ -2926,7 +2930,7 @@ def test_div(self):
expected = Series([-0.01,-np.inf])
result = p['second'].div(p['first'])
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
result = p['second'] / p['first']
assert_series_equal(result, expected)
@@ -3101,13 +3105,13 @@ def test_operators_timedelta64(self):
# timestamp on lhs
result = resultb + df['A']
- expected = Series(
- [Timestamp('20111230'), Timestamp('20120101'), Timestamp('20120103')])
+ values = [Timestamp('20111230'), Timestamp('20120101'), Timestamp('20120103')]
+ expected = Series(values, name='A')
assert_series_equal(result, expected)
# datetimes on rhs
result = df['A'] - datetime(2001, 1, 1)
- expected = Series([timedelta(days=4017 + i) for i in range(3)])
+ expected = Series([timedelta(days=4017 + i) for i in range(3)], name='A')
assert_series_equal(result, expected)
self.assertEqual(result.dtype, 'm8[ns]')
@@ -3432,13 +3436,14 @@ def test_ops_datetimelike_align(self):
dt.iloc[2] = np.nan
dt2 = dt[::-1]
- expected = Series([timedelta(0),timedelta(0),pd.NaT])
-
- result = dt2-dt
- assert_series_equal(result,expected)
+ expected = Series([timedelta(0), timedelta(0), pd.NaT])
+ # name is reset
+ result = dt2 - dt
+ assert_series_equal(result, expected)
- result = (dt2.to_frame()-dt.to_frame())[0]
- assert_series_equal(result,expected)
+ expected = Series(expected, name=0)
+ result = (dt2.to_frame() - dt.to_frame())[0]
+ assert_series_equal(result, expected)
def test_timedelta64_functions(self):
@@ -3739,25 +3744,27 @@ def test_datetime64_with_index(self):
# arithmetic integer ops with an index
s = Series(np.random.randn(5))
- expected = s-s.index.to_series()
- result = s-s.index
- assert_series_equal(result,expected)
+ expected = s - s.index.to_series()
+ result = s - s.index
+ assert_series_equal(result, expected)
# GH 4629
# arithmetic datetime64 ops with an index
- s = Series(date_range('20130101',periods=5),index=date_range('20130101',periods=5))
- expected = s-s.index.to_series()
- result = s-s.index
- assert_series_equal(result,expected)
+ s = Series(date_range('20130101', periods=5),
+ index=date_range('20130101', periods=5))
+ expected = s - s.index.to_series()
+ result = s - s.index
+ assert_series_equal(result, expected)
- result = s-s.index.to_period()
- assert_series_equal(result,expected)
+ result = s - s.index.to_period()
+ assert_series_equal(result, expected)
- df = DataFrame(np.random.randn(5,2),index=date_range('20130101',periods=5))
+ df = DataFrame(np.random.randn(5,2),
+ index=date_range('20130101', periods=5))
df['date'] = Timestamp('20130102')
df['expected'] = df['date'] - df.index.to_series()
df['result'] = df['date'] - df.index
- assert_series_equal(df['result'],df['expected'])
+ assert_series_equal(df['result'], df['expected'], check_names=False)
def test_timedelta64_nan(self):
@@ -4934,14 +4941,17 @@ def test_from_csv(self):
with ensure_clean() as path:
self.ts.to_csv(path)
ts = Series.from_csv(path)
- assert_series_equal(self.ts, ts)
+ assert_series_equal(self.ts, ts, check_names=False)
+ self.assertTrue(ts.name is None)
self.assertTrue(ts.index.name is None)
self.series.to_csv(path)
series = Series.from_csv(path)
self.assertIsNone(series.name)
self.assertIsNone(series.index.name)
- assert_series_equal(self.series, series)
+ assert_series_equal(self.series, series, check_names=False)
+ self.assertTrue(series.name is None)
+ self.assertTrue(series.index.name is None)
outfile = open(path, 'w')
outfile.write('1998-01-01|1.0\n1999-01-01|2.0')
@@ -5191,7 +5201,8 @@ def test_tshift(self):
shifted2 = self.ts.tshift(freq=self.ts.index.freq)
assert_series_equal(shifted, shifted2)
- inferred_ts = Series(self.ts.values, Index(np.asarray(self.ts.index)))
+ inferred_ts = Series(self.ts.values, Index(np.asarray(self.ts.index)),
+ name='ts')
shifted = inferred_ts.tshift(1)
unshifted = shifted.tshift(-1)
assert_series_equal(shifted, self.ts.tshift(1))
@@ -5779,7 +5790,7 @@ def test_map_dict_with_tuple_keys(self):
df['labels'] = df['a'].map(label_mappings)
df['expected_labels'] = pd.Series(['A', 'B', 'A', 'B'], index=df.index)
# All labels should be filled now
- tm.assert_series_equal(df['labels'], df['expected_labels'])
+ tm.assert_series_equal(df['labels'], df['expected_labels'], check_names=False)
def test_apply(self):
assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts))
@@ -6361,6 +6372,8 @@ def test_unstack(self):
left = ts.unstack()
right = DataFrame([[nan, 1], [2, nan]], index=[101, 102],
columns=[nan, 3.5])
+ print(left)
+ print(right)
assert_frame_equal(left, right)
idx = pd.MultiIndex.from_arrays([['cat', 'cat', 'cat', 'dog', 'dog'],
diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py
index cf5cc4661ec52..7b322b0d311de 100644
--- a/pandas/tools/tests/test_merge.py
+++ b/pandas/tools/tests/test_merge.py
@@ -922,8 +922,10 @@ def run_asserts(left, right):
self.assertFalse(res['4th'].isnull().any())
self.assertFalse(res['5th'].isnull().any())
- tm.assert_series_equal(res['4th'], - res['5th'])
- tm.assert_series_equal(res['4th'], bind_cols(res.iloc[:, :-2]))
+ tm.assert_series_equal(res['4th'], - res['5th'], check_names=False)
+ result = bind_cols(res.iloc[:, :-2])
+ tm.assert_series_equal(res['4th'], result, check_names=False)
+ self.assertTrue(result.name is None)
if sort:
tm.assert_frame_equal(res,
@@ -1250,8 +1252,10 @@ def test_int64_overflow_issues(self):
out = merge(left, right, how='outer')
self.assertEqual(len(out), len(left))
- assert_series_equal(out['left'], - out['right'])
- assert_series_equal(out['left'], out.iloc[:, :-2].sum(axis=1))
+ assert_series_equal(out['left'], - out['right'], check_names=False)
+ result = out.iloc[:, :-2].sum(axis=1)
+ assert_series_equal(out['left'], result, check_names=False)
+ self.assertTrue(result.name is None)
out.sort(out.columns.tolist(), inplace=True)
out.index = np.arange(len(out))
diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py
index 4618501bed841..bb95234657ec2 100644
--- a/pandas/tools/tests/test_pivot.py
+++ b/pandas/tools/tests/test_pivot.py
@@ -166,8 +166,8 @@ def test_pivot_index_with_nan(self):
result = df.pivot('a','b','c')
expected = DataFrame([[nan,nan,17,nan],[10,nan,nan,nan],
[nan,15,nan,nan],[nan,nan,nan,20]],
- index = Index([nan,'R1','R2','R4'],name='a'),
- columns = Index(['C1','C2','C3','C4'],name='b'))
+ index = Index([nan,'R1','R2','R4'], name='a'),
+ columns = Index(['C1','C2','C3','C4'], name='b'))
tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(df.pivot('b', 'a', 'c'), expected.T)
@@ -227,12 +227,14 @@ def test_margins(self):
def _check_output(res, col, index=['A', 'B'], columns=['C']):
cmarg = res['All'][:-1]
exp = self.data.groupby(index)[col].mean()
- tm.assert_series_equal(cmarg, exp)
+ tm.assert_series_equal(cmarg, exp, check_names=False)
+ self.assertEqual(cmarg.name, 'All')
res = res.sortlevel()
rmarg = res.xs(('All', ''))[:-1]
exp = self.data.groupby(columns)[col].mean()
- tm.assert_series_equal(rmarg, exp)
+ tm.assert_series_equal(rmarg, exp, check_names=False)
+ self.assertEqual(rmarg.name, ('All', ''))
gmarg = res['All']['All', '']
exp = self.data[col].mean()
@@ -679,12 +681,14 @@ def test_crosstab_margins(self):
all_cols = result['All', '']
exp_cols = df.groupby(['a']).size().astype('i8')
exp_cols = exp_cols.append(Series([len(df)], index=['All']))
+ exp_cols.name = ('All', '')
tm.assert_series_equal(all_cols, exp_cols)
all_rows = result.ix['All']
exp_rows = df.groupby(['b', 'c']).size().astype('i8')
exp_rows = exp_rows.append(Series([len(df)], index=[('All', '')]))
+ exp_rows.name = 'All'
exp_rows = exp_rows.reindex(all_rows.index)
exp_rows = exp_rows.fillna(0).astype(np.int64)
diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py
index d7b1256329cc3..202ccb9438db5 100644
--- a/pandas/tseries/tests/test_resample.py
+++ b/pandas/tseries/tests/test_resample.py
@@ -815,7 +815,9 @@ def test_how_lambda_functions(self):
result = ts.resample('M', how={'foo': lambda x: x.mean(),
'bar': lambda x: x.std(ddof=1)})
foo_exp = ts.resample('M', how='mean')
+ foo_exp.name = 'foo'
bar_exp = ts.resample('M', how='std')
+ bar_exp.name = 'bar'
tm.assert_series_equal(result['foo'], foo_exp)
tm.assert_series_equal(result['bar'], bar_exp)
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index 8412ba8d4aad1..09b2bb24714af 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -248,25 +248,27 @@ def test_indexing(self):
# GH 3070, make sure semantics work on Series/Frame
expected = ts['2001']
+ expected.name = 'A'
df = DataFrame(dict(A = ts))
result = df['2001']['A']
- assert_series_equal(expected,result)
+ assert_series_equal(expected, result)
# setting
ts['2001'] = 1
expected = ts['2001']
+ expected.name = 'A'
df.loc['2001','A'] = 1
result = df['2001']['A']
- assert_series_equal(expected,result)
+ assert_series_equal(expected, result)
# GH3546 (not including times on the last day)
idx = date_range(start='2013-05-31 00:00', end='2013-05-31 23:00', freq='H')
ts = Series(lrange(len(idx)), index=idx)
expected = ts['2013-05']
- assert_series_equal(expected,ts)
+ assert_series_equal(expected, ts)
idx = date_range(start='2013-05-31 00:00', end='2013-05-31 23:59', freq='S')
ts = Series(lrange(len(idx)), index=idx)
@@ -882,10 +884,10 @@ def test_string_na_nat_conversion(self):
else:
expected[i] = to_datetime(x)
- assert_series_equal(result, expected)
+ assert_series_equal(result, expected, check_names=False)
self.assertEqual(result.name, 'foo')
- assert_series_equal(dresult, expected)
+ assert_series_equal(dresult, expected, check_names=False)
self.assertEqual(dresult.name, 'foo')
def test_to_datetime_iso8601(self):
diff --git a/pandas/tseries/tests/test_util.py b/pandas/tseries/tests/test_util.py
index df556cdc77d08..c75fcbdac07c0 100644
--- a/pandas/tseries/tests/test_util.py
+++ b/pandas/tseries/tests/test_util.py
@@ -30,12 +30,15 @@ def test_daily(self):
subset = ts[doy == i]
subset.index = [x.year for x in subset.index]
- tm.assert_series_equal(annual[i].dropna(), subset)
+ result = annual[i].dropna()
+ tm.assert_series_equal(result, subset, check_names=False)
+ self.assertEqual(result.name, i)
# check leap days
leaps = ts[(ts.index.month == 2) & (ts.index.day == 29)]
day = leaps.index.dayofyear[0]
leaps.index = leaps.index.year
+ leaps.name = 60
tm.assert_series_equal(annual[day].dropna(), leaps)
def test_hourly(self):
@@ -57,15 +60,19 @@ def test_hourly(self):
subset = ts_hourly[hoy == i]
subset.index = [x.year for x in subset.index]
- tm.assert_series_equal(annual[i].dropna(), subset)
+ result = annual[i].dropna()
+ tm.assert_series_equal(result, subset, check_names=False)
+ self.assertEqual(result.name, i)
leaps = ts_hourly[(ts_hourly.index.month == 2) &
(ts_hourly.index.day == 29) &
(ts_hourly.index.hour == 0)]
hour = leaps.index.dayofyear[0] * 24 - 23
leaps.index = leaps.index.year
+ leaps.name = 1417
tm.assert_series_equal(annual[hour].dropna(), leaps)
+
def test_weekly(self):
pass
@@ -79,7 +86,9 @@ def test_monthly(self):
for i in range(1, 13):
subset = ts[month == i]
subset.index = [x.year for x in subset.index]
- tm.assert_series_equal(annual[i].dropna(), subset)
+ result = annual[i].dropna()
+ tm.assert_series_equal(result, subset, check_names=False)
+ self.assertEqual(result.name, i)
def test_period_monthly(self):
pass
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 55f95b602779f..25f5f84b0b1d9 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -24,7 +24,7 @@
from numpy.testing import assert_array_equal
import pandas as pd
-from pandas.core.common import is_sequence, array_equivalent, is_list_like
+from pandas.core.common import is_sequence, array_equivalent, is_list_like, is_number
import pandas.compat as compat
from pandas.compat import(
filter, map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter,
@@ -691,6 +691,14 @@ def assert_series_equal(left, right, check_dtype=True,
assert_isinstance(lindex, type(rindex))
assert_attr_equal('dtype', lindex, rindex)
assert_attr_equal('inferred_type', lindex, rindex)
+ if check_names:
+ if is_number(left.name) and np.isnan(left.name):
+ # Series.name can be np.nan in some test cases
+ assert is_number(right.name) and np.isnan(right.name)
+ elif left.name is pd.NaT:
+ assert right.name is pd.NaT
+ else:
+ assert_attr_equal('name', left, right)
# This could be refactored to use the NDFrame.equals method
| Related to #9972. Made `assert_series_equal` to check name attribute.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10276 | 2015-06-04T15:16:12Z | 2015-06-05T22:16:57Z | 2015-06-05T22:16:57Z | 2015-06-05T22:48:55Z |
Fix meantim typo | diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index ad41373f6dd3f..8670ea61dbec2 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -14,7 +14,7 @@ fi
if [ x"$DOC_BUILD" != x"" ]; then
- # we're running network tests, let's build the docs in the meantim
+ # we're running network tests, let's build the docs in the meantime
echo "Will build docs"
conda install sphinx==1.1.3 ipython
| https://api.github.com/repos/pandas-dev/pandas/pulls/10275 | 2015-06-04T14:43:50Z | 2015-06-04T14:49:25Z | 2015-06-04T14:49:25Z | 2015-06-04T14:50:01Z | |
BUG: bug in cache updating when consolidating #10264 | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index ddfe6fa0b2f74..72fba51797783 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -59,6 +59,9 @@ Bug Fixes
- Bug in ``Categorical`` repr with ``display.width`` of ``None`` in Python 3 (:issue:`10087`)
- Bug in groupby.apply aggregation for Categorical not preserving categories (:issue:`10138`)
+
+- Bug in cache updating when consolidating (:issue:`10264`)
+
- Bug in ``mean()`` where integer dtypes can overflow (:issue:`10172`)
- Bug where Panel.from_dict does not set dtype when specified (:issue:`10058`)
- Bug in ``Index.union`` raises ``AttributeError`` when passing array-likes. (:issue:`10149`)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index d6c7d87bb25b1..3bf90aaf71849 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1352,6 +1352,7 @@ def take(self, indices, axis=0, convert=True, is_copy=True):
taken : type of caller
"""
+ self._consolidate_inplace()
new_data = self._data.take(indices,
axis=self._get_block_manager_axis(axis),
convert=True, verify=True)
@@ -2128,8 +2129,10 @@ def _protect_consolidate(self, f):
return result
def _consolidate_inplace(self):
- f = lambda: self._data.consolidate()
- self._data = self._protect_consolidate(f)
+ """ we are inplace consolidating; return None """
+ def f():
+ self._data = self._data.consolidate()
+ self._protect_consolidate(f)
def consolidate(self, inplace=False):
"""
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index e0f06e22c431b..c23b691e0fe3a 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -369,6 +369,7 @@ def _setitem_with_indexer(self, indexer, value):
# we can directly set the series here
# as we select a slice indexer on the mi
idx = index._convert_slice_indexer(idx)
+ obj._consolidate_inplace()
obj = obj.copy()
obj._data = obj._data.setitem(indexer=tuple([idx]), value=value)
self.obj[item] = obj
@@ -396,6 +397,7 @@ def setter(item, v):
s = v
else:
# set the item, possibly having a dtype change
+ s._consolidate_inplace()
s = s.copy()
s._data = s._data.setitem(indexer=pi, value=v)
s._maybe_update_cacher(clear=True)
@@ -492,6 +494,7 @@ def can_do_equal_len():
self.obj._check_is_chained_assignment_possible()
# actually do the set
+ self.obj._consolidate_inplace()
self.obj._data = self.obj._data.setitem(indexer=indexer, value=value)
self.obj._maybe_update_cacher(clear=True)
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 3395ea360165e..8b37033749c9c 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -2830,13 +2830,13 @@ def consolidate(self):
return self
bm = self.__class__(self.blocks, self.axes)
+ bm._is_consolidated = False
bm._consolidate_inplace()
return bm
def _consolidate_inplace(self):
if not self.is_consolidated():
self.blocks = tuple(_consolidate(self.blocks))
-
self._is_consolidated = True
self._known_consolidated = True
self._rebuild_blknos_and_blklocs()
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index c998ce65791a3..5b60475ffcf33 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -3527,6 +3527,18 @@ def test_cache_updating(self):
result = df.loc[(0,0),'z']
self.assertEqual(result, 2)
+ # 10264
+ df = DataFrame(np.zeros((5,5),dtype='int64'),columns=['a','b','c','d','e'],index=range(5))
+ df['f'] = 0
+ df.f.values[3] = 1
+ y = df.iloc[np.arange(2,len(df))]
+ df.f.values[3] = 2
+ expected = DataFrame(np.zeros((5,6),dtype='int64'),columns=['a','b','c','d','e','f'],index=range(5))
+ expected.at[3,'f'] = 2
+ assert_frame_equal(df, expected)
+ expected = Series([0,0,0,2,0],name='f')
+ assert_series_equal(df.f, expected)
+
def test_slice_consolidate_invalidate_item_cache(self):
# this is chained assignment, but will 'work'
| closes #10264
| https://api.github.com/repos/pandas-dev/pandas/pulls/10272 | 2015-06-04T12:10:26Z | 2015-06-05T21:41:05Z | 2015-06-05T21:41:05Z | 2015-06-05T21:41:05Z |
PERF: write basic datetimes faster | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index f1c5b0c854055..1430fa1a309be 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -47,6 +47,7 @@ Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Improved ``Series.resample`` performance with dtype=datetime64[ns] (:issue:`7754`)
+- Modest improvement in datetime writing speed in to_csv (:issue:`10271`)
.. _whatsnew_0162.bug_fixes:
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 59eb432844ee3..8fda9bb31061e 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -5,6 +5,7 @@ from numpy cimport (int8_t, int32_t, int64_t, import_array, ndarray,
NPY_INT64, NPY_DATETIME, NPY_TIMEDELTA)
import numpy as np
+from cpython.ref cimport PyObject
from cpython cimport (
PyTypeObject,
PyFloat_Check,
@@ -12,13 +13,14 @@ from cpython cimport (
PyObject_RichCompareBool,
PyObject_RichCompare,
PyString_Check,
- Py_GT, Py_GE, Py_EQ, Py_NE, Py_LT, Py_LE
+ Py_GT, Py_GE, Py_EQ, Py_NE, Py_LT, Py_LE,
)
# Cython < 0.17 doesn't have this in cpython
cdef extern from "Python.h":
cdef PyTypeObject *Py_TYPE(object)
int PySlice_Check(object)
+ object PyUnicode_FromFormat(const char*, ...)
cdef extern from "datetime_helper.h":
double total_seconds(object)
@@ -1450,20 +1452,43 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None, object f
elif basic_format:
pandas_datetime_to_datetimestruct(val, PANDAS_FR_ns, &dts)
- res = '%d-%.2d-%.2d %.2d:%.2d:%.2d' % (dts.year,
- dts.month,
- dts.day,
- dts.hour,
- dts.min,
- dts.sec)
-
if show_ns:
ns = dts.ps / 1000
- res += '.%.9d' % (ns + 1000 * dts.us)
+ res = PyUnicode_FromFormat('%d-%02d-%02d %02d:%02d:%02d.%09d',
+ dts.year,
+ dts.month,
+ dts.day,
+ dts.hour,
+ dts.min,
+ dts.sec,
+ ns + 1000 * dts.us)
elif show_us:
- res += '.%.6d' % dts.us
+ res = PyUnicode_FromFormat('%d-%02d-%02d %02d:%02d:%02d.%06d',
+ dts.year,
+ dts.month,
+ dts.day,
+ dts.hour,
+ dts.min,
+ dts.sec,
+ dts.us)
+
elif show_ms:
- res += '.%.3d' % (dts.us/1000)
+ res = PyUnicode_FromFormat('%d-%02d-%02d %02d:%02d:%02d.%03d',
+ dts.year,
+ dts.month,
+ dts.day,
+ dts.hour,
+ dts.min,
+ dts.sec,
+ dts.us/1000)
+ else:
+ res = PyUnicode_FromFormat('%d-%02d-%02d %02d:%02d:%02d',
+ dts.year,
+ dts.month,
+ dts.day,
+ dts.hour,
+ dts.min,
+ dts.sec)
result[i] = res
| with PR
```
In [2]: df = DataFrame({'A' : pd.date_range('20130101',periods=100000,freq='s')})
In [3]: %timeit df.to_csv('test.csv',mode='w')
1 loops, best of 3: 282 ms per loop
```
0.16.1
```
In [2]: %timeit df.to_csv('test.csv',mode='w')
1 loops, best of 3: 352 ms per loop
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10271 | 2015-06-04T12:08:35Z | 2015-06-04T13:27:51Z | 2015-06-04T13:27:51Z | 2015-06-04T13:27:51Z |
TST: fix for bottleneck >= 1.0 nansum behavior, xref #9422 | diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 0df160618b7c3..c64c50f791edf 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -16,7 +16,7 @@
ensure_float, _ensure_float64,
_ensure_int64, _ensure_object,
is_float, is_integer, is_complex,
- is_float_dtype, is_floating_dtype,
+ is_float_dtype,
is_complex_dtype, is_integer_dtype,
is_bool_dtype, is_object_dtype,
is_datetime64_dtype, is_timedelta64_dtype,
@@ -373,7 +373,7 @@ def nansem(values, axis=None, skipna=True, ddof=1):
var = nanvar(values, axis, skipna, ddof=ddof)
mask = isnull(values)
- if not is_floating_dtype(values):
+ if not is_float_dtype(values.dtype):
values = values.astype('f8')
count, _ = _get_counts_nanvar(mask, axis, ddof)
@@ -467,7 +467,7 @@ def nanargmin(values, axis=None, skipna=True):
def nanskew(values, axis=None, skipna=True):
mask = isnull(values)
- if not is_floating_dtype(values):
+ if not is_float_dtype(values.dtype):
values = values.astype('f8')
count = _get_counts(mask, axis)
@@ -502,7 +502,7 @@ def nanskew(values, axis=None, skipna=True):
def nankurt(values, axis=None, skipna=True):
mask = isnull(values)
- if not is_floating_dtype(values):
+ if not is_float_dtype(values.dtype):
values = values.astype('f8')
count = _get_counts(mask, axis)
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index bbe942e607faf..eb583f17f3ace 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -2316,7 +2316,7 @@ def test_iteritems(self):
self.assertFalse(hasattr(self.series.iteritems(), 'reverse'))
def test_sum(self):
- self._check_stat_op('sum', np.sum)
+ self._check_stat_op('sum', np.sum, check_allna=True)
def test_sum_inf(self):
import pandas.core.nanops as nanops
@@ -2629,7 +2629,7 @@ def test_npdiff(self):
r = np.diff(s)
assert_series_equal(Series([nan, 0, 0, 0, nan]), r)
- def _check_stat_op(self, name, alternate, check_objects=False):
+ def _check_stat_op(self, name, alternate, check_objects=False, check_allna=False):
import pandas.core.nanops as nanops
def testit():
@@ -2653,7 +2653,17 @@ def testit():
assert_almost_equal(f(self.series), alternate(nona.values))
allna = self.series * nan
- self.assertTrue(np.isnan(f(allna)))
+
+ if check_allna:
+ # xref 9422
+ # bottleneck >= 1.0 give 0.0 for an allna Series sum
+ try:
+ self.assertTrue(nanops._USE_BOTTLENECK)
+ import bottleneck as bn
+ self.assertTrue(bn.__version__ >= LooseVersion('1.0'))
+ self.assertEqual(f(allna),0.0)
+ except:
+ self.assertTrue(np.isnan(f(allna)))
# dtype=object with None, it works!
s = Series([1, 2, 3, None, 5])
| https://api.github.com/repos/pandas-dev/pandas/pulls/10270 | 2015-06-04T10:57:29Z | 2015-06-04T12:06:05Z | 2015-06-04T12:06:05Z | 2015-06-04T12:06:05Z | |
BUG: Ensure 'coerce' actually coerces datatypes | diff --git a/doc/source/basics.rst b/doc/source/basics.rst
index 349e7e25fdafb..f739d89295ac1 100644
--- a/doc/source/basics.rst
+++ b/doc/source/basics.rst
@@ -1522,23 +1522,31 @@ then the more *general* one will be used as the result of the operation.
object conversion
~~~~~~~~~~~~~~~~~
-:meth:`~DataFrame.convert_objects` is a method to try to force conversion of types from the ``object`` dtype to other types.
-To force conversion of specific types that are *number like*, e.g. could be a string that represents a number,
-pass ``convert_numeric=True``. This will force strings and numbers alike to be numbers if possible, otherwise
-they will be set to ``np.nan``.
+.. note::
+
+ The syntax of :meth:`~DataFrame.convert_objects` changed in 0.17.0. See
+ :ref:`API changes <whatsnew_0170.api_breaking.convert_objects>`
+ for more details.
+
+:meth:`~DataFrame.convert_objects` is a method to try to force conversion of
+types from the ``object`` dtype to other types. To try converting specific
+types that are *number like*, e.g. could be a string that represents a number,
+pass ``numeric=True``. To force the conversion, add the keyword argument
+``coerce=True``. This will force strings and number-like objects to be numbers if
+possible, otherwise they will be set to ``np.nan``.
.. ipython:: python
df3['D'] = '1.'
df3['E'] = '1'
- df3.convert_objects(convert_numeric=True).dtypes
+ df3.convert_objects(numeric=True).dtypes
# same, but specific dtype conversion
df3['D'] = df3['D'].astype('float16')
df3['E'] = df3['E'].astype('int32')
df3.dtypes
-To force conversion to ``datetime64[ns]``, pass ``convert_dates='coerce'``.
+To force conversion to ``datetime64[ns]``, pass ``datetime=True`` and ``coerce=True``.
This will convert any datetime-like object to dates, forcing other values to ``NaT``.
This might be useful if you are reading in data which is mostly dates,
but occasionally has non-dates intermixed and you want to represent as missing.
@@ -1550,10 +1558,15 @@ but occasionally has non-dates intermixed and you want to represent as missing.
'foo', 1.0, 1, pd.Timestamp('20010104'),
'20010105'], dtype='O')
s
- s.convert_objects(convert_dates='coerce')
+ s.convert_objects(datetime=True, coerce=True)
-In addition, :meth:`~DataFrame.convert_objects` will attempt the *soft* conversion of any *object* dtypes, meaning that if all
+Without passing ``coerce=True``, :meth:`~DataFrame.convert_objects` will attempt
+*soft* conversion of any *object* dtypes, meaning that if all
the objects in a Series are of the same type, the Series will have that dtype.
+Note that setting ``coerce=True`` does not *convert* arbitrary types to either
+``datetime64[ns]`` or ``timedelta64[ns]``. For example, a series containing string
+dates will not be converted to a series of datetimes. To convert between types,
+see :ref:`converting to timestamps <timeseries.converting>`.
gotchas
~~~~~~~
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index a3ec13439fe76..5abdd272b442b 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -48,6 +48,71 @@ Backwards incompatible API changes
.. _whatsnew_0170.api_breaking.other:
+.. _whatsnew_0170.api_breaking.convert_objects:
+Changes to convert_objects
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+- ``DataFrame.convert_objects`` keyword arguments have been shortened. (:issue:`10265`)
+
+===================== =============
+Old New
+===================== =============
+``convert_dates`` ``datetime``
+``convert_numeric`` ``numeric``
+``convert_timedelta`` ``timedelta``
+===================== =============
+
+- Coercing types with ``DataFrame.convert_objects`` is now implemented using the
+keyword argument ``coerce=True``. Previously types were coerced by setting a
+keyword argument to ``'coerce'`` instead of ``True``, as in ``convert_dates='coerce'``.
+
+ .. ipython:: python
+
+ df = pd.DataFrame({'i': ['1','2'],
+ 'f': ['apple', '4.2'],
+ 's': ['apple','banana']})
+ df
+
+ The old usage of ``DataFrame.convert_objects`` used `'coerce'` along with the
+ type.
+
+ .. code-block:: python
+
+ In [2]: df.convert_objects(convert_numeric='coerce')
+
+ Now the ``coerce`` keyword must be explicitly used.
+
+ .. ipython:: python
+
+ df.convert_objects(numeric=True, coerce=True)
+
+- In earlier versions of pandas, ``DataFrame.convert_objects`` would not coerce
+numeric types when there were no values convertible to a numeric type. For example,
+
+ .. code-block:: python
+
+ In [1]: df = pd.DataFrame({'s': ['a','b']})
+ In [2]: df.convert_objects(convert_numeric='coerce')
+ Out[2]:
+ s
+ 0 a
+ 1 b
+
+returns the original DataFrame with no conversion. This change alters
+this behavior so that
+
+ .. ipython:: python
+
+ pd.DataFrame({'s': ['a','b']})
+ df.convert_objects(numeric=True, coerce=True)
+
+converts all non-number-like strings to ``NaN``.
+
+- In earlier versions of pandas, the default behavior was to try and convert
+datetimes and timestamps. The new default is for ``DataFrame.convert_objects``
+to do nothing, and so it is necessary to pass at least one conversion target
+in the method call.
+
+
Other API Changes
^^^^^^^^^^^^^^^^^
- Enable writing Excel files in :ref:`memory <_io.excel_writing_buffer>` using StringIO/BytesIO (:issue:`7074`)
@@ -55,6 +120,7 @@ Other API Changes
- Allow passing `kwargs` to the interpolation methods (:issue:`10378`).
- Serialize metadata properties of subclasses of pandas objects (:issue:`10553`).
+
.. _whatsnew_0170.deprecations:
Deprecations
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 773ecea8f2712..33a2fc0aea732 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -1887,65 +1887,68 @@ def _maybe_box_datetimelike(value):
_values_from_object = lib.values_from_object
-def _possibly_convert_objects(values, convert_dates=True,
- convert_numeric=True,
- convert_timedeltas=True):
+
+def _possibly_convert_objects(values,
+ datetime=True,
+ numeric=True,
+ timedelta=True,
+ coerce=False,
+ copy=True):
""" if we have an object dtype, try to coerce dates and/or numbers """
- # if we have passed in a list or scalar
+ conversion_count = sum((datetime, numeric, timedelta))
+ if conversion_count == 0:
+ import warnings
+ warnings.warn('Must explicitly pass type for conversion. Defaulting to '
+ 'pre-0.17 behavior where datetime=True, numeric=True, '
+ 'timedelta=True and coerce=False', DeprecationWarning)
+ datetime = numeric = timedelta = True
+ coerce = False
+
if isinstance(values, (list, tuple)):
+ # List or scalar
values = np.array(values, dtype=np.object_)
- if not hasattr(values, 'dtype'):
+ elif not hasattr(values, 'dtype'):
values = np.array([values], dtype=np.object_)
-
- # convert dates
- if convert_dates and values.dtype == np.object_:
-
- # we take an aggressive stance and convert to datetime64[ns]
- if convert_dates == 'coerce':
- new_values = _possibly_cast_to_datetime(
- values, 'M8[ns]', coerce=True)
-
- # if we are all nans then leave me alone
- if not isnull(new_values).all():
- values = new_values
-
- else:
- values = lib.maybe_convert_objects(
- values, convert_datetime=convert_dates)
-
- # convert timedeltas
- if convert_timedeltas and values.dtype == np.object_:
-
- if convert_timedeltas == 'coerce':
- from pandas.tseries.timedeltas import to_timedelta
- values = to_timedelta(values, coerce=True)
-
- # if we are all nans then leave me alone
- if not isnull(new_values).all():
- values = new_values
-
- else:
- values = lib.maybe_convert_objects(
- values, convert_timedelta=convert_timedeltas)
-
- # convert to numeric
- if values.dtype == np.object_:
- if convert_numeric:
- try:
- new_values = lib.maybe_convert_numeric(
- values, set(), coerce_numeric=True)
-
- # if we are all nans then leave me alone
- if not isnull(new_values).all():
- values = new_values
-
- except:
- pass
- else:
-
- # soft-conversion
- values = lib.maybe_convert_objects(values)
+ elif not is_object_dtype(values.dtype):
+ # If not object, do not attempt conversion
+ values = values.copy() if copy else values
+ return values
+
+ # If 1 flag is coerce, ensure 2 others are False
+ if coerce:
+ if conversion_count > 1:
+ raise ValueError("Only one of 'datetime', 'numeric' or "
+ "'timedelta' can be True when when coerce=True.")
+
+ # Immediate return if coerce
+ if datetime:
+ return pd.to_datetime(values, coerce=True, box=False)
+ elif timedelta:
+ return pd.to_timedelta(values, coerce=True, box=False)
+ elif numeric:
+ return lib.maybe_convert_numeric(values, set(), coerce_numeric=True)
+
+ # Soft conversions
+ if datetime:
+ values = lib.maybe_convert_objects(values,
+ convert_datetime=datetime)
+
+ if timedelta and is_object_dtype(values.dtype):
+ # Object check to ensure only run if previous did not convert
+ values = lib.maybe_convert_objects(values,
+ convert_timedelta=timedelta)
+
+ if numeric and is_object_dtype(values.dtype):
+ try:
+ converted = lib.maybe_convert_numeric(values,
+ set(),
+ coerce_numeric=True)
+ # If all NaNs, then do not-alter
+ values = converted if not isnull(converted).all() else values
+ values = values.copy() if copy else values
+ except:
+ pass
return values
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index bb192aeca5b6d..a7ecb74a67485 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3351,7 +3351,7 @@ def combine(self, other, func, fill_value=None, overwrite=True):
return self._constructor(result,
index=new_index,
columns=new_columns).convert_objects(
- convert_dates=True,
+ datetime=True,
copy=False)
def combine_first(self, other):
@@ -3830,7 +3830,9 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True):
if axis == 1:
result = result.T
- result = result.convert_objects(copy=False)
+ result = result.convert_objects(datetime=True,
+ timedelta=True,
+ copy=False)
else:
@@ -3958,7 +3960,10 @@ def append(self, other, ignore_index=False, verify_integrity=False):
combined_columns = self.columns.tolist() + self.columns.union(other.index).difference(self.columns).tolist()
other = other.reindex(combined_columns, copy=False)
other = DataFrame(other.values.reshape((1, len(other))),
- index=index, columns=combined_columns).convert_objects()
+ index=index,
+ columns=combined_columns)
+ other = other.convert_objects(datetime=True, timedelta=True)
+
if not self.columns.equals(combined_columns):
self = self.reindex(columns=combined_columns)
elif isinstance(other, list) and not isinstance(other[0], DataFrame):
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f39e953284f26..1656b306a0ddb 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -2433,22 +2433,26 @@ def copy(self, deep=True):
data = self._data.copy(deep=deep)
return self._constructor(data).__finalize__(self)
- def convert_objects(self, convert_dates=True, convert_numeric=False,
- convert_timedeltas=True, copy=True):
+ @deprecate_kwarg(old_arg_name='convert_dates', new_arg_name='datetime')
+ @deprecate_kwarg(old_arg_name='convert_numeric', new_arg_name='numeric')
+ @deprecate_kwarg(old_arg_name='convert_timedeltas', new_arg_name='timedelta')
+ def convert_objects(self, datetime=False, numeric=False,
+ timedelta=False, coerce=False, copy=True):
"""
Attempt to infer better dtype for object columns
Parameters
----------
- convert_dates : boolean, default True
- If True, convert to date where possible. If 'coerce', force
- conversion, with unconvertible values becoming NaT.
- convert_numeric : boolean, default False
- If True, attempt to coerce to numbers (including strings), with
+ datetime : boolean, default False
+ If True, convert to date where possible.
+ numeric : boolean, default False
+ If True, attempt to convert to numbers (including strings), with
unconvertible values becoming NaN.
- convert_timedeltas : boolean, default True
- If True, convert to timedelta where possible. If 'coerce', force
- conversion, with unconvertible values becoming NaT.
+ timedelta : boolean, default False
+ If True, convert to timedelta where possible.
+ coerce : boolean, default False
+ If True, force conversion with unconvertible values converted to
+ nulls (NaN or NaT)
copy : boolean, default True
If True, return a copy even if no copy is necessary (e.g. no
conversion was done). Note: This is meant for internal use, and
@@ -2459,9 +2463,10 @@ def convert_objects(self, convert_dates=True, convert_numeric=False,
converted : same as input object
"""
return self._constructor(
- self._data.convert(convert_dates=convert_dates,
- convert_numeric=convert_numeric,
- convert_timedeltas=convert_timedeltas,
+ self._data.convert(datetime=datetime,
+ numeric=numeric,
+ timedelta=timedelta,
+ coerce=coerce,
copy=copy)).__finalize__(self)
#----------------------------------------------------------------------
@@ -2859,7 +2864,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
'{0!r}').format(type(to_replace).__name__)
raise TypeError(msg) # pragma: no cover
- new_data = new_data.convert(copy=not inplace, convert_numeric=False)
+ new_data = new_data.convert(copy=not inplace, numeric=False)
if inplace:
self._update_inplace(new_data)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 4abdd1112c721..df788f806eda6 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -111,7 +111,7 @@ def f(self):
except Exception:
result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
if _convert:
- result = result.convert_objects()
+ result = result.convert_objects(datetime=True)
return result
f.__doc__ = "Compute %s of group values" % name
@@ -2700,7 +2700,7 @@ def aggregate(self, arg, *args, **kwargs):
self._insert_inaxis_grouper_inplace(result)
result.index = np.arange(len(result))
- return result.convert_objects()
+ return result.convert_objects(datetime=True)
def _aggregate_multiple_funcs(self, arg):
from pandas.tools.merge import concat
@@ -2939,18 +2939,25 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
# if we have date/time like in the original, then coerce dates
# as we are stacking can easily have object dtypes here
- if (self._selected_obj.ndim == 2
- and self._selected_obj.dtypes.isin(_DATELIKE_DTYPES).any()):
- cd = 'coerce'
+ if (self._selected_obj.ndim == 2 and
+ self._selected_obj.dtypes.isin(_DATELIKE_DTYPES).any()):
+ result = result.convert_objects(numeric=True)
+ date_cols = self._selected_obj.select_dtypes(
+ include=list(_DATELIKE_DTYPES)).columns
+ result[date_cols] = (result[date_cols]
+ .convert_objects(datetime=True,
+ coerce=True))
else:
- cd = True
- result = result.convert_objects(convert_dates=cd)
+ result = result.convert_objects(datetime=True)
+
return self._reindex_output(result)
else:
# only coerce dates if we find at least 1 datetime
- cd = 'coerce' if any([ isinstance(v,Timestamp) for v in values ]) else False
- return Series(values, index=key_index).convert_objects(convert_dates=cd)
+ coerce = True if any([ isinstance(v,Timestamp) for v in values ]) else False
+ return (Series(values, index=key_index)
+ .convert_objects(datetime=True,
+ coerce=coerce))
else:
# Handle cases like BinGrouper
@@ -3053,7 +3060,8 @@ def transform(self, func, *args, **kwargs):
if any(counts == 0):
results = self._try_cast(results, obj[result.columns])
- return DataFrame(results,columns=result.columns,index=obj.index).convert_objects()
+ return (DataFrame(results,columns=result.columns,index=obj.index)
+ .convert_objects(datetime=True))
def _define_paths(self, func, *args, **kwargs):
if isinstance(func, compat.string_types):
@@ -3246,7 +3254,7 @@ def _wrap_aggregated_output(self, output, names=None):
if self.axis == 1:
result = result.T
- return self._reindex_output(result).convert_objects()
+ return self._reindex_output(result).convert_objects(datetime=True)
def _wrap_agged_blocks(self, items, blocks):
if not self.as_index:
@@ -3264,7 +3272,7 @@ def _wrap_agged_blocks(self, items, blocks):
if self.axis == 1:
result = result.T
- return self._reindex_output(result).convert_objects()
+ return self._reindex_output(result).convert_objects(datetime=True)
def _reindex_output(self, result):
"""
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 42d7163e7f741..6b7909086403e 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -622,7 +622,7 @@ def _is_empty_indexer(indexer):
# may have to soft convert_objects here
if block.is_object and not self.is_object:
- block = block.convert(convert_numeric=False)
+ block = block.convert(numeric=False)
return block
except (ValueError, TypeError) as detail:
@@ -1455,7 +1455,7 @@ def is_bool(self):
"""
return lib.is_bool_array(self.values.ravel())
- def convert(self, convert_dates=True, convert_numeric=True, convert_timedeltas=True,
+ def convert(self, datetime=True, numeric=True, timedelta=True, coerce=False,
copy=True, by_item=True):
""" attempt to coerce any object types to better types
return a copy of the block (if copy = True)
@@ -1472,9 +1472,12 @@ def convert(self, convert_dates=True, convert_numeric=True, convert_timedeltas=T
values = self.iget(i)
values = com._possibly_convert_objects(
- values.ravel(), convert_dates=convert_dates,
- convert_numeric=convert_numeric,
- convert_timedeltas=convert_timedeltas,
+ values.ravel(),
+ datetime=datetime,
+ numeric=numeric,
+ timedelta=timedelta,
+ coerce=coerce,
+ copy=copy
).reshape(values.shape)
values = _block_shape(values, ndim=self.ndim)
newb = make_block(values,
@@ -1484,8 +1487,12 @@ def convert(self, convert_dates=True, convert_numeric=True, convert_timedeltas=T
else:
values = com._possibly_convert_objects(
- self.values.ravel(), convert_dates=convert_dates,
- convert_numeric=convert_numeric
+ self.values.ravel(),
+ datetime=datetime,
+ numeric=numeric,
+ timedelta=timedelta,
+ coerce=coerce,
+ copy=copy
).reshape(self.values.shape)
blocks.append(make_block(values,
ndim=self.ndim, placement=self.mgr_locs))
@@ -1529,8 +1536,8 @@ def _maybe_downcast(self, blocks, downcast=None):
# split and convert the blocks
result_blocks = []
for blk in blocks:
- result_blocks.extend(blk.convert(convert_dates=True,
- convert_numeric=False))
+ result_blocks.extend(blk.convert(datetime=True,
+ numeric=False))
return result_blocks
def _can_hold_element(self, element):
diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py
index fca9e1c4e47ca..9093df9f0bf62 100644
--- a/pandas/io/tests/test_html.py
+++ b/pandas/io/tests/test_html.py
@@ -540,9 +540,11 @@ def try_remove_ws(x):
'Hamilton Bank, NA', 'The Citizens Savings Bank']
dfnew = df.applymap(try_remove_ws).replace(old, new)
gtnew = ground_truth.applymap(try_remove_ws)
- converted = dfnew.convert_objects(convert_numeric=True)
- tm.assert_frame_equal(converted.convert_objects(convert_dates='coerce'),
- gtnew)
+ converted = dfnew.convert_objects(datetime=True, numeric=True)
+ date_cols = ['Closing Date','Updated Date']
+ converted[date_cols] = converted[date_cols].convert_objects(datetime=True,
+ coerce=True)
+ tm.assert_frame_equal(converted,gtnew)
@slow
def test_gold_canyon(self):
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index 1b932fb3759e5..ea30fb14251f4 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -403,7 +403,7 @@ def test_repr(self):
df['datetime1'] = datetime.datetime(2001,1,2,0,0)
df['datetime2'] = datetime.datetime(2001,1,3,0,0)
df.ix[3:6,['obj1']] = np.nan
- df = df.consolidate().convert_objects()
+ df = df.consolidate().convert_objects(datetime=True)
warnings.filterwarnings('ignore', category=PerformanceWarning)
store['df'] = df
@@ -728,7 +728,7 @@ def test_put_mixed_type(self):
df['datetime1'] = datetime.datetime(2001, 1, 2, 0, 0)
df['datetime2'] = datetime.datetime(2001, 1, 3, 0, 0)
df.ix[3:6, ['obj1']] = np.nan
- df = df.consolidate().convert_objects()
+ df = df.consolidate().convert_objects(datetime=True)
with ensure_clean_store(self.path) as store:
_maybe_remove(store, 'df')
@@ -1381,7 +1381,7 @@ def check_col(key,name,size):
df_dc.ix[7:9, 'string'] = 'bar'
df_dc['string2'] = 'cool'
df_dc['datetime'] = Timestamp('20010102')
- df_dc = df_dc.convert_objects()
+ df_dc = df_dc.convert_objects(datetime=True)
df_dc.ix[3:5, ['A', 'B', 'datetime']] = np.nan
_maybe_remove(store, 'df_dc')
@@ -1843,7 +1843,7 @@ def test_table_mixed_dtypes(self):
df['datetime1'] = datetime.datetime(2001, 1, 2, 0, 0)
df['datetime2'] = datetime.datetime(2001, 1, 3, 0, 0)
df.ix[3:6, ['obj1']] = np.nan
- df = df.consolidate().convert_objects()
+ df = df.consolidate().convert_objects(datetime=True)
with ensure_clean_store(self.path) as store:
store.append('df1_mixed', df)
@@ -1899,7 +1899,7 @@ def test_unimplemented_dtypes_table_columns(self):
df['obj1'] = 'foo'
df['obj2'] = 'bar'
df['datetime1'] = datetime.date(2001, 1, 2)
- df = df.consolidate().convert_objects()
+ df = df.consolidate().convert_objects(datetime=True)
with ensure_clean_store(self.path) as store:
# this fails because we have a date in the object block......
diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py
index 97bbfb0edf92c..8eb60b13fcc81 100644
--- a/pandas/io/tests/test_stata.py
+++ b/pandas/io/tests/test_stata.py
@@ -383,7 +383,7 @@ def test_read_write_reread_dta14(self):
expected = self.read_csv(self.csv14)
cols = ['byte_', 'int_', 'long_', 'float_', 'double_']
for col in cols:
- expected[col] = expected[col].convert_objects(convert_numeric=True)
+ expected[col] = expected[col].convert_objects(datetime=True, numeric=True)
expected['float_'] = expected['float_'].astype(np.float32)
expected['date_td'] = pd.to_datetime(expected['date_td'], coerce=True)
diff --git a/pandas/io/wb.py b/pandas/io/wb.py
index 7a9443c4b9ac6..fba4c72a51376 100644
--- a/pandas/io/wb.py
+++ b/pandas/io/wb.py
@@ -155,7 +155,7 @@ def download(country=['MX', 'CA', 'US'], indicator=['NY.GDP.MKTP.CD', 'NY.GNS.IC
out = reduce(lambda x, y: x.merge(y, how='outer'), data)
out = out.drop('iso_code', axis=1)
out = out.set_index(['country', 'year'])
- out = out.convert_objects(convert_numeric=True)
+ out = out.convert_objects(datetime=True, numeric=True)
return out
else:
msg = "No indicators returned data."
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index f7121fa54a5b1..94f151efbe2a6 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -4,7 +4,7 @@
import re
import nose
-from nose.tools import assert_equal
+from nose.tools import assert_equal, assert_true
import numpy as np
from pandas.tslib import iNaT, NaT
from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp, Float64Index
@@ -1026,6 +1026,22 @@ def test_dict_compat():
assert(com._dict_compat(expected) == expected)
assert(com._dict_compat(data_unchanged) == data_unchanged)
+def test_possibly_convert_objects_copy():
+ values = np.array([1, 2])
+
+ out = com._possibly_convert_objects(values, copy=False)
+ assert_true(values is out)
+
+ out = com._possibly_convert_objects(values, copy=True)
+ assert_true(values is not out)
+
+ values = np.array(['apply','banana'])
+ out = com._possibly_convert_objects(values, copy=False)
+ assert_true(values is out)
+
+ out = com._possibly_convert_objects(values, copy=True)
+ assert_true(values is not out)
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 4dea73a3a73a1..cc807aae2be49 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -6434,7 +6434,8 @@ def make_dtnat_arr(n,nnat=None):
with ensure_clean('.csv') as pth:
df=DataFrame(dict(a=s1,b=s2))
df.to_csv(pth,chunksize=chunksize)
- recons = DataFrame.from_csv(pth).convert_objects('coerce')
+ recons = DataFrame.from_csv(pth).convert_objects(datetime=True,
+ coerce=True)
assert_frame_equal(df, recons,check_names=False,check_less_precise=True)
for ncols in [4]:
@@ -7144,7 +7145,7 @@ def test_dtypes(self):
def test_convert_objects(self):
oops = self.mixed_frame.T.T
- converted = oops.convert_objects()
+ converted = oops.convert_objects(datetime=True)
assert_frame_equal(converted, self.mixed_frame)
self.assertEqual(converted['A'].dtype, np.float64)
@@ -7157,7 +7158,8 @@ def test_convert_objects(self):
self.mixed_frame['J'] = '1.'
self.mixed_frame['K'] = '1'
self.mixed_frame.ix[0:5,['J','K']] = 'garbled'
- converted = self.mixed_frame.convert_objects(convert_numeric=True)
+ converted = self.mixed_frame.convert_objects(datetime=True,
+ numeric=True)
self.assertEqual(converted['H'].dtype, 'float64')
self.assertEqual(converted['I'].dtype, 'int64')
self.assertEqual(converted['J'].dtype, 'float64')
@@ -7179,14 +7181,14 @@ def test_convert_objects(self):
# mixed in a single column
df = DataFrame(dict(s = Series([1, 'na', 3 ,4])))
- result = df.convert_objects(convert_numeric=True)
+ result = df.convert_objects(datetime=True, numeric=True)
expected = DataFrame(dict(s = Series([1, np.nan, 3 ,4])))
assert_frame_equal(result, expected)
def test_convert_objects_no_conversion(self):
mixed1 = DataFrame(
{'a': [1, 2, 3], 'b': [4.0, 5, 6], 'c': ['x', 'y', 'z']})
- mixed2 = mixed1.convert_objects()
+ mixed2 = mixed1.convert_objects(datetime=True)
assert_frame_equal(mixed1, mixed2)
def test_append_series_dict(self):
@@ -10698,7 +10700,7 @@ def test_apply_convert_objects(self):
'F': np.random.randn(11)})
result = data.apply(lambda x: x, axis=1)
- assert_frame_equal(result.convert_objects(), data)
+ assert_frame_equal(result.convert_objects(datetime=True), data)
def test_apply_attach_name(self):
result = self.frame.apply(lambda x: x.name)
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index e2a447207db82..91902aae3c835 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -601,7 +601,7 @@ def f(grp):
return grp.iloc[0]
result = df.groupby('A').apply(f)[['C']]
e = df.groupby('A').first()[['C']]
- e.loc['Pony'] = np.nan
+ e.loc['Pony'] = pd.NaT
assert_frame_equal(result,e)
# scalar outputs
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index b666fba274b70..624fa11ac908a 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -3060,7 +3060,8 @@ def test_astype_assignment(self):
assert_frame_equal(df,expected)
df = df_orig.copy()
- df.iloc[:,0:2] = df.iloc[:,0:2].convert_objects(convert_numeric=True)
+ df.iloc[:,0:2] = df.iloc[:,0:2].convert_objects(datetime=True,
+ numeric=True)
expected = DataFrame([[1,2,'3','.4',5,6.,'foo']],columns=list('ABCDEFG'))
assert_frame_equal(df,expected)
diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py
index 36585abd1b98f..ef05b40827dfd 100644
--- a/pandas/tests/test_internals.py
+++ b/pandas/tests/test_internals.py
@@ -554,7 +554,7 @@ def _compare(old_mgr, new_mgr):
mgr.set('a', np.array(['1'] * N, dtype=np.object_))
mgr.set('b', np.array(['2.'] * N, dtype=np.object_))
mgr.set('foo', np.array(['foo.'] * N, dtype=np.object_))
- new_mgr = mgr.convert(convert_numeric=True)
+ new_mgr = mgr.convert(numeric=True)
self.assertEqual(new_mgr.get('a').dtype, np.int64)
self.assertEqual(new_mgr.get('b').dtype, np.float64)
self.assertEqual(new_mgr.get('foo').dtype, np.object_)
@@ -566,7 +566,7 @@ def _compare(old_mgr, new_mgr):
mgr.set('a', np.array(['1'] * N, dtype=np.object_))
mgr.set('b', np.array(['2.'] * N, dtype=np.object_))
mgr.set('foo', np.array(['foo.'] * N, dtype=np.object_))
- new_mgr = mgr.convert(convert_numeric=True)
+ new_mgr = mgr.convert(numeric=True)
self.assertEqual(new_mgr.get('a').dtype, np.int64)
self.assertEqual(new_mgr.get('b').dtype, np.float64)
self.assertEqual(new_mgr.get('foo').dtype, np.object_)
diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py
index bc0aaee1b10b6..9cdc769dd7d74 100644
--- a/pandas/tests/test_panel.py
+++ b/pandas/tests/test_panel.py
@@ -1119,7 +1119,7 @@ def test_convert_objects(self):
# GH 4937
p = Panel(dict(A = dict(a = ['1','1.0'])))
expected = Panel(dict(A = dict(a = [1,1.0])))
- result = p.convert_objects(convert_numeric='force')
+ result = p.convert_objects(numeric=True, coerce=True)
assert_panel_equal(result, expected)
def test_dtypes(self):
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 075362e006206..7326d7a9d811d 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -8,6 +8,7 @@
from inspect import getargspec
from itertools import product, starmap
from distutils.version import LooseVersion
+import warnings
import nose
@@ -5912,39 +5913,105 @@ def test_apply_dont_convert_dtype(self):
result = s.apply(f, convert_dtype=False)
self.assertEqual(result.dtype, object)
+ # GH 10265
def test_convert_objects(self):
+ # Tests: All to nans, coerce, true
+ # Test coercion returns correct type
+ s = Series(['a', 'b', 'c'])
+ results = s.convert_objects(datetime=True, coerce=True)
+ expected = Series([lib.NaT] * 3)
+ assert_series_equal(results, expected)
+
+ results = s.convert_objects(numeric=True, coerce=True)
+ expected = Series([np.nan] * 3)
+ assert_series_equal(results, expected)
+
+ expected = Series([lib.NaT] * 3, dtype=np.dtype('m8[ns]'))
+ results = s.convert_objects(timedelta=True, coerce=True)
+ assert_series_equal(results, expected)
+
+ dt = datetime(2001, 1, 1, 0, 0)
+ td = dt - datetime(2000, 1, 1, 0, 0)
+
+ # Test coercion with mixed types
+ s = Series(['a', '3.1415', dt, td])
+ results = s.convert_objects(datetime=True, coerce=True)
+ expected = Series([lib.NaT, lib.NaT, dt, lib.NaT])
+ assert_series_equal(results, expected)
+
+ results = s.convert_objects(numeric=True, coerce=True)
+ expected = Series([nan, 3.1415, nan, nan])
+ assert_series_equal(results, expected)
+
+ results = s.convert_objects(timedelta=True, coerce=True)
+ expected = Series([lib.NaT, lib.NaT, lib.NaT, td],
+ dtype=np.dtype('m8[ns]'))
+ assert_series_equal(results, expected)
+
+ # Test standard conversion returns original
+ results = s.convert_objects(datetime=True)
+ assert_series_equal(results, s)
+ results = s.convert_objects(numeric=True)
+ expected = Series([nan, 3.1415, nan, nan])
+ assert_series_equal(results, expected)
+ results = s.convert_objects(timedelta=True)
+ assert_series_equal(results, s)
+
+ # test pass-through and non-conversion when other types selected
+ s = Series(['1.0','2.0','3.0'])
+ results = s.convert_objects(datetime=True, numeric=True, timedelta=True)
+ expected = Series([1.0,2.0,3.0])
+ assert_series_equal(results, expected)
+ results = s.convert_objects(True,False,True)
+ assert_series_equal(results, s)
+
+ s = Series([datetime(2001, 1, 1, 0, 0),datetime(2001, 1, 1, 0, 0)],
+ dtype='O')
+ results = s.convert_objects(datetime=True, numeric=True, timedelta=True)
+ expected = Series([datetime(2001, 1, 1, 0, 0),datetime(2001, 1, 1, 0, 0)])
+ assert_series_equal(results, expected)
+ results = s.convert_objects(datetime=False,numeric=True,timedelta=True)
+ assert_series_equal(results, s)
+
+ td = datetime(2001, 1, 1, 0, 0) - datetime(2000, 1, 1, 0, 0)
+ s = Series([td, td], dtype='O')
+ results = s.convert_objects(datetime=True, numeric=True, timedelta=True)
+ expected = Series([td, td])
+ assert_series_equal(results, expected)
+ results = s.convert_objects(True,True,False)
+ assert_series_equal(results, s)
+
s = Series([1., 2, 3], index=['a', 'b', 'c'])
- result = s.convert_objects(convert_dates=False, convert_numeric=True)
+ result = s.convert_objects(numeric=True)
assert_series_equal(result, s)
# force numeric conversion
r = s.copy().astype('O')
r['a'] = '1'
- result = r.convert_objects(convert_dates=False, convert_numeric=True)
+ result = r.convert_objects(numeric=True)
assert_series_equal(result, s)
r = s.copy().astype('O')
r['a'] = '1.'
- result = r.convert_objects(convert_dates=False, convert_numeric=True)
+ result = r.convert_objects(numeric=True)
assert_series_equal(result, s)
r = s.copy().astype('O')
r['a'] = 'garbled'
+ result = r.convert_objects(numeric=True)
expected = s.copy()
- expected['a'] = np.nan
- result = r.convert_objects(convert_dates=False, convert_numeric=True)
+ expected['a'] = nan
assert_series_equal(result, expected)
# GH 4119, not converting a mixed type (e.g.floats and object)
s = Series([1, 'na', 3, 4])
- result = s.convert_objects(convert_numeric=True)
- expected = Series([1, np.nan, 3, 4])
+ result = s.convert_objects(datetime=True, numeric=True)
+ expected = Series([1, nan, 3, 4])
assert_series_equal(result, expected)
s = Series([1, '', 3, 4])
- result = s.convert_objects(convert_numeric=True)
- expected = Series([1, np.nan, 3, 4])
+ result = s.convert_objects(datetime=True, numeric=True)
assert_series_equal(result, expected)
# dates
@@ -5953,38 +6020,34 @@ def test_convert_objects(self):
s2 = Series([datetime(2001, 1, 1, 0, 0), datetime(2001, 1, 2, 0, 0), datetime(
2001, 1, 3, 0, 0), 'foo', 1.0, 1, Timestamp('20010104'), '20010105'], dtype='O')
- result = s.convert_objects(convert_dates=True, convert_numeric=False)
+ result = s.convert_objects(datetime=True)
expected = Series(
[Timestamp('20010101'), Timestamp('20010102'), Timestamp('20010103')], dtype='M8[ns]')
assert_series_equal(result, expected)
- result = s.convert_objects(
- convert_dates='coerce', convert_numeric=False)
- result = s.convert_objects(
- convert_dates='coerce', convert_numeric=True)
+ result = s.convert_objects(datetime=True, coerce=True)
assert_series_equal(result, expected)
expected = Series(
[Timestamp(
'20010101'), Timestamp('20010102'), Timestamp('20010103'),
lib.NaT, lib.NaT, lib.NaT, Timestamp('20010104'), Timestamp('20010105')], dtype='M8[ns]')
- result = s2.convert_objects(
- convert_dates='coerce', convert_numeric=False)
+ result = s2.convert_objects(datetime=True,
+ numeric=False,
+ timedelta=False,
+ coerce=True)
assert_series_equal(result, expected)
- result = s2.convert_objects(
- convert_dates='coerce', convert_numeric=True)
+ result = s2.convert_objects(datetime=True, coerce=True)
assert_series_equal(result, expected)
- # preserver all-nans (if convert_dates='coerce')
s = Series(['foo', 'bar', 1, 1.0], dtype='O')
- result = s.convert_objects(
- convert_dates='coerce', convert_numeric=False)
- assert_series_equal(result, s)
+ result = s.convert_objects(datetime=True, coerce=True)
+ expected = Series([lib.NaT]*4)
+ assert_series_equal(result, expected)
# preserver if non-object
s = Series([1], dtype='float32')
- result = s.convert_objects(
- convert_dates='coerce', convert_numeric=False)
+ result = s.convert_objects(datetime=True, coerce=True)
assert_series_equal(result, s)
#r = s.copy()
@@ -5993,23 +6056,31 @@ def test_convert_objects(self):
#self.assertEqual(result.dtype, 'M8[ns]')
# dateutil parses some single letters into today's value as a date
+ expected = Series([lib.NaT])
for x in 'abcdefghijklmnopqrstuvwxyz':
s = Series([x])
- result = s.convert_objects(convert_dates='coerce')
- assert_series_equal(result, s)
+ result = s.convert_objects(datetime=True, coerce=True)
+ assert_series_equal(result, expected)
s = Series([x.upper()])
- result = s.convert_objects(convert_dates='coerce')
- assert_series_equal(result, s)
+ result = s.convert_objects(datetime=True, coerce=True)
+ assert_series_equal(result, expected)
+
+ def test_convert_objects_no_arg_warning(self):
+ s = Series(['1.0','2'])
+ with warnings.catch_warnings(record=True) as w:
+ warnings.simplefilter('always', DeprecationWarning)
+ s.convert_objects()
+ self.assertEqual(len(w), 1)
def test_convert_objects_preserve_bool(self):
s = Series([1, True, 3, 5], dtype=object)
- r = s.convert_objects(convert_numeric=True)
+ r = s.convert_objects(datetime=True, numeric=True)
e = Series([1, 1, 3, 5], dtype='i8')
tm.assert_series_equal(r, e)
def test_convert_objects_preserve_all_bool(self):
s = Series([False, True, False, False], dtype=object)
- r = s.convert_objects(convert_numeric=True)
+ r = s.convert_objects(datetime=True, numeric=True)
e = Series([False, True, False, False], dtype=bool)
tm.assert_series_equal(r, e)
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 07d7ced02e6ba..54298e8434a1b 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -1030,7 +1030,7 @@ def _compute_plot_data(self):
label = 'None'
data = data.to_frame(name=label)
- numeric_data = data.convert_objects()._get_numeric_data()
+ numeric_data = data.convert_objects(datetime=True)._get_numeric_data()
try:
is_empty = numeric_data.empty
@@ -1960,7 +1960,8 @@ def __init__(self, data, bins=10, bottom=0, **kwargs):
def _args_adjust(self):
if com.is_integer(self.bins):
# create common bin edge
- values = self.data.convert_objects()._get_numeric_data()
+ values = (self.data.convert_objects(datetime=True)
+ ._get_numeric_data())
values = np.ravel(values)
values = values[~com.isnull(values)]
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 27cd5e89220a9..9bb7d7261a8df 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -2416,6 +2416,8 @@ cdef inline parse_timedelta_string(object ts, coerce=False):
elif have_dot:
if (len(number) or len(frac)) and not len(unit) and current_unit is None:
+ if coerce:
+ return iNaT
raise ValueError("no units specified")
if len(frac) > 0 and len(frac) <= 3:
| Changes behavior of convert objects so that passing 'coerce' will
ensure that data of the correct type is returned, even if all
values are null-types (NaN or NaT).
closes #9589
| https://api.github.com/repos/pandas-dev/pandas/pulls/10265 | 2015-06-03T21:53:02Z | 2015-07-14T12:47:25Z | 2015-07-14T12:47:25Z | 2015-07-15T21:14:39Z |
BUG: Should allow numeric mysql table/column names | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index ddfe6fa0b2f74..c1bf3b9252428 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -88,3 +88,5 @@ Bug Fixes
- Bug where infer_freq infers timerule (WOM-5XXX) unsupported by to_offset (:issue:`9425`)
- Bug to handle masking empty ``DataFrame``(:issue:`10126`)
+- Bug where MySQL interface could not handle numeric table/column names (:issue:`10255`)
+
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index ad88d74a5aa91..b4e8c7de2b4e1 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -1283,8 +1283,6 @@ def _get_valid_mysql_name(name):
if not re.match(basere, c):
if not (0x80 < ord(c) < 0xFFFF):
raise ValueError("Invalid MySQL identifier '%s'" % uname)
- if not re.match(r'[^0-9]', uname):
- raise ValueError('MySQL identifier cannot be entirely numeric')
return '`' + uname + '`'
diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index 9576f80696350..a848917196e62 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -1738,7 +1738,8 @@ def test_illegal_names(self):
for ndx, weird_name in enumerate(['test_weird_name]','test_weird_name[',
'test_weird_name`','test_weird_name"', 'test_weird_name\'',
- '_b.test_weird_name_01-30', '"_b.test_weird_name_01-30"']):
+ '_b.test_weird_name_01-30', '"_b.test_weird_name_01-30"',
+ '12345','12345blah']):
df.to_sql(weird_name, self.conn, flavor=self.flavor)
sql.table_exists(weird_name, self.conn)
@@ -1839,16 +1840,30 @@ def test_to_sql_save_index(self):
self._to_sql_save_index()
def test_illegal_names(self):
+ df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
+
+ # These tables and columns should be ok
+ for ndx, ok_name in enumerate(['99beginswithnumber','12345']):
+ df.to_sql(ok_name, self.conn, flavor=self.flavor, index=False,
+ if_exists='replace')
+ self.conn.cursor().execute("DROP TABLE `%s`" % ok_name)
+ self.conn.commit()
+ df2 = DataFrame([[1, 2], [3, 4]], columns=['a', ok_name])
+ c_tbl = 'test_ok_col_name%d'%ndx
+ df2.to_sql(c_tbl, self.conn, flavor=self.flavor, index=False,
+ if_exists='replace')
+ self.conn.cursor().execute("DROP TABLE `%s`" % c_tbl)
+ self.conn.commit()
+
# For MySQL, these should raise ValueError
for ndx, illegal_name in enumerate(['test_illegal_name]','test_illegal_name[',
'test_illegal_name`','test_illegal_name"', 'test_illegal_name\'', '']):
- df = DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
self.assertRaises(ValueError, df.to_sql, illegal_name, self.conn,
flavor=self.flavor, index=False)
df2 = DataFrame([[1, 2], [3, 4]], columns=['a', illegal_name])
c_tbl = 'test_illegal_col_name%d'%ndx
- self.assertRaises(ValueError, df2.to_sql, 'test_illegal_col_name',
+ self.assertRaises(ValueError, df2.to_sql, c_tbl,
self.conn, flavor=self.flavor, index=False)
| Closes #10255 , alternative to #10262 .
| https://api.github.com/repos/pandas-dev/pandas/pulls/10263 | 2015-06-03T20:57:06Z | 2015-06-05T09:11:25Z | 2015-06-05T09:11:25Z | 2015-06-05T09:11:32Z |
TST: test_sql: properly drop tables with names that need to be quoted | diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index ba951c7cb513d..d95babff2653b 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -165,15 +165,64 @@
}
+class MixInBase(object):
+ def tearDown(self):
+ for tbl in self._get_all_tables():
+ self.drop_table(tbl)
+ self._close_conn()
+
+
+class MySQLMixIn(MixInBase):
+ def drop_table(self, table_name):
+ cur = self.conn.cursor()
+ cur.execute("DROP TABLE IF EXISTS %s" % sql._get_valid_mysql_name(table_name))
+ self.conn.commit()
+
+ def _get_all_tables(self):
+ cur = self.conn.cursor()
+ cur.execute('SHOW TABLES')
+ return [table[0] for table in cur.fetchall()]
+
+ def _close_conn(self):
+ from pymysql.err import Error
+ try:
+ self.conn.close()
+ except Error:
+ pass
+
+
+class SQLiteMixIn(MixInBase):
+ def drop_table(self, table_name):
+ self.conn.execute("DROP TABLE IF EXISTS %s" % sql._get_valid_sqlite_name(table_name))
+ self.conn.commit()
+
+ def _get_all_tables(self):
+ c = self.conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
+ return [table[0] for table in c.fetchall()]
+
+ def _close_conn(self):
+ self.conn.close()
+
+
+class SQLAlchemyMixIn(MixInBase):
+ def drop_table(self, table_name):
+ sql.SQLDatabase(self.conn).drop_table(table_name)
+
+ def _get_all_tables(self):
+ meta = sqlalchemy.schema.MetaData(bind=self.conn)
+ meta.reflect()
+ table_list = meta.tables.keys()
+ return table_list
+
+ def _close_conn(self):
+ pass
+
class PandasSQLTest(unittest.TestCase):
"""
Base class with common private methods for SQLAlchemy and fallback cases.
"""
- def drop_table(self, table_name):
- self._get_exec().execute("DROP TABLE IF EXISTS %s" % table_name)
-
def _get_exec(self):
if hasattr(self.conn, 'execute'):
return self.conn
@@ -768,7 +817,7 @@ def test_categorical(self):
tm.assert_frame_equal(res, df)
-class TestSQLApi(_TestSQLApi):
+class TestSQLApi(SQLAlchemyMixIn, _TestSQLApi):
"""
Test the public API as it would be used directly
@@ -889,13 +938,14 @@ def tearDown(self):
self.conn.close()
self.conn = self.__engine
self.pandasSQL = sql.SQLDatabase(self.__engine)
+ super(_EngineToConnMixin, self).tearDown()
class TestSQLApiConn(_EngineToConnMixin, TestSQLApi):
pass
-class TestSQLiteFallbackApi(_TestSQLApi):
+class TestSQLiteFallbackApi(SQLiteMixIn, _TestSQLApi):
"""
Test the public sqlite connection fallback API
@@ -978,7 +1028,7 @@ def test_sqlite_type_mapping(self):
#--- Database flavor specific tests
-class _TestSQLAlchemy(PandasSQLTest):
+class _TestSQLAlchemy(SQLAlchemyMixIn, PandasSQLTest):
"""
Base class for testing the sqlalchemy backend.
@@ -1451,10 +1501,6 @@ def setup_driver(cls):
# sqlite3 is built-in
cls.driver = None
- def tearDown(self):
- super(_TestSQLiteAlchemy, self).tearDown()
- # in memory so tables should not be removed explicitly
-
def test_default_type_conversion(self):
df = sql.read_sql_table("types_test_data", self.conn)
@@ -1511,12 +1557,6 @@ def setup_driver(cls):
except ImportError:
raise nose.SkipTest('pymysql not installed')
- def tearDown(self):
- super(_TestMySQLAlchemy, self).tearDown()
- c = self.conn.execute('SHOW TABLES')
- for table in c.fetchall():
- self.conn.execute('DROP TABLE %s' % table[0])
-
def test_default_type_conversion(self):
df = sql.read_sql_table("types_test_data", self.conn)
@@ -1586,14 +1626,6 @@ def setup_driver(cls):
except ImportError:
raise nose.SkipTest('psycopg2 not installed')
- def tearDown(self):
- super(_TestPostgreSQLAlchemy, self).tearDown()
- c = self.conn.execute(
- "SELECT table_name FROM information_schema.tables"
- " WHERE table_schema = 'public'")
- for table in c.fetchall():
- self.conn.execute("DROP TABLE %s" % table[0])
-
def test_schema_support(self):
# only test this for postgresql (schema's not supported in mysql/sqlite)
df = DataFrame({'col1':[1, 2], 'col2':[0.1, 0.2], 'col3':['a', 'n']})
@@ -1694,7 +1726,7 @@ class TestSQLiteAlchemyConn(_TestSQLiteAlchemy, _TestSQLAlchemyConn):
#------------------------------------------------------------------------------
#--- Test Sqlite / MySQL fallback
-class TestSQLiteFallback(PandasSQLTest):
+class TestSQLiteFallback(SQLiteMixIn, PandasSQLTest):
"""
Test the fallback mode against an in-memory sqlite database.
@@ -1705,11 +1737,6 @@ class TestSQLiteFallback(PandasSQLTest):
def connect(cls):
return sqlite3.connect(':memory:')
- def drop_table(self, table_name):
- cur = self.conn.cursor()
- cur.execute("DROP TABLE IF EXISTS %s" % table_name)
- self.conn.commit()
-
def setUp(self):
self.conn = self.connect()
self.pandasSQL = sql.SQLiteDatabase(self.conn, 'sqlite')
@@ -1856,7 +1883,7 @@ def test_illegal_names(self):
for ndx, weird_name in enumerate(['test_weird_name]','test_weird_name[',
'test_weird_name`','test_weird_name"', 'test_weird_name\'',
'_b.test_weird_name_01-30', '"_b.test_weird_name_01-30"',
- '12345','12345blah']):
+ '99beginswithnumber', '12345']):
df.to_sql(weird_name, self.conn, flavor=self.flavor)
sql.table_exists(weird_name, self.conn)
@@ -1866,7 +1893,7 @@ def test_illegal_names(self):
sql.table_exists(c_tbl, self.conn)
-class TestMySQLLegacy(TestSQLiteFallback):
+class TestMySQLLegacy(MySQLMixIn, TestSQLiteFallback):
"""
Test the legacy mode against a MySQL database.
@@ -1895,11 +1922,6 @@ def setup_driver(cls):
def connect(cls):
return cls.driver.connect(host='127.0.0.1', user='root', passwd='', db='pandas_nosetest')
- def drop_table(self, table_name):
- cur = self.conn.cursor()
- cur.execute("DROP TABLE IF EXISTS %s" % table_name)
- self.conn.commit()
-
def _count_rows(self, table_name):
cur = self._get_exec()
cur.execute(
@@ -1918,14 +1940,6 @@ def setUp(self):
self._load_iris_data()
self._load_test1_data()
- def tearDown(self):
- c = self.conn.cursor()
- c.execute('SHOW TABLES')
- for table in c.fetchall():
- c.execute('DROP TABLE %s' % table[0])
- self.conn.commit()
- self.conn.close()
-
def test_a_deprecation(self):
with tm.assert_produces_warning(FutureWarning):
sql.to_sql(self.test_frame1, 'test_frame1', self.conn,
@@ -1963,14 +1977,10 @@ def test_illegal_names(self):
for ndx, ok_name in enumerate(['99beginswithnumber','12345']):
df.to_sql(ok_name, self.conn, flavor=self.flavor, index=False,
if_exists='replace')
- self.conn.cursor().execute("DROP TABLE `%s`" % ok_name)
- self.conn.commit()
df2 = DataFrame([[1, 2], [3, 4]], columns=['a', ok_name])
- c_tbl = 'test_ok_col_name%d'%ndx
- df2.to_sql(c_tbl, self.conn, flavor=self.flavor, index=False,
+
+ df2.to_sql('test_ok_col_name', self.conn, flavor=self.flavor, index=False,
if_exists='replace')
- self.conn.cursor().execute("DROP TABLE `%s`" % c_tbl)
- self.conn.commit()
# For MySQL, these should raise ValueError
for ndx, illegal_name in enumerate(['test_illegal_name]','test_illegal_name[',
@@ -1979,8 +1989,7 @@ def test_illegal_names(self):
flavor=self.flavor, index=False)
df2 = DataFrame([[1, 2], [3, 4]], columns=['a', illegal_name])
- c_tbl = 'test_illegal_col_name%d'%ndx
- self.assertRaises(ValueError, df2.to_sql, c_tbl,
+ self.assertRaises(ValueError, df2.to_sql, 'test_illegal_col_name%d'%ndx,
self.conn, flavor=self.flavor, index=False)
@@ -2022,10 +2031,10 @@ def _skip_if_no_pymysql():
raise nose.SkipTest('pymysql not installed, skipping')
-class TestXSQLite(tm.TestCase):
+class TestXSQLite(SQLiteMixIn, tm.TestCase):
def setUp(self):
- self.db = sqlite3.connect(':memory:')
+ self.conn = sqlite3.connect(':memory:')
def test_basic(self):
frame = tm.makeTimeDataFrame()
@@ -2036,34 +2045,34 @@ def test_write_row_by_row(self):
frame = tm.makeTimeDataFrame()
frame.ix[0, 0] = np.nan
create_sql = sql.get_schema(frame, 'test', 'sqlite')
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(create_sql)
- cur = self.db.cursor()
+ cur = self.conn.cursor()
ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
for idx, row in frame.iterrows():
fmt_sql = format_query(ins, *row)
sql.tquery(fmt_sql, cur=cur)
- self.db.commit()
+ self.conn.commit()
- result = sql.read_frame("select * from test", con=self.db)
+ result = sql.read_frame("select * from test", con=self.conn)
result.index = frame.index
tm.assert_frame_equal(result, frame)
def test_execute(self):
frame = tm.makeTimeDataFrame()
create_sql = sql.get_schema(frame, 'test', 'sqlite')
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(create_sql)
ins = "INSERT INTO test VALUES (?, ?, ?, ?)"
row = frame.ix[0]
- sql.execute(ins, self.db, params=tuple(row))
- self.db.commit()
+ sql.execute(ins, self.conn, params=tuple(row))
+ self.conn.commit()
- result = sql.read_frame("select * from test", self.db)
+ result = sql.read_frame("select * from test", self.conn)
result.index = frame.index[:1]
tm.assert_frame_equal(result, frame[:1])
@@ -2080,7 +2089,7 @@ def test_schema(self):
create_sql = sql.get_schema(frame, 'test', 'sqlite', keys=['A', 'B'],)
lines = create_sql.splitlines()
self.assertTrue('PRIMARY KEY ("A", "B")' in create_sql)
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(create_sql)
def test_execute_fail(self):
@@ -2093,17 +2102,17 @@ def test_execute_fail(self):
PRIMARY KEY (a, b)
);
"""
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(create_sql)
- sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.db)
- sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)', self.db)
+ sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
+ sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)', self.conn)
try:
sys.stdout = StringIO()
self.assertRaises(Exception, sql.execute,
'INSERT INTO test VALUES("foo", "bar", 7)',
- self.db)
+ self.conn)
finally:
sys.stdout = sys.__stdout__
@@ -2117,24 +2126,27 @@ def test_execute_closed_connection(self):
PRIMARY KEY (a, b)
);
"""
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(create_sql)
- sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.db)
- self.db.close()
+ sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
+ self.conn.close()
try:
sys.stdout = StringIO()
self.assertRaises(Exception, sql.tquery, "select * from test",
- con=self.db)
+ con=self.conn)
finally:
sys.stdout = sys.__stdout__
+ # Initialize connection again (needed for tearDown)
+ self.setUp()
+
def test_na_roundtrip(self):
pass
def _check_roundtrip(self, frame):
- sql.write_frame(frame, name='test_table', con=self.db)
- result = sql.read_frame("select * from test_table", self.db)
+ sql.write_frame(frame, name='test_table', con=self.conn)
+ result = sql.read_frame("select * from test_table", self.conn)
# HACK! Change this once indexes are handled properly.
result.index = frame.index
@@ -2145,8 +2157,8 @@ def _check_roundtrip(self, frame):
frame['txt'] = ['a'] * len(frame)
frame2 = frame.copy()
frame2['Idx'] = Index(lrange(len(frame2))) + 10
- sql.write_frame(frame2, name='test_table2', con=self.db)
- result = sql.read_frame("select * from test_table2", self.db,
+ sql.write_frame(frame2, name='test_table2', con=self.conn)
+ result = sql.read_frame("select * from test_table2", self.conn,
index_col='Idx')
expected = frame.copy()
expected.index = Index(lrange(len(frame2))) + 10
@@ -2155,8 +2167,8 @@ def _check_roundtrip(self, frame):
def test_tquery(self):
frame = tm.makeTimeDataFrame()
- sql.write_frame(frame, name='test_table', con=self.db)
- result = sql.tquery("select A from test_table", self.db)
+ sql.write_frame(frame, name='test_table', con=self.conn)
+ result = sql.tquery("select A from test_table", self.conn)
expected = Series(frame.A.values, frame.index) # not to have name
result = Series(result, frame.index)
tm.assert_series_equal(result, expected)
@@ -2164,27 +2176,27 @@ def test_tquery(self):
try:
sys.stdout = StringIO()
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'select * from blah', con=self.db)
+ 'select * from blah', con=self.conn)
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'select * from blah', con=self.db, retry=True)
+ 'select * from blah', con=self.conn, retry=True)
finally:
sys.stdout = sys.__stdout__
def test_uquery(self):
frame = tm.makeTimeDataFrame()
- sql.write_frame(frame, name='test_table', con=self.db)
+ sql.write_frame(frame, name='test_table', con=self.conn)
stmt = 'INSERT INTO test_table VALUES(2.314, -123.1, 1.234, 2.3)'
- self.assertEqual(sql.uquery(stmt, con=self.db), 1)
+ self.assertEqual(sql.uquery(stmt, con=self.conn), 1)
try:
sys.stdout = StringIO()
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'insert into blah values (1)', con=self.db)
+ 'insert into blah values (1)', con=self.conn)
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'insert into blah values (1)', con=self.db,
+ 'insert into blah values (1)', con=self.conn,
retry=True)
finally:
sys.stdout = sys.__stdout__
@@ -2193,16 +2205,16 @@ def test_keyword_as_column_names(self):
'''
'''
df = DataFrame({'From':np.ones(5)})
- sql.write_frame(df, con = self.db, name = 'testkeywords')
+ sql.write_frame(df, con = self.conn, name = 'testkeywords')
def test_onecolumn_of_integer(self):
# GH 3628
# a column_of_integers dataframe should transfer well to sql
mono_df=DataFrame([1 , 2], columns=['c0'])
- sql.write_frame(mono_df, con = self.db, name = 'mono_df')
+ sql.write_frame(mono_df, con = self.conn, name = 'mono_df')
# computing the sum via sql
- con_x=self.db
+ con_x=self.conn
the_sum=sum([my_c0[0] for my_c0 in con_x.execute("select * from mono_df")])
# it should not fail, and gives 3 ( Issue #3628 )
self.assertEqual(the_sum , 3)
@@ -2221,56 +2233,53 @@ def clean_up(test_table_to_drop):
Drops tables created from individual tests
so no dependencies arise from sequential tests
"""
- if sql.table_exists(test_table_to_drop, self.db, flavor='sqlite'):
- cur = self.db.cursor()
- cur.execute("DROP TABLE %s" % test_table_to_drop)
- cur.close()
+ self.drop_table(test_table_to_drop)
# test if invalid value for if_exists raises appropriate error
self.assertRaises(ValueError,
sql.write_frame,
frame=df_if_exists_1,
- con=self.db,
+ con=self.conn,
name=table_name,
flavor='sqlite',
if_exists='notvalidvalue')
clean_up(table_name)
# test if_exists='fail'
- sql.write_frame(frame=df_if_exists_1, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_1, con=self.conn, name=table_name,
flavor='sqlite', if_exists='fail')
self.assertRaises(ValueError,
sql.write_frame,
frame=df_if_exists_1,
- con=self.db,
+ con=self.conn,
name=table_name,
flavor='sqlite',
if_exists='fail')
# test if_exists='replace'
- sql.write_frame(frame=df_if_exists_1, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_1, con=self.conn, name=table_name,
flavor='sqlite', if_exists='replace')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(1, 'A'), (2, 'B')])
- sql.write_frame(frame=df_if_exists_2, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_2, con=self.conn, name=table_name,
flavor='sqlite', if_exists='replace')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(3, 'C'), (4, 'D'), (5, 'E')])
clean_up(table_name)
# test if_exists='append'
- sql.write_frame(frame=df_if_exists_1, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_1, con=self.conn, name=table_name,
flavor='sqlite', if_exists='fail')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(1, 'A'), (2, 'B')])
- sql.write_frame(frame=df_if_exists_2, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_2, con=self.conn, name=table_name,
flavor='sqlite', if_exists='append')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')])
clean_up(table_name)
-class TestXMySQL(tm.TestCase):
+class TestXMySQL(MySQLMixIn, tm.TestCase):
@classmethod
def setUpClass(cls):
@@ -2307,14 +2316,14 @@ def setUp(self):
try:
# Try Travis defaults.
# No real user should allow root access with a blank password.
- self.db = pymysql.connect(host='localhost', user='root', passwd='',
+ self.conn = pymysql.connect(host='localhost', user='root', passwd='',
db='pandas_nosetest')
except:
pass
else:
return
try:
- self.db = pymysql.connect(read_default_group='pandas')
+ self.conn = pymysql.connect(read_default_group='pandas')
except pymysql.ProgrammingError as e:
raise nose.SkipTest(
"Create a group of connection parameters under the heading "
@@ -2327,12 +2336,6 @@ def setUp(self):
"[pandas] in your system's mysql default file, "
"typically located at ~/.my.cnf or /etc/.my.cnf. ")
- def tearDown(self):
- from pymysql.err import Error
- try:
- self.db.close()
- except Error:
- pass
def test_basic(self):
_skip_if_no_pymysql()
@@ -2346,7 +2349,7 @@ def test_write_row_by_row(self):
frame.ix[0, 0] = np.nan
drop_sql = "DROP TABLE IF EXISTS test"
create_sql = sql.get_schema(frame, 'test', 'mysql')
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
@@ -2354,9 +2357,9 @@ def test_write_row_by_row(self):
fmt_sql = format_query(ins, *row)
sql.tquery(fmt_sql, cur=cur)
- self.db.commit()
+ self.conn.commit()
- result = sql.read_frame("select * from test", con=self.db)
+ result = sql.read_frame("select * from test", con=self.conn)
result.index = frame.index
tm.assert_frame_equal(result, frame)
@@ -2365,7 +2368,7 @@ def test_execute(self):
frame = tm.makeTimeDataFrame()
drop_sql = "DROP TABLE IF EXISTS test"
create_sql = sql.get_schema(frame, 'test', 'mysql')
- cur = self.db.cursor()
+ cur = self.conn.cursor()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Unknown table.*")
cur.execute(drop_sql)
@@ -2373,10 +2376,10 @@ def test_execute(self):
ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
row = frame.ix[0].values.tolist()
- sql.execute(ins, self.db, params=tuple(row))
- self.db.commit()
+ sql.execute(ins, self.conn, params=tuple(row))
+ self.conn.commit()
- result = sql.read_frame("select * from test", self.db)
+ result = sql.read_frame("select * from test", self.conn)
result.index = frame.index[:1]
tm.assert_frame_equal(result, frame[:1])
@@ -2395,7 +2398,7 @@ def test_schema(self):
create_sql = sql.get_schema(frame, 'test', 'mysql', keys=['A', 'B'],)
lines = create_sql.splitlines()
self.assertTrue('PRIMARY KEY (`A`, `B`)' in create_sql)
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
@@ -2411,18 +2414,18 @@ def test_execute_fail(self):
PRIMARY KEY (a(5), b(5))
);
"""
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
- sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.db)
- sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)', self.db)
+ sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
+ sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)', self.conn)
try:
sys.stdout = StringIO()
self.assertRaises(Exception, sql.execute,
'INSERT INTO test VALUES("foo", "bar", 7)',
- self.db)
+ self.conn)
finally:
sys.stdout = sys.__stdout__
@@ -2438,19 +2441,23 @@ def test_execute_closed_connection(self):
PRIMARY KEY (a(5), b(5))
);
"""
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(drop_sql)
cur.execute(create_sql)
- sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.db)
- self.db.close()
+ sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)', self.conn)
+ self.conn.close()
try:
sys.stdout = StringIO()
self.assertRaises(Exception, sql.tquery, "select * from test",
- con=self.db)
+ con=self.conn)
finally:
sys.stdout = sys.__stdout__
+ # Initialize connection again (needed for tearDown)
+ self.setUp()
+
+
def test_na_roundtrip(self):
_skip_if_no_pymysql()
pass
@@ -2458,12 +2465,12 @@ def test_na_roundtrip(self):
def _check_roundtrip(self, frame):
_skip_if_no_pymysql()
drop_sql = "DROP TABLE IF EXISTS test_table"
- cur = self.db.cursor()
+ cur = self.conn.cursor()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Unknown table.*")
cur.execute(drop_sql)
- sql.write_frame(frame, name='test_table', con=self.db, flavor='mysql')
- result = sql.read_frame("select * from test_table", self.db)
+ sql.write_frame(frame, name='test_table', con=self.conn, flavor='mysql')
+ result = sql.read_frame("select * from test_table", self.conn)
# HACK! Change this once indexes are handled properly.
result.index = frame.index
@@ -2477,12 +2484,12 @@ def _check_roundtrip(self, frame):
index = Index(lrange(len(frame2))) + 10
frame2['Idx'] = index
drop_sql = "DROP TABLE IF EXISTS test_table2"
- cur = self.db.cursor()
+ cur = self.conn.cursor()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "Unknown table.*")
cur.execute(drop_sql)
- sql.write_frame(frame2, name='test_table2', con=self.db, flavor='mysql')
- result = sql.read_frame("select * from test_table2", self.db,
+ sql.write_frame(frame2, name='test_table2', con=self.conn, flavor='mysql')
+ result = sql.read_frame("select * from test_table2", self.conn,
index_col='Idx')
expected = frame.copy()
@@ -2498,10 +2505,10 @@ def test_tquery(self):
raise nose.SkipTest("no pymysql")
frame = tm.makeTimeDataFrame()
drop_sql = "DROP TABLE IF EXISTS test_table"
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(drop_sql)
- sql.write_frame(frame, name='test_table', con=self.db, flavor='mysql')
- result = sql.tquery("select A from test_table", self.db)
+ sql.write_frame(frame, name='test_table', con=self.conn, flavor='mysql')
+ result = sql.tquery("select A from test_table", self.conn)
expected = Series(frame.A.values, frame.index) # not to have name
result = Series(result, frame.index)
tm.assert_series_equal(result, expected)
@@ -2509,10 +2516,10 @@ def test_tquery(self):
try:
sys.stdout = StringIO()
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'select * from blah', con=self.db)
+ 'select * from blah', con=self.conn)
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'select * from blah', con=self.db, retry=True)
+ 'select * from blah', con=self.conn, retry=True)
finally:
sys.stdout = sys.__stdout__
@@ -2523,20 +2530,20 @@ def test_uquery(self):
raise nose.SkipTest("no pymysql")
frame = tm.makeTimeDataFrame()
drop_sql = "DROP TABLE IF EXISTS test_table"
- cur = self.db.cursor()
+ cur = self.conn.cursor()
cur.execute(drop_sql)
- sql.write_frame(frame, name='test_table', con=self.db, flavor='mysql')
+ sql.write_frame(frame, name='test_table', con=self.conn, flavor='mysql')
stmt = 'INSERT INTO test_table VALUES(2.314, -123.1, 1.234, 2.3)'
- self.assertEqual(sql.uquery(stmt, con=self.db), 1)
+ self.assertEqual(sql.uquery(stmt, con=self.conn), 1)
try:
sys.stdout = StringIO()
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'insert into blah values (1)', con=self.db)
+ 'insert into blah values (1)', con=self.conn)
self.assertRaises(sql.DatabaseError, sql.tquery,
- 'insert into blah values (1)', con=self.db,
+ 'insert into blah values (1)', con=self.conn,
retry=True)
finally:
sys.stdout = sys.__stdout__
@@ -2546,7 +2553,7 @@ def test_keyword_as_column_names(self):
'''
_skip_if_no_pymysql()
df = DataFrame({'From':np.ones(5)})
- sql.write_frame(df, con = self.db, name = 'testkeywords',
+ sql.write_frame(df, con = self.conn, name = 'testkeywords',
if_exists='replace', flavor='mysql')
def test_if_exists(self):
@@ -2561,51 +2568,48 @@ def clean_up(test_table_to_drop):
Drops tables created from individual tests
so no dependencies arise from sequential tests
"""
- if sql.table_exists(test_table_to_drop, self.db, flavor='mysql'):
- cur = self.db.cursor()
- cur.execute("DROP TABLE %s" % test_table_to_drop)
- cur.close()
+ self.drop_table(test_table_to_drop)
# test if invalid value for if_exists raises appropriate error
self.assertRaises(ValueError,
sql.write_frame,
frame=df_if_exists_1,
- con=self.db,
+ con=self.conn,
name=table_name,
flavor='mysql',
if_exists='notvalidvalue')
clean_up(table_name)
# test if_exists='fail'
- sql.write_frame(frame=df_if_exists_1, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_1, con=self.conn, name=table_name,
flavor='mysql', if_exists='fail')
self.assertRaises(ValueError,
sql.write_frame,
frame=df_if_exists_1,
- con=self.db,
+ con=self.conn,
name=table_name,
flavor='mysql',
if_exists='fail')
# test if_exists='replace'
- sql.write_frame(frame=df_if_exists_1, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_1, con=self.conn, name=table_name,
flavor='mysql', if_exists='replace')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(1, 'A'), (2, 'B')])
- sql.write_frame(frame=df_if_exists_2, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_2, con=self.conn, name=table_name,
flavor='mysql', if_exists='replace')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(3, 'C'), (4, 'D'), (5, 'E')])
clean_up(table_name)
# test if_exists='append'
- sql.write_frame(frame=df_if_exists_1, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_1, con=self.conn, name=table_name,
flavor='mysql', if_exists='fail')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(1, 'A'), (2, 'B')])
- sql.write_frame(frame=df_if_exists_2, con=self.db, name=table_name,
+ sql.write_frame(frame=df_if_exists_2, con=self.conn, name=table_name,
flavor='mysql', if_exists='append')
- self.assertEqual(sql.tquery(sql_select, con=self.db),
+ self.assertEqual(sql.tquery(sql_select, con=self.conn),
[(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')])
clean_up(table_name)
| Closes #10255 .
Rewrote some of the code in `test_sql.py` so that it now properly drops tables with names that need to be quoted.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10262 | 2015-06-03T19:38:14Z | 2015-07-10T23:17:52Z | 2015-07-10T23:17:52Z | 2015-07-10T23:17:58Z |
BUG: SparseSeries constructor ignores input data name | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index b571aab0b19a5..8e30e90087bcb 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -80,6 +80,6 @@ Bug Fixes
- Bug in GroupBy.get_group raises ValueError when group key contains NaT (:issue:`6992`)
-
+- Bug in ``SparseSeries`` constructor ignores input data name (:issue:`10258`)
- Bug where infer_freq infers timerule (WOM-5XXX) unsupported by to_offset (:issue:`9425`)
diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py
index f53cc66bee961..24d06970f4741 100644
--- a/pandas/sparse/series.py
+++ b/pandas/sparse/series.py
@@ -121,6 +121,9 @@ def __init__(self, data=None, index=None, sparse_index=None, kind='block',
if data is None:
data = []
+ if isinstance(data, Series) and name is None:
+ name = data.name
+
is_sparse_array = isinstance(data, SparseArray)
if fill_value is None:
if is_sparse_array:
diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py
index a7a78ba226a0b..96e5ff87fbb0c 100644
--- a/pandas/sparse/tests/test_sparse.py
+++ b/pandas/sparse/tests/test_sparse.py
@@ -128,14 +128,15 @@ def setUp(self):
date_index = bdate_range('1/1/2011', periods=len(index))
- self.bseries = SparseSeries(arr, index=index, kind='block')
- self.bseries.name = 'bseries'
+ self.bseries = SparseSeries(arr, index=index, kind='block',
+ name='bseries')
self.ts = self.bseries
self.btseries = SparseSeries(arr, index=date_index, kind='block')
- self.iseries = SparseSeries(arr, index=index, kind='integer')
+ self.iseries = SparseSeries(arr, index=index, kind='integer',
+ name='iseries')
arr, index = _test_data2()
self.bseries2 = SparseSeries(arr, index=index, kind='block')
@@ -143,7 +144,7 @@ def setUp(self):
arr, index = _test_data1_zero()
self.zbseries = SparseSeries(arr, index=index, kind='block',
- fill_value=0)
+ fill_value=0, name='zbseries')
self.ziseries = SparseSeries(arr, index=index, kind='integer',
fill_value=0)
@@ -234,12 +235,21 @@ def test_constructor(self):
self.bseries.to_dense().fillna(0).values)
# pass SparseSeries
- s2 = SparseSeries(self.bseries)
- s3 = SparseSeries(self.iseries)
- s4 = SparseSeries(self.zbseries)
- assert_sp_series_equal(s2, self.bseries)
- assert_sp_series_equal(s3, self.iseries)
- assert_sp_series_equal(s4, self.zbseries)
+ def _check_const(sparse, name):
+ # use passed series name
+ result = SparseSeries(sparse)
+ assert_sp_series_equal(result, sparse)
+ self.assertEqual(sparse.name, name)
+ self.assertEqual(result.name, name)
+
+ # use passed name
+ result = SparseSeries(sparse, name='x')
+ assert_sp_series_equal(result, sparse)
+ self.assertEqual(result.name, 'x')
+
+ _check_const(self.bseries, 'bseries')
+ _check_const(self.iseries, 'iseries')
+ _check_const(self.zbseries, 'zbseries')
# Sparse time series works
date_index = bdate_range('1/1/2000', periods=len(self.bseries))
| When constructing `Series` from `Series`, name attribute is preserved otherwise specified.
```
import pandas as pd
s = pd.Series([1, 2, 3], name='a')
pd.Series(s)
#0 1
#1 2
#2 3
# Name: a, dtype: int64
pd.Series(s, name='x')
#0 1
#1 2
#2 3
# Name: x, dtype: int64
```
But `SparseSeries` doesn't preserve its name.
```
s = pd.SparseSeries([1, 2, 3], name='a')
pd.SparseSeries(s)
#0 1
#1 2
#2 3
# dtype: int64
# BlockIndex
# Block locations: array([0], dtype=int32)
# Block lengths: array([3], dtype=int32)
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10258 | 2015-06-03T12:07:26Z | 2015-06-03T21:21:22Z | 2015-06-03T21:21:22Z | 2015-06-04T13:16:14Z |
add numba example to enhancingperf.rst | diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst
index d007446a5b922..54fd0a2131861 100644
--- a/doc/source/enhancingperf.rst
+++ b/doc/source/enhancingperf.rst
@@ -7,7 +7,7 @@
import os
import csv
- from pandas import DataFrame
+ from pandas import DataFrame, Series
import pandas as pd
pd.options.display.max_rows=15
@@ -68,9 +68,10 @@ Here's the function in pure python:
We achieve our result by using ``apply`` (row-wise):
-.. ipython:: python
+.. code-block:: python
- %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)
+ In [7]: %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)
+ 10 loops, best of 3: 174 ms per loop
But clearly this isn't fast enough for us. Let's take a look and see where the
time is spent during this operation (limited to the most time consuming
@@ -97,7 +98,7 @@ First we're going to need to import the cython magic function to ipython:
.. ipython:: python
- %load_ext cythonmagic
+ %load_ext Cython
Now, let's simply copy our functions over to cython as is (the suffix
@@ -122,9 +123,10 @@ is here to distinguish between function versions):
to be using bleeding edge ipython for paste to play well with cell magics.
-.. ipython:: python
+.. code-block:: python
- %timeit df.apply(lambda x: integrate_f_plain(x['a'], x['b'], x['N']), axis=1)
+ In [4]: %timeit df.apply(lambda x: integrate_f_plain(x['a'], x['b'], x['N']), axis=1)
+ 10 loops, best of 3: 85.5 ms per loop
Already this has shaved a third off, not too bad for a simple copy and paste.
@@ -150,9 +152,10 @@ We get another huge improvement simply by providing type information:
...: return s * dx
...:
-.. ipython:: python
+.. code-block:: python
- %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)
+ In [4]: %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)
+ 10 loops, best of 3: 20.3 ms per loop
Now, we're talking! It's now over ten times faster than the original python
implementation, and we haven't *really* modified the code. Let's have another
@@ -229,9 +232,10 @@ the rows, applying our ``integrate_f_typed``, and putting this in the zeros arra
Loops like this would be *extremely* slow in python, but in Cython looping
over numpy arrays is *fast*.
-.. ipython:: python
+.. code-block:: python
- %timeit apply_integrate_f(df['a'].values, df['b'].values, df['N'].values)
+ In [4]: %timeit apply_integrate_f(df['a'].values, df['b'].values, df['N'].values)
+ 1000 loops, best of 3: 1.25 ms per loop
We've gotten another big improvement. Let's check again where the time is spent:
@@ -278,20 +282,70 @@ advanced cython techniques:
...: return res
...:
-.. ipython:: python
+.. code-block:: python
- %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values)
+ In [4]: %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values)
+ 1000 loops, best of 3: 987 us per loop
Even faster, with the caveat that a bug in our cython code (an off-by-one error,
for example) might cause a segfault because memory access isn't checked.
-Further topics
-~~~~~~~~~~~~~~
+.. _enhancingperf.numba:
+
+Using numba
+-----------
+
+A recent alternative to statically compiling cython code, is to use a *dynamic jit-compiler*, ``numba``.
+
+Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.
+
+Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack.
+
+.. note::
+
+ You will need to install ``numba``. This is easy with ``conda``, by using: ``conda install numba``, see :ref:`installing using miniconda<install.miniconda>`.
+
+We simply take the plain python code from above and annotate with the ``@jit`` decorator.
+
+.. code-block:: python
+
+ import numba
+
+ @numba.jit
+ def f_plain(x):
+ return x * (x - 1)
+
+ @numba.jit
+ def integrate_f_numba(a, b, N):
+ s = 0
+ dx = (b - a) / N
+ for i in range(N):
+ s += f_plain(a + i * dx)
+ return s * dx
+
+ @numba.jit
+ def apply_integrate_f_numba(col_a, col_b, col_N):
+ n = len(col_N)
+ result = np.empty(n, dtype='float64')
+ assert len(col_a) == len(col_b) == n
+ for i in range(n):
+ result[i] = integrate_f_numba(col_a[i], col_b[i], col_N[i])
+ return result
+
+ def compute_numba(df):
+ result = apply_integrate_f_numba(df['a'].values, df['b'].values, df['N'].values)
+ return Series(result, index=df.index, name='result')
+
+Similar to above, we directly pass ``numpy`` arrays directly to the numba function. Further
+we are wrapping the results to provide a nice interface by passing/returning pandas objects.
+
+.. code-block:: python
-- Loading C modules into cython.
+ In [4]: %timeit compute_numba(df)
+ 1000 loops, best of 3: 798 us per loop
-Read more in the `cython docs <http://docs.cython.org/>`__.
+Read more in the `numba docs <http://numba.pydata.org/>`__.
.. _enhancingperf.eval:
diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index b571aab0b19a5..aede460766922 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -9,6 +9,8 @@ We recommend that all users upgrade to this version.
Highlights include:
+- Documentation on how to use ``numba`` with *pandas*, see :ref:`here <enhancingperf.numba>`
+
Check the :ref:`API Changes <whatsnew_0162.api>` before updating.
.. contents:: What's new in v0.16.2
| 
| https://api.github.com/repos/pandas-dev/pandas/pulls/10257 | 2015-06-03T11:57:09Z | 2015-06-03T19:10:10Z | 2015-06-03T19:10:10Z | 2015-06-03T19:10:23Z |
ENH: Add pipe method | diff --git a/doc/source/basics.rst b/doc/source/basics.rst
index d16feb3a6c448..349e7e25fdafb 100644
--- a/doc/source/basics.rst
+++ b/doc/source/basics.rst
@@ -624,6 +624,77 @@ We can also pass infinite values to define the bins:
Function application
--------------------
+To apply your own or another library's functions to pandas objects,
+you should be aware of the three methods below. The appropriate
+method to use depends on whether your function expects to operate
+on an entire ``DataFrame`` or ``Series``, row- or column-wise, or elementwise.
+
+1. `Tablewise Function Application`_: :meth:`~DataFrame.pipe`
+2. `Row or Column-wise Function Application`_: :meth:`~DataFrame.apply`
+3. Elementwise_ function application: :meth:`~DataFrame.applymap`
+
+.. _basics.pipe:
+
+Tablewise Function Application
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. versionadded:: 0.16.2
+
+``DataFrames`` and ``Series`` can of course just be passed into functions.
+However, if the function needs to be called in a chain, consider using the :meth:`~DataFrame.pipe` method.
+Compare the following
+
+.. code-block:: python
+
+ # f, g, and h are functions taking and returning ``DataFrames``
+ >>> f(g(h(df), arg1=1), arg2=2, arg3=3)
+
+with the equivalent
+
+.. code-block:: python
+
+ >>> (df.pipe(h)
+ .pipe(g, arg1=1)
+ .pipe(f, arg2=2, arg3=3)
+ )
+
+Pandas encourages the second style, which is known as method chaining.
+``pipe`` makes it easy to use your own or another library's functions
+in method chains, alongside pandas' methods.
+
+In the example above, the functions ``f``, ``g``, and ``h`` each expected the ``DataFrame`` as the first positional argument.
+What if the function you wish to apply takes its data as, say, the second argument?
+In this case, provide ``pipe`` with a tuple of ``(callable, data_keyword)``.
+``.pipe`` will route the ``DataFrame`` to the argument specified in the tuple.
+
+For example, we can fit a regression using statsmodels. Their API expects a formula first and a ``DataFrame`` as the second argument, ``data``. We pass in the function, keyword pair ``(sm.poisson, 'data')`` to ``pipe``:
+
+.. ipython:: python
+
+ import statsmodels.formula.api as sm
+
+ bb = pd.read_csv('data/baseball.csv', index_col='id')
+
+ (bb.query('h > 0')
+ .assign(ln_h = lambda df: np.log(df.h))
+ .pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)')
+ .fit()
+ .summary()
+ )
+
+The pipe method is inspired by unix pipes and more recently dplyr_ and magrittr_, which
+have introduced the popular ``(%>%)`` (read pipe) operator for R_.
+The implementation of ``pipe`` here is quite clean and feels right at home in python.
+We encourage you to view the source code (``pd.DataFrame.pipe??`` in IPython).
+
+.. _dplyr: https://github.com/hadley/dplyr
+.. _magrittr: https://github.com/smbache/magrittr
+.. _R: http://www.r-project.org
+
+
+Row or Column-wise Function Application
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
Arbitrary functions can be applied along the axes of a DataFrame or Panel
using the :meth:`~DataFrame.apply` method, which, like the descriptive
statistics methods, take an optional ``axis`` argument:
@@ -678,6 +749,7 @@ Series operation on each column or row:
tsdf
tsdf.apply(pd.Series.interpolate)
+
Finally, :meth:`~DataFrame.apply` takes an argument ``raw`` which is False by default, which
converts each row or column into a Series before applying the function. When
set to True, the passed function will instead receive an ndarray object, which
@@ -690,6 +762,8 @@ functionality.
functionality for grouping by some criterion, applying, and combining the
results into a Series, DataFrame, etc.
+.. _Elementwise:
+
Applying elementwise Python functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/doc/source/faq.rst b/doc/source/faq.rst
index 1fc8488e92fde..32290839ad71d 100644
--- a/doc/source/faq.rst
+++ b/doc/source/faq.rst
@@ -89,46 +89,6 @@ representation; i.e., 1KB = 1024 bytes).
See also :ref:`Categorical Memory Usage <categorical.memory>`.
-.. _ref-monkey-patching:
-
-Adding Features to your pandas Installation
--------------------------------------------
-
-pandas is a powerful tool and already has a plethora of data manipulation
-operations implemented, most of them are very fast as well.
-It's very possible however that certain functionality that would make your
-life easier is missing. In that case you have several options:
-
-1) Open an issue on `Github <https://github.com/pydata/pandas/issues/>`__ , explain your need and the sort of functionality you would like to see implemented.
-2) Fork the repo, Implement the functionality yourself and open a PR
- on Github.
-3) Write a method that performs the operation you are interested in and
- Monkey-patch the pandas class as part of your IPython profile startup
- or PYTHONSTARTUP file.
-
- For example, here is an example of adding an ``just_foo_cols()``
- method to the dataframe class:
-
-::
-
- import pandas as pd
- def just_foo_cols(self):
- """Get a list of column names containing the string 'foo'
-
- """
- return [x for x in self.columns if 'foo' in x]
-
- pd.DataFrame.just_foo_cols = just_foo_cols # monkey-patch the DataFrame class
- df = pd.DataFrame([list(range(4))], columns=["A","foo","foozball","bar"])
- df.just_foo_cols()
- del pd.DataFrame.just_foo_cols # you can also remove the new method
-
-
-Monkey-patching is usually frowned upon because it makes your code
-less portable and can cause subtle bugs in some circumstances.
-Monkey-patching existing methods is usually a bad idea in that respect.
-When used with proper care, however, it's a very useful tool to have.
-
.. _ref-scikits-migration:
diff --git a/doc/source/internals.rst b/doc/source/internals.rst
index 17be04cd64d27..8b4f7360fc235 100644
--- a/doc/source/internals.rst
+++ b/doc/source/internals.rst
@@ -101,7 +101,7 @@ Subclassing pandas Data Structures
.. warning:: There are some easier alternatives before considering subclassing ``pandas`` data structures.
- 1. Monkey-patching: See :ref:`Adding Features to your pandas Installation <ref-monkey-patching>`.
+ 1. Extensible method chains with :ref:`pipe <basics.pipe>`
2. Use *composition*. See `here <http://en.wikipedia.org/wiki/Composition_over_inheritance>`_.
diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index 627c79f7289b7..9421ab0f841ac 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -10,6 +10,7 @@ We recommend that all users upgrade to this version.
Highlights include:
- Documentation on how to use ``numba`` with *pandas*, see :ref:`here <enhancingperf.numba>`
+- A new ``pipe`` method, see :ref:`here <whatsnew_0162.enhancements.pipe>`
Check the :ref:`API Changes <whatsnew_0162.api>` before updating.
@@ -22,6 +23,62 @@ Check the :ref:`API Changes <whatsnew_0162.api>` before updating.
New features
~~~~~~~~~~~~
+.. _whatsnew_0162.enhancements.pipe:
+
+Pipe
+^^^^
+
+We've introduced a new method :meth:`DataFrame.pipe`. As suggested by the name, ``pipe``
+should be used to pipe data through a chain of function calls.
+The goal is to avoid confusing nested function calls like
+
+ .. code-block:: python
+
+ # df is a DataFrame
+ # f, g, and h are functions that take and return DataFrames
+ f(g(h(df), arg1=1), arg2=2, arg3=3)
+
+The logic flows from inside out, and function names are separated from their keyword arguments.
+This can be rewritten as
+
+ .. code-block:: python
+
+ (df.pipe(h)
+ .pipe(g, arg1=1)
+ .pipe(f, arg2=2)
+ )
+
+Now both the code and the logic flow from top to bottom. Keyword arguments are next to
+their functions. Overall the code is much more readable.
+
+In the example above, the functions ``f``, ``g``, and ``h`` each expected the DataFrame as the first positional argument.
+When the function you wish to apply takes its data anywhere other than the first argument, pass a tuple
+of ``(function, keyword)`` indicating where the DataFrame should flow. For example:
+
+.. ipython:: python
+
+ import statsmodels.formula.api as sm
+
+ bb = pd.read_csv('data/baseball.csv', index_col='id')
+
+ # sm.poisson takes (formula, data)
+ (bb.query('h > 0')
+ .assign(ln_h = lambda df: np.log(df.h))
+ .pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)')
+ .fit()
+ .summary()
+ )
+
+The pipe method is inspired by unix pipes, which stream text through
+processes. More recently dplyr_ and magrittr_ have introduced the
+popular ``(%>%)`` pipe operator for R_.
+
+See the :ref:`documentation <basics.pipe>` for more. (:issue:`10129`)
+
+.. _dplyr: https://github.com/hadley/dplyr
+.. _magrittr: https://github.com/smbache/magrittr
+.. _R: http://www.r-project.org
+
.. _whatsnew_0162.enhancements.other:
Other enhancements
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 87a9d197bd0d1..164ab73def894 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -21,6 +21,7 @@ Check the :ref:`API Changes <whatsnew_0170.api>` and :ref:`deprecations <whatsne
New features
~~~~~~~~~~~~
+
.. _whatsnew_0170.enhancements.other:
Other enhancements
diff --git a/pandas/__init__.py b/pandas/__init__.py
index 2a142a6ff2072..0e7bc628fdb6a 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -57,4 +57,3 @@
from pandas.util.print_versions import show_versions
import pandas.util.testing
-
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 3bf90aaf71849..0b6476950333e 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -2045,6 +2045,68 @@ def sample(self, n=None, frac=None, replace=False, weights=None, random_state=No
locs = rs.choice(axis_length, size=n, replace=replace, p=weights)
return self.take(locs, axis=axis)
+ _shared_docs['pipe'] = ("""
+ Apply func(self, *args, **kwargs)
+
+ .. versionadded:: 0.16.2
+
+ Parameters
+ ----------
+ func : function
+ function to apply to the %(klass)s.
+ ``args``, and ``kwargs`` are passed into ``func``.
+ Alternatively a ``(callable, data_keyword)`` tuple where
+ ``data_keyword`` is a string indicating the keyword of
+ ``callable`` that expects the %(klass)s.
+ args : positional arguments passed into ``func``.
+ kwargs : a dictionary of keyword arguments passed into ``func``.
+
+ Returns
+ -------
+ object : the return type of ``func``.
+
+ Notes
+ -----
+
+ Use ``.pipe`` when chaining together functions that expect
+ on Series or DataFrames. Instead of writing
+
+ >>> f(g(h(df), arg1=a), arg2=b, arg3=c)
+
+ You can write
+
+ >>> (df.pipe(h)
+ ... .pipe(g, arg1=a)
+ ... .pipe(f, arg2=b, arg3=c)
+ ... )
+
+ If you have a function that takes the data as (say) the second
+ argument, pass a tuple indicating which keyword expects the
+ data. For example, suppose ``f`` takes its data as ``arg2``:
+
+ >>> (df.pipe(h)
+ ... .pipe(g, arg1=a)
+ ... .pipe((f, 'arg2'), arg1=a, arg3=c)
+ ... )
+
+ See Also
+ --------
+ pandas.DataFrame.apply
+ pandas.DataFrame.applymap
+ pandas.Series.map
+ """
+ )
+ @Appender(_shared_docs['pipe'] % _shared_doc_kwargs)
+ def pipe(self, func, *args, **kwargs):
+ if isinstance(func, tuple):
+ func, target = func
+ if target in kwargs:
+ msg = '%s is both the pipe target and a keyword argument' % target
+ raise ValueError(msg)
+ kwargs[target] = self
+ return func(*args, **kwargs)
+ else:
+ return func(self, *args, **kwargs)
#----------------------------------------------------------------------
# Attribute access
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index a03fe3c2241a3..44f7791b7f8ba 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -1649,6 +1649,48 @@ def test_describe_raises(self):
with tm.assertRaises(NotImplementedError):
tm.makePanel().describe()
+ def test_pipe(self):
+ df = DataFrame({'A': [1, 2, 3]})
+ f = lambda x, y: x ** y
+ result = df.pipe(f, 2)
+ expected = DataFrame({'A': [1, 4, 9]})
+ self.assert_frame_equal(result, expected)
+
+ result = df.A.pipe(f, 2)
+ self.assert_series_equal(result, expected.A)
+
+ def test_pipe_tuple(self):
+ df = DataFrame({'A': [1, 2, 3]})
+ f = lambda x, y: y
+ result = df.pipe((f, 'y'), 0)
+ self.assert_frame_equal(result, df)
+
+ result = df.A.pipe((f, 'y'), 0)
+ self.assert_series_equal(result, df.A)
+
+ def test_pipe_tuple_error(self):
+ df = DataFrame({"A": [1, 2, 3]})
+ f = lambda x, y: y
+ with tm.assertRaises(ValueError):
+ result = df.pipe((f, 'y'), x=1, y=0)
+
+ with tm.assertRaises(ValueError):
+ result = df.A.pipe((f, 'y'), x=1, y=0)
+
+ def test_pipe_panel(self):
+ wp = Panel({'r1': DataFrame({"A": [1, 2, 3]})})
+ f = lambda x, y: x + y
+ result = wp.pipe(f, 2)
+ expected = wp + 2
+ assert_panel_equal(result, expected)
+
+ result = wp.pipe((f, 'y'), x=1)
+ expected = wp + 1
+ assert_panel_equal(result, expected)
+
+ with tm.assertRaises(ValueError):
+ result = wp.pipe((f, 'y'), x=1, y=1)
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| Closes https://github.com/pydata/pandas/issues/10129
In the dev meeting, we settled on the following:
- `.pipe` will **not** include a check for `__pipe_func__` on the function passed in.
- To avoid messiness with lambdas when a function takes the DataFrame other than in the first position, users can pass in a `(callable, data_keyword)` argument to `.pipe` (thanks @mwaskom)
``` python
import statsmodels.formula.api as sm
bb = pd.read_csv('data/baseball.csv', index_col='id')
(bb.query('h > 0')
.assign(ln_h = lambda df: np.log(df.h))
# sm.possion expects `formula, data`
.pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)')
.fit()
.summary()
)
## -- End pasted text --
Optimization terminated successfully.
Current function value: 2.116284
Iterations 24
Out[1]:
<class 'statsmodels.iolib.summary.Summary'>
"""
Poisson Regression Results
==============================================================================
Dep. Variable: hr No. Observations: 68
Model: Poisson Df Residuals: 63
Method: MLE Df Model: 4
Date: Tue, 02 Jun 2015 Pseudo R-squ.: 0.6878
Time: 20:57:27 Log-Likelihood: -143.91
converged: True LL-Null: -460.91
LLR p-value: 6.774e-136
===============================================================================
coef std err z P>|z| [95.0% Conf. Int.]
-------------------------------------------------------------------------------
Intercept -1267.3636 457.867 -2.768 0.006 -2164.767 -369.960
C(lg)[T.NL] -0.2057 0.101 -2.044 0.041 -0.403 -0.008
ln_h 0.9280 0.191 4.866 0.000 0.554 1.302
year 0.6301 0.228 2.762 0.006 0.183 1.077
g 0.0099 0.004 2.754 0.006 0.003 0.017
===============================================================================
"""
```
Thanks everyone for the input in the issue.
---
This is mostly ready. What's a good name for the argument to `pipe`? I have `func` right now, @shoyer you had `target` in the issue thread. I've been using `target` as the keyword expecting the data, i.e. where the DataFrame should be pipe to.
The tests are extremely minimal... but so is the implementation. Am I missing any obvious edge-cases?
We'll see how this goes. I don't think I push `.pipe` as a protocol at all in the documentation, though we can change that in the future. We should be forwards-compatible if we do ever go down the `__pipe_func__` route.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10253 | 2015-06-03T02:26:03Z | 2015-06-06T03:14:27Z | 2015-06-06T03:14:27Z | 2015-08-19T17:19:06Z |
ENH: make sure return dtypes for nan funcs are consistent | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index ddfe6fa0b2f74..c59f2317431d5 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -57,7 +57,7 @@ Bug Fixes
- Bug where read_hdf store.select modifies the passed columns list when
multi-indexed (:issue:`7212`)
- Bug in ``Categorical`` repr with ``display.width`` of ``None`` in Python 3 (:issue:`10087`)
-
+- Bug where some of the nan funcs do not have consistent return dtypes (:issue:`10251`)
- Bug in groupby.apply aggregation for Categorical not preserving categories (:issue:`10138`)
- Bug in ``mean()`` where integer dtypes can overflow (:issue:`10172`)
- Bug where Panel.from_dict does not set dtype when specified (:issue:`10058`)
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index c64c50f791edf..c70fb6339517d 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -244,7 +244,10 @@ def nanall(values, axis=None, skipna=True):
@bottleneck_switch(zero_value=0)
def nansum(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
- the_sum = values.sum(axis, dtype=dtype_max)
+ dtype_sum = dtype_max
+ if is_float_dtype(dtype):
+ dtype_sum = dtype
+ the_sum = values.sum(axis, dtype=dtype_sum)
the_sum = _maybe_null_out(the_sum, axis, mask)
return _wrap_results(the_sum, dtype)
@@ -288,7 +291,7 @@ def get_median(x):
return np.nan
return algos.median(_values_from_object(x[mask]))
- if values.dtype != np.float64:
+ if not is_float_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
@@ -317,10 +320,10 @@ def get_median(x):
return _wrap_results(get_median(values) if notempty else np.nan, dtype)
-def _get_counts_nanvar(mask, axis, ddof):
- count = _get_counts(mask, axis)
-
- d = count-ddof
+def _get_counts_nanvar(mask, axis, ddof, dtype=float):
+ dtype = _get_dtype(dtype)
+ count = _get_counts(mask, axis, dtype=dtype)
+ d = count - dtype.type(ddof)
# always return NaN, never inf
if np.isscalar(count):
@@ -341,7 +344,10 @@ def _nanvar(values, axis=None, skipna=True, ddof=1):
if is_any_int_dtype(values):
values = values.astype('f8')
- count, d = _get_counts_nanvar(mask, axis, ddof)
+ if is_float_dtype(values):
+ count, d = _get_counts_nanvar(mask, axis, ddof, values.dtype)
+ else:
+ count, d = _get_counts_nanvar(mask, axis, ddof)
if skipna:
values = values.copy()
@@ -349,7 +355,8 @@ def _nanvar(values, axis=None, skipna=True, ddof=1):
X = _ensure_numeric(values.sum(axis))
XX = _ensure_numeric((values ** 2).sum(axis))
- return np.fabs((XX - X ** 2 / count) / d)
+ result = np.fabs((XX - X * X / count) / d)
+ return result
@disallow('M8')
@bottleneck_switch(ddof=1)
@@ -375,9 +382,9 @@ def nansem(values, axis=None, skipna=True, ddof=1):
mask = isnull(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
- count, _ = _get_counts_nanvar(mask, axis, ddof)
+ count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)
- return np.sqrt(var)/np.sqrt(count)
+ return np.sqrt(var) / np.sqrt(count)
@bottleneck_switch()
@@ -469,23 +476,25 @@ def nanskew(values, axis=None, skipna=True):
mask = isnull(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
-
- count = _get_counts(mask, axis)
+ count = _get_counts(mask, axis)
+ else:
+ count = _get_counts(mask, axis, dtype=values.dtype)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
+ typ = values.dtype.type
A = values.sum(axis) / count
- B = (values ** 2).sum(axis) / count - A ** 2
- C = (values ** 3).sum(axis) / count - A ** 3 - 3 * A * B
+ B = (values ** 2).sum(axis) / count - A ** typ(2)
+ C = (values ** 3).sum(axis) / count - A ** typ(3) - typ(3) * A * B
# floating point error
B = _zero_out_fperr(B)
C = _zero_out_fperr(C)
- result = ((np.sqrt((count ** 2 - count)) * C) /
- ((count - 2) * np.sqrt(B) ** 3))
+ result = ((np.sqrt(count * count - count) * C) /
+ ((count - typ(2)) * np.sqrt(B) ** typ(3)))
if isinstance(result, np.ndarray):
result = np.where(B == 0, 0, result)
@@ -504,17 +513,19 @@ def nankurt(values, axis=None, skipna=True):
mask = isnull(values)
if not is_float_dtype(values.dtype):
values = values.astype('f8')
-
- count = _get_counts(mask, axis)
+ count = _get_counts(mask, axis)
+ else:
+ count = _get_counts(mask, axis, dtype=values.dtype)
if skipna:
values = values.copy()
np.putmask(values, mask, 0)
+ typ = values.dtype.type
A = values.sum(axis) / count
- B = (values ** 2).sum(axis) / count - A ** 2
- C = (values ** 3).sum(axis) / count - A ** 3 - 3 * A * B
- D = (values ** 4).sum(axis) / count - A ** 4 - 6 * B * A * A - 4 * C * A
+ B = (values ** 2).sum(axis) / count - A ** typ(2)
+ C = (values ** 3).sum(axis) / count - A ** typ(3) - typ(3) * A * B
+ D = (values ** 4).sum(axis) / count - A ** typ(4) - typ(6) * B * A * A - typ(4) * C * A
B = _zero_out_fperr(B)
D = _zero_out_fperr(D)
@@ -526,8 +537,8 @@ def nankurt(values, axis=None, skipna=True):
if B == 0:
return 0
- result = (((count * count - 1.) * D / (B * B) - 3 * ((count - 1.) ** 2)) /
- ((count - 2.) * (count - 3.)))
+ result = (((count * count - typ(1)) * D / (B * B) - typ(3) * ((count - typ(1)) ** typ(2))) /
+ ((count - typ(2)) * (count - typ(3))))
if isinstance(result, np.ndarray):
result = np.where(B == 0, 0, result)
@@ -598,7 +609,7 @@ def _zero_out_fperr(arg):
if isinstance(arg, np.ndarray):
return np.where(np.abs(arg) < 1e-14, 0, arg)
else:
- return 0 if np.abs(arg) < 1e-14 else arg
+ return arg.dtype.type(0) if np.abs(arg) < 1e-14 else arg
@disallow('M8','m8')
diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index 1adb8a5d9217c..951a693d22280 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -4,7 +4,7 @@
from functools import partial
import numpy as np
-
+from pandas import Series
from pandas.core.common import isnull, is_integer_dtype
import pandas.core.nanops as nanops
import pandas.util.testing as tm
@@ -327,7 +327,6 @@ def test_nanmean_overflow(self):
# GH 10155
# In the previous implementation mean can overflow for int dtypes, it
# is now consistent with numpy
- from pandas import Series
# numpy < 1.9.0 is not computing this correctly
from distutils.version import LooseVersion
@@ -340,14 +339,19 @@ def test_nanmean_overflow(self):
self.assertEqual(result, np_result)
self.assertTrue(result.dtype == np.float64)
- # check returned dtype
- for dtype in [np.int16, np.int32, np.int64, np.float16, np.float32, np.float64]:
+ def test_returned_dtype(self):
+ for dtype in [np.int16, np.int32, np.int64, np.float32, np.float64, np.float128]:
s = Series(range(10), dtype=dtype)
- result = s.mean()
- if is_integer_dtype(dtype):
- self.assertTrue(result.dtype == np.float64)
- else:
- self.assertTrue(result.dtype == dtype)
+ group_a = ['mean', 'std', 'var', 'skew', 'kurt']
+ group_b = ['min', 'max']
+ for method in group_a + group_b:
+ result = getattr(s, method)()
+ if is_integer_dtype(dtype) and method in group_a:
+ self.assertTrue(result.dtype == np.float64,
+ "return dtype expected from %s is np.float64, got %s instead" % (method, result.dtype))
+ else:
+ self.assertTrue(result.dtype == dtype,
+ "return dtype expected from %s is %s, got %s instead" % (method, dtype, result.dtype))
def test_nanmedian(self):
self.check_funs(nanops.nanmedian, np.median,
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index eb583f17f3ace..b2591c7537ad1 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -528,7 +528,6 @@ def test_nansum_buglet(self):
assert_almost_equal(result, 1)
def test_overflow(self):
-
# GH 6915
# overflowing on the smaller int dtypes
for dtype in ['int32','int64']:
@@ -551,25 +550,25 @@ def test_overflow(self):
result = s.max()
self.assertEqual(int(result),v[-1])
- for dtype in ['float32','float64']:
- v = np.arange(5000000,dtype=dtype)
+ for dtype in ['float32', 'float64']:
+ v = np.arange(5000000, dtype=dtype)
s = Series(v)
# no bottleneck
result = s.sum(skipna=False)
- self.assertTrue(np.allclose(float(result),v.sum(dtype='float64')))
+ self.assertEqual(result, v.sum(dtype=dtype))
result = s.min(skipna=False)
- self.assertTrue(np.allclose(float(result),0.0))
+ self.assertTrue(np.allclose(float(result), 0.0))
result = s.max(skipna=False)
- self.assertTrue(np.allclose(float(result),v[-1]))
+ self.assertTrue(np.allclose(float(result), v[-1]))
# use bottleneck if available
result = s.sum()
- self.assertTrue(np.allclose(float(result),v.sum(dtype='float64')))
+ self.assertEqual(result, v.sum(dtype=dtype))
result = s.min()
- self.assertTrue(np.allclose(float(result),0.0))
+ self.assertTrue(np.allclose(float(result), 0.0))
result = s.max()
- self.assertTrue(np.allclose(float(result),v[-1]))
+ self.assertTrue(np.allclose(float(result), v[-1]))
class SafeForSparse(object):
pass
| this is a follow-up to https://github.com/pydata/pandas/pull/10172
In addition to `mean()`, this PR also makes sure the returned dtype is consistent for `std()`, `var()`, `skew()`, and `kurt()`
| https://api.github.com/repos/pandas-dev/pandas/pulls/10251 | 2015-06-02T17:32:47Z | 2015-06-05T21:28:59Z | 2015-06-05T21:28:59Z | 2015-06-05T21:29:03Z |
ENH: Conditional HTML Formatting | diff --git a/ci/requirements-2.6.run b/ci/requirements-2.6.run
index 5f8a2fde1409f..32d71beb24388 100644
--- a/ci/requirements-2.6.run
+++ b/ci/requirements-2.6.run
@@ -14,3 +14,4 @@ psycopg2=2.5.1
pymysql=0.6.0
sqlalchemy=0.7.8
xlsxwriter=0.4.6
+jinja2=2.8
diff --git a/ci/requirements-2.7.run b/ci/requirements-2.7.run
index 10049179912da..8fc074b96e0e4 100644
--- a/ci/requirements-2.7.run
+++ b/ci/requirements-2.7.run
@@ -19,3 +19,4 @@ pymysql=0.6.3
html5lib=1.0b2
beautiful-soup=4.2.1
statsmodels
+jinja2=2.8
diff --git a/ci/requirements-3.3.run b/ci/requirements-3.3.run
index 0256802a69eba..2379ab42391db 100644
--- a/ci/requirements-3.3.run
+++ b/ci/requirements-3.3.run
@@ -14,3 +14,4 @@ lxml=3.2.1
scipy
beautiful-soup=4.2.1
statsmodels
+jinja2=2.8
diff --git a/ci/requirements-3.4.run b/ci/requirements-3.4.run
index 45d082022713e..902a2984d4b3d 100644
--- a/ci/requirements-3.4.run
+++ b/ci/requirements-3.4.run
@@ -16,3 +16,4 @@ sqlalchemy
bottleneck
pymysql=0.6.3
psycopg2
+jinja2=2.8
diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run
index 1eca130ecd96a..f0101d34204a3 100644
--- a/ci/requirements-3.4_SLOW.run
+++ b/ci/requirements-3.4_SLOW.run
@@ -18,3 +18,4 @@ bottleneck
pymysql
psycopg2
statsmodels
+jinja2=2.8
diff --git a/ci/requirements-3.5.run b/ci/requirements-3.5.run
index 8de8f7d8f0630..64ed11b744ffd 100644
--- a/ci/requirements-3.5.run
+++ b/ci/requirements-3.5.run
@@ -12,6 +12,7 @@ pytables
html5lib
lxml
matplotlib
+jinja2
# currently causing some warnings
#sqlalchemy
diff --git a/doc/source/api.rst b/doc/source/api.rst
index 6bee0a1ceafb8..668e297dc8f18 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -1662,6 +1662,56 @@ The following methods are available only for ``DataFrameGroupBy`` objects.
DataFrameGroupBy.corrwith
DataFrameGroupBy.boxplot
+Style
+-----
+.. currentmodule:: pandas.core.style
+
+``Styler`` objects are returned by :attr:`pandas.DataFrame.style`.
+
+
+Constructor
+~~~~~~~~~~~
+.. autosummary::
+ :toctree: generated/
+
+ Styler
+
+Style Application
+~~~~~~~~~~~~~~~~~
+.. autosummary::
+ :toctree: generated/
+
+ Styler.apply
+ Styler.applymap
+ Styler.set_precision
+ Styler.set_table_styles
+ Styler.set_caption
+ Styler.set_properties
+ Styler.set_uuid
+ Styler.clear
+
+Builtin Styles
+~~~~~~~~~~~~~~
+
+.. autosummary::
+ :toctree: generated/
+
+ Styler.highlight_max
+ Styler.highlight_min
+ Styler.highlight_null
+ Styler.background_gradient
+ Styler.bar
+
+Style Export and Import
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+ :toctree: generated/
+
+ Styler.render
+ Styler.export
+ Styler.set
+
.. currentmodule:: pandas
General utility functions
diff --git a/doc/source/html-styling.html b/doc/source/html-styling.html
new file mode 100644
index 0000000000000..e595cffc751a8
--- /dev/null
+++ b/doc/source/html-styling.html
@@ -0,0 +1,21492 @@
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8" />
+<title>html-styling</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+ /*!
+*
+* Twitter Bootstrap
+*
+*//*! normalize.css v3.0.2 | MIT License | git.io/normalize */html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%;font-size:10px;-webkit-tap-highlight-color:transparent}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,optgroup,strong{font-weight:700}dfn{font-style:italic}h1{font-size:2em;margin:.67em 0}mark{background:#ff0;color:#000}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0;vertical-align:middle}svg:not(:root){overflow:hidden}hr{-moz-box-sizing:content-box;box-sizing:content-box;height:0}pre,textarea{overflow:auto}code,kbd,pre,samp{font-size:1em}button,input,optgroup,select,textarea{color:inherit;font:inherit;margin:0}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}input{line-height:normal}input[type=checkbox],input[type=radio]{box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}/*! 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+ </style>
+<style type="text/css">
+ /* Experimental typographically tinkered IJulia stylesheet
+ * Copyright © 2013--2015 Jiahao Chen <jiahao@mit.edu>
+ * MIT License
+ *
+ * To use, place in ~/.ipython/profile_notebook/static/custom/custom.css
+ * and refresh the page. (The IPython/Jupyter server does not need to be restarted.)
+ *
+ * Based on suggestions from practicaltypography.com
+ */
+
+.rendered_html ol {
+ list-style:decimal;
+ margin: 1em 2em;
+ color: #fff;
+}
+
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+
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+ font-family: "Optima", "Charis SIL", "Hoefler Text", Garamond, Palatino, serif;
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+ text-rendering: optimizeLegibility;
+ font-variant-ligatures: contextual no-historical-ligatures common-ligatures;,
+ background-color: #4a525a;
+}
+
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+}
+
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+}
+
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+ font-family: "Source Code Pro", "Droid Sans Mono", Consolas, "Ubuntu Mono", "Liberation Mono", monospace;
+ font-size: 11px;
+}
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+div.output_area pre {
+ font-family: "Source Code Pro", "Droid Sans Mono", Consolas, "Ubuntu Mono", "Liberation Mono", monospace;
+ line-height:1.25em;
+ font-size: 16px;
+ background-color: #F4F4F4;
+}
+
+div.output_error pre {
+ background-color: #ffeeee;
+}
+
+.cm-header-1, .rendered_html h1 {
+ font-family: "Charis SIL", "Hoefler Text", Garamond, Palatino, serif;
+ font-size: 24px;
+ font-variant: small-caps;
+ text-rendering: auto;
+ letter-spacing: 0.06em;
+ font-variant-ligatures: contextual no-historical-ligatures common-ligatures;
+}
+
+.cm-header-2, .rendered_html h2 {
+ font-family: "Charis SIL", "Hoefler Text", Garamond, Palatino, serif;
+ font-size: 21px;
+ font-weight: bold;
+ text-rendering: optimizeLegibility;
+ font-variant-ligatures: contextual no-historical-ligatures common-ligatures;
+}
+
+.cm-header-3, .rendered_html h3 {
+ font-family: "Charis SIL", "Hoefler Text", Garamond, Palatino, serif;
+ font-size: 19px;
+ font-weight: bold;
+ text-rendering: optimizeLegibility;
+ font-variant-ligatures: contextual no-historical-ligatures common-ligatures;
+}
+
+.cm-header-4, .rendered_html h4, .cm-header-5, .rendered_html h5, .cm-header-6, .rendered_html h6 {
+ font-family: "Charis SIL", "Hoefler Text", Garamond, Palatino, serif;
+ font-size: 18px;
+ font-weight: bold;
+ text-rendering: optimizeLegibility;
+ font-variant-ligatures: contextual no-historical-ligatures common-ligatures;
+}
+
+.rendered_html td {
+ font-variant-numeric: tabular-nums;
+}
+
+ </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+ overflow: visible;
+ padding: 8px;
+}
+
+div#notebook {
+ overflow: visible;
+ border-top: none;
+}
+
+@media print {
+ div.cell {
+ display: block;
+ page-break-inside: avoid;
+ }
+ div.output_wrapper {
+ display: block;
+ page-break-inside: avoid;
+ }
+ div.output {
+ display: block;
+ page-break-inside: avoid;
+ }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+ <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+ <!-- MathJax configuration -->
+ <script type="text/x-mathjax-config">
+ MathJax.Hub.Config({
+ tex2jax: {
+ inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+ displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+ processEscapes: true,
+ processEnvironments: true
+ },
+ // Center justify equations in code and markdown cells. Elsewhere
+ // we use CSS to left justify single line equations in code cells.
+ displayAlign: 'center',
+ "HTML-CSS": {
+ styles: {'.MathJax_Display': {"margin": 0}},
+ linebreaks: { automatic: true }
+ }
+ });
+ </script>
+ <!-- End of mathjax configuration --></head>
+<body>
+ <div tabindex="-1" id="notebook" class="border-box-sizing">
+ <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Conditional-Formatting">Conditional Formatting<a class="anchor-link" href="#Conditional-Formatting">¶</a></h1><p><em>New in version 0.17.1</em></p>
+<p><p style="color: red"><em>Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your <a href="https://github.com/pydata/pandas/issues">feedback</a>.</em><p style="color: red"></p>
+<p>You can apply <strong>conditional formatting</strong>, the visual styling of a DataFrame
+depending on the data within, by using the <code>DataFrame.style</code> property.
+This is a property that returns a <code>pandas.Styler</code> object, which has
+useful methods for formatting and displaying DataFrames.</p>
+<p>The styling is accomplished using CSS.
+You write "style functions" that take scalars, <code>DataFrame</code>s or <code>Series</code>, and return <em>like-indexed</em> DataFrames or Series with CSS <code>"attribute: value"</code> pairs for the values.
+These functions can be incrementally passed to the <code>Styler</code> which collects the styles before rendering.</p>
+<h3 id="Contents">Contents<a class="anchor-link" href="#Contents">¶</a></h3><ul>
+<li><a href="#Building-Styles">Building Styles</a></li>
+<li><a href="#Finer-Control:-Slicing">Finer Control: Slicing</a></li>
+<li><a href="#Builtin-Styles">Builtin Styles</a></li>
+<li><a href="#Other-options">Other options</a></li>
+<li><a href="#Sharing-Styles">Sharing Styles</a></li>
+<li><a href="#Limitations">Limitations</a></li>
+<li><a href="#Terms">Terms</a></li>
+<li><a href="#Extensibility">Extensibility</a></li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Building-Styles">Building Styles<a class="anchor-link" href="#Building-Styles">¶</a></h1><p>Pass your style functions into one of the following methods:</p>
+<ul>
+<li><code>Styler.applymap</code>: elementwise</li>
+<li><code>Styler.apply</code>: column-/row-/table-wise</li>
+</ul>
+<p>Both of those methods take a function (and some other keyword arguments) and applies your function to the DataFrame in a certain way.
+<code>Styler.applymap</code> works through the DataFrame elementwise.
+<code>Styler.apply</code> passes each column or row into your DataFrame one-at-a-time or the entire table at once, depending on the <code>axis</code> keyword argument.
+For columnwise use <code>axis=0</code>, rowwise use <code>axis=1</code>, and for the entire table at once use <code>axis=None</code>.</p>
+<p>The result of the function application, a CSS attribute-value pair, is stored in an internal dictionary on your <code>Styler</code> object.</p>
+<p>Let's see some examples.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [1]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">24</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s">'A'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)})</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s">'BCDE'</span><span class="p">))],</span>
+ <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Here's a boring example of rendering a DataFrame, without any (visible) styles:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [2]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[2]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ </style>
+
+ <table id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><em>Note</em>: The <code>DataFrame.style</code> attribute is a propetry that returns a <code>Styler</code> object. <code>Styler</code> has a <code>_repr_html_</code> method defined on it so they are rendered automatically. If you want the actual HTML back for further processing or for writing to file call the <code>.render()</code> method which returns a string.</p>
+<p>The above output looks very similar to the standard DataFrame HTML representation. But we've done some work behind the scenes to attach CSS classes to each cell. We can view these by calling the <code>.render</code> method.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [3]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_null</span><span class="p">()</span><span class="o">.</span><span class="n">render</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">'</span><span class="se">\n</span><span class="s">'</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[3]:</div>
+
+
+<div class="output_text output_subarea output_execute_result">
+<pre>['',
+ ' <style type="text/css" >',
+ ' ',
+ ' ',
+ ' #T_e7c1f51a_8bd5_11e5_803e_a45e60bd97fbrow0_col2 {',
+ ' ',
+ ' background-color: red;',
+ ' ',
+ ' }',
+ ' ']</pre>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The <code>row0_col2</code> is the identifier for that particular cell. We've also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn't collied with the styling from another within the same notebook or page (you can set the <code>uuid</code> if you'd like to tie together the styling of two DataFrames).</p>
+<p>When writing style functions, you take care of producing the CSS attribute / value pairs you want. Pandas matches those up with the CSS classes that identify each cell.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's write a simple style function that will color negative numbers red and positive numbers black.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [4]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">color_negative_red</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Takes a scalar and returns a string with</span>
+<span class="sd"> the css property `'color: red'` for negative</span>
+<span class="sd"> strings, black otherwise.</span>
+<span class="sd"> """</span>
+ <span class="n">color</span> <span class="o">=</span> <span class="s">'red'</span> <span class="k">if</span> <span class="n">val</span> <span class="o"><</span> <span class="mi">0</span> <span class="k">else</span> <span class="s">'black'</span>
+ <span class="k">return</span> <span class="s">'color: %s'</span> <span class="o">%</span> <span class="n">color</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In this case, the cell's style depends only on it's own value.
+That means we should use the <code>Styler.applymap</code> method which works elementwise.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [5]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>
+<span class="n">s</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[5]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2 {
+
+ color: black;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3 {
+
+ color: red;
+
+ }
+
+ #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4 {
+
+ color: black;
+
+ }
+
+ </style>
+
+ <table id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row0">
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+ 0
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+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0" class="data row0 col0">
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+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1" class="data row0 col1">
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+ 1.329212
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
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+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Notice the similarity with the standard <code>df.applymap</code>, which operates on DataFrames elementwise. We want you to be able to resuse your existing knowledge of how to interact with DataFrames.</p>
+<p>Notice also that our function returned a string containing the CSS attribute and value, separated by a colon just like in a <code><style></code> tag. This will be a common theme.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Now suppose you wanted to highlight the maximum value in each column.
+We can't use <code>.applymap</code> anymore since that operated elementwise.
+Instead, we'll turn to <code>.apply</code> which operates columnwise (or rowwise using the <code>axis</code> keyword). Later on we'll see that something like <code>highlight_max</code> is already defined on <code>Styler</code> so you wouldn't need to write this yourself.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [6]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">highlight_max</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
+ <span class="sd">'''</span>
+<span class="sd"> highlight the maximum in a Series yellow.</span>
+<span class="sd"> '''</span>
+ <span class="n">is_max</span> <span class="o">=</span> <span class="n">s</span> <span class="o">==</span> <span class="n">s</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+ <span class="k">return</span> <span class="p">[</span><span class="s">'background-color: yellow'</span> <span class="k">if</span> <span class="n">v</span> <span class="k">else</span> <span class="s">''</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">is_max</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [7]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[7]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col4 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col2 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col1 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col3 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col0 {
+
+ background-color: yellow;
+
+ }
+
+ </style>
+
+ <table id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col1" class="data row6 col1">
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+ 0.121668
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+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
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+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
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+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
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+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
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+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>We encourage you to use method chains to build up a style piecewise, before finally rending at the end of the chain.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [8]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span>\
+ <span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span><span class="o">.</span>\
+ <span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[8]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1 {
+
+ color: black;
+
+ : ;
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+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col2 {
+
+ color: black;
+
+ : ;
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3 {
+
+ color: red;
+
+ : ;
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+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4 {
+
+ color: red;
+
+ : ;
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+ }
+
+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col0 {
+
+ color: black;
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+ : ;
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+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col2 {
+
+ color: red;
+
+ : ;
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+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col3 {
+
+ color: black;
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+ : ;
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col4 {
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+
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col1 {
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col3 {
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+ : ;
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1 {
+
+ color: black;
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+ background-color: yellow;
+
+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2 {
+
+ color: red;
+
+ : ;
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+ }
+
+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3 {
+
+ color: black;
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+ background-color: yellow;
+
+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4 {
+
+ color: red;
+
+ : ;
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+ }
+
+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0 {
+
+ color: black;
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+ background-color: yellow;
+
+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1 {
+
+ color: red;
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+ : ;
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+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2 {
+
+ color: black;
+
+ : ;
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+ }
+
+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3 {
+
+ color: red;
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+ : ;
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+ }
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+ #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ </style>
+
+ <table id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col2" class="data row0 col2">
+
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+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
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+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row1">
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+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col1" class="data row1 col1">
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+
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+
+ </tr>
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+ <tr>
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+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row2">
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+
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+ </tr>
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+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row3">
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+ 0.850229
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+
+ </tr>
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+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row4">
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+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row5">
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+ -0.063328
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+ </tr>
+
+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row6">
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+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Above we used <code>Styler.apply</code> to pass in each column one at a time.</p>
+<p style="background-color: #DEDEBE">*Debugging Tip*: If you're having trouble writing your style function, try just passing it into <code style="background-color: #DEDEBE">df.apply</code>. <code style="background-color: #DEDEBE">Styler.apply</code> uses that internally, so the result should be the same.</p><p>What if you wanted to highlight just the maximum value in the entire table?
+Use <code>.apply(function, axis=None)</code> to indicate that your function wants the entire table, not one column or row at a time. Let's try that next.</p>
+<p>We'll rewrite our <code>highlight-max</code> to handle either Series (from <code>.apply(axis=0 or 1)</code>) or DataFrames (from <code>.apply(axis=None)</code>). We'll also allow the color to be adjustable, to demonstrate that <code>.apply</code>, and <code>.applymap</code> pass along keyword arguments.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [9]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">highlight_max</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'yellow'</span><span class="p">):</span>
+ <span class="sd">'''</span>
+<span class="sd"> highlight the maximum in a Series or DataFrame</span>
+<span class="sd"> '''</span>
+ <span class="n">attr</span> <span class="o">=</span> <span class="s">'background-color: {}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">color</span><span class="p">)</span>
+ <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="c"># Series from .apply(axis=0) or axis=1</span>
+ <span class="n">is_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+ <span class="k">return</span> <span class="p">[</span><span class="n">attr</span> <span class="k">if</span> <span class="n">v</span> <span class="k">else</span> <span class="s">''</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">is_max</span><span class="p">]</span>
+ <span class="k">else</span><span class="p">:</span> <span class="c"># from .apply(axis=None)</span>
+ <span class="n">is_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">==</span> <span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+ <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">is_max</span><span class="p">,</span> <span class="n">attr</span><span class="p">,</span> <span class="s">''</span><span class="p">),</span>
+ <span class="n">index</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [10]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">'darkorange'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="k">None</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[10]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0 {
+
+ background-color: darkorange;
+
+ }
+
+ </style>
+
+ <table id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Building-Styles-Summary">Building Styles Summary<a class="anchor-link" href="#Building-Styles-Summary">¶</a></h3><p>Style functions should return strings with one or more CSS <code>attribute: value</code> delimited by semicolons. Use</p>
+<ul>
+<li><code>Styler.applymap(func)</code> for elementwise styles</li>
+<li><code>Styler.apply(func, axis=0)</code> for columnwise styles</li>
+<li><code>Styler.apply(func, axis=1)</code> for rowwise styles</li>
+<li><code>Styler.apply(func, axis=None)</code> for tablewise styles</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Finer-Control:-Slicing">Finer Control: Slicing<a class="anchor-link" href="#Finer-Control:-Slicing">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Both <code>Styler.apply</code>, and <code>Styler.applymap</code> accept a <code>subset</code> keyword.
+This allows you to apply styles to specific rows or columns, without having to code that logic into your <code>style</code> function.</p>
+<p>The value passed to <code>subset</code> behaves simlar to slicing a DataFrame.</p>
+<ul>
+<li>A scalar is treated as a column label</li>
+<li>A list (or series or numpy array)</li>
+<li>A tuple is treated as <code>(row_indexer, column_indexer)</code></li>
+</ul>
+<p>Consider using <code>pd.IndexSlice</code> to construct the tuple for the last one.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [11]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s">'B'</span><span class="p">,</span> <span class="s">'C'</span><span class="p">,</span> <span class="s">'D'</span><span class="p">])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[11]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col2 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col1 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col3 {
+
+ background-color: yellow;
+
+ }
+
+ </style>
+
+ <table id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For row and column slicing, any valid indexer to <code>.loc</code> will work.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [12]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">,</span>
+ <span class="n">subset</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">IndexSlice</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span> <span class="p">[</span><span class="s">'B'</span><span class="p">,</span> <span class="s">'D'</span><span class="p">]])</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1 {
+
+ color: red;
+
+ }
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col3 {
+
+ color: black;
+
+ }
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col3 {
+
+ color: red;
+
+ }
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col1 {
+
+ color: black;
+
+ }
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col3 {
+
+ color: black;
+
+ }
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col1 {
+
+ color: red;
+
+ }
+
+ #T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col3 {
+
+ color: black;
+
+ }
+
+ </style>
+
+ <table id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
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+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
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+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Only label-based slicing is supported right now, not positional.</p>
+<p>If your style function uses a <code>subset</code> or <code>axis</code> keyword argument, consider wrapping your function in a <code>functools.partial</code>, partialing out that keyword.</p>
+<div class="highlight"><pre><span class="n">my_func2</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span><span class="n">my_func</span><span class="p">,</span> <span class="n">subset</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Builtin-Styles">Builtin Styles<a class="anchor-link" href="#Builtin-Styles">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Finally, we expect certain styling functions to be common enough that we've included a few "built-in" to the <code>Styler</code>, so you don't have to write them yourself.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [13]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_null</span><span class="p">(</span><span class="n">null_color</span><span class="o">=</span><span class="s">'red'</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[13]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2 {
+
+ background-color: red;
+
+ }
+
+ </style>
+
+ <table id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can create "heatmaps" with the <code>background_gradient</code> method. These require matplotlib, and we'll use <a href="http://stanford.edu/~mwaskom/software/seaborn/">Seaborn</a> to get a nice colormap.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [14]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+<span class="n">cm</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">light_palette</span><span class="p">(</span><span class="s">"green"</span><span class="p">,</span> <span class="n">as_cmap</span><span class="o">=</span><span class="k">True</span><span class="p">)</span>
+
+<span class="n">s</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">)</span>
+<span class="n">s</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[14]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col0 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col1 {
+
+ background-color: #188d18;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col2 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col3 {
+
+ background-color: #c7eec7;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col4 {
+
+ background-color: #a6dca6;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col0 {
+
+ background-color: #ccf1cc;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col1 {
+
+ background-color: #c0eac0;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col2 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col3 {
+
+ background-color: #62b662;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col4 {
+
+ background-color: #5cb35c;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col0 {
+
+ background-color: #b3e3b3;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col1 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col2 {
+
+ background-color: #56af56;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col3 {
+
+ background-color: #56af56;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col4 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col0 {
+
+ background-color: #99d599;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col1 {
+
+ background-color: #329c32;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col2 {
+
+ background-color: #5fb55f;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col3 {
+
+ background-color: #daf9da;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col4 {
+
+ background-color: #3ba13b;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col0 {
+
+ background-color: #80c780;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col1 {
+
+ background-color: #108910;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col2 {
+
+ background-color: #0d870d;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col3 {
+
+ background-color: #90d090;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col4 {
+
+ background-color: #4fac4f;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col0 {
+
+ background-color: #66b866;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col1 {
+
+ background-color: #d2f4d2;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col2 {
+
+ background-color: #389f38;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col3 {
+
+ background-color: #048204;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col4 {
+
+ background-color: #70be70;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col0 {
+
+ background-color: #4daa4d;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col1 {
+
+ background-color: #6cbc6c;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col2 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col3 {
+
+ background-color: #a3daa3;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col4 {
+
+ background-color: #0e880e;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col0 {
+
+ background-color: #329c32;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col1 {
+
+ background-color: #5cb35c;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col2 {
+
+ background-color: #0e880e;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col3 {
+
+ background-color: #cff3cf;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col4 {
+
+ background-color: #4fac4f;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col0 {
+
+ background-color: #198e19;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col1 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col2 {
+
+ background-color: #dcfadc;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col3 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col4 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col0 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col1 {
+
+ background-color: #7ec67e;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2 {
+
+ background-color: #319b31;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col4 {
+
+ background-color: #5cb35c;
+
+ }
+
+ </style>
+
+ <table id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><code>Styler.background_gradient</code> takes the keyword arguments <code>low</code> and <code>high</code>. Roughly speaking these extend the range of your data by <code>low</code> and <code>high</code> percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [15]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="c"># Uses the full color range</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s">'viridis'</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[15]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0 {
+
+ background-color: #440154;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1 {
+
+ background-color: #e5e419;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2 {
+
+ background-color: #440154;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3 {
+
+ background-color: #46327e;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4 {
+
+ background-color: #440154;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0 {
+
+ background-color: #3b528b;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1 {
+
+ background-color: #433e85;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2 {
+
+ background-color: #440154;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3 {
+
+ background-color: #bddf26;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4 {
+
+ background-color: #25838e;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0 {
+
+ background-color: #21918c;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1 {
+
+ background-color: #440154;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2 {
+
+ background-color: #35b779;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3 {
+
+ background-color: #fde725;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4 {
+
+ background-color: #fde725;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0 {
+
+ background-color: #5ec962;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1 {
+
+ background-color: #95d840;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2 {
+
+ background-color: #26ad81;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3 {
+
+ background-color: #440154;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4 {
+
+ background-color: #2cb17e;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0 {
+
+ background-color: #fde725;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1 {
+
+ background-color: #fde725;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2 {
+
+ background-color: #fde725;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3 {
+
+ background-color: #1f9e89;
+
+ }
+
+ #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4 {
+
+ background-color: #1f958b;
+
+ }
+
+ </style>
+
+ <table id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [16]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="c"># Compreess the color range</span>
+<span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span>
+ <span class="o">.</span><span class="n">style</span>
+ <span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="s">'viridis'</span><span class="p">,</span> <span class="n">low</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+ <span class="o">.</span><span class="n">highlight_null</span><span class="p">(</span><span class="s">'red'</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[16]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0 {
+
+ background-color: #31688e;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1 {
+
+ background-color: #efe51c;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2 {
+
+ background-color: #440154;
+
+ background-color: red;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3 {
+
+ background-color: #277f8e;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4 {
+
+ background-color: #31688e;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0 {
+
+ background-color: #21918c;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1 {
+
+ background-color: #25858e;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2 {
+
+ background-color: #31688e;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3 {
+
+ background-color: #d5e21a;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4 {
+
+ background-color: #29af7f;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0 {
+
+ background-color: #35b779;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1 {
+
+ background-color: #31688e;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2 {
+
+ background-color: #6ccd5a;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3 {
+
+ background-color: #fde725;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4 {
+
+ background-color: #fde725;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0 {
+
+ background-color: #90d743;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1 {
+
+ background-color: #b8de29;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2 {
+
+ background-color: #5ac864;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3 {
+
+ background-color: #31688e;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4 {
+
+ background-color: #63cb5f;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0 {
+
+ background-color: #fde725;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1 {
+
+ background-color: #fde725;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2 {
+
+ background-color: #fde725;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3 {
+
+ background-color: #46c06f;
+
+ : ;
+
+ }
+
+ #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4 {
+
+ background-color: #3bbb75;
+
+ : ;
+
+ }
+
+ </style>
+
+ <table id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can include "bar charts" in your DataFrame.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [17]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">[</span><span class="s">'A'</span><span class="p">,</span> <span class="s">'B'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s">'#d65f5f'</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[17]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 16.77000113307442%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 22.22222222222222%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 33.333333333333336%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 78.1150827834652%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 44.44444444444444%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 92.96229618327422%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
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+
+ }
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+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1 {
+
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+
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+
+ background: linear-gradient(90deg,#d65f5f 8.737388253449494%, transparent 0%);
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+ }
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+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0 {
+
+ width: 10em;
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+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 66.66666666666667%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 52.764243600289866%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 77.77777777777777%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 59.79187201238315%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 88.88888888888889%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 100.00000000000001%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col0 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);
+
+ }
+
+ #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col1 {
+
+ width: 10em;
+
+ height: 80%;
+
+ background: linear-gradient(90deg,#d65f5f 45.17326030334935%, transparent 0%);
+
+ }
+
+ </style>
+
+ <table id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>There's also <code>.highlight_min</code> and <code>.highlight_max</code>.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [18]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[18]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow2_col4 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow6_col2 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col1 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow8_col3 {
+
+ background-color: yellow;
+
+ }
+
+ #T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow9_col0 {
+
+ background-color: yellow;
+
+ }
+
+ </style>
+
+ <table id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e80cc464_8bd5_11e5_8a2b_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
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+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [19]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">highlight_min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[19]:</div>
+
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+ <style type="text/css" >
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+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Use <code>Styler.set_properties</code> when the style doesn't actually depend on the values.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [20]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_properties</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s">'background-color'</span><span class="p">:</span> <span class="s">'black'</span><span class="p">,</span>
+ <span class="s">'color'</span><span class="p">:</span> <span class="s">'lawngreen'</span><span class="p">,</span>
+ <span class="s">'border-color'</span><span class="p">:</span> <span class="s">'white'</span><span class="p">})</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[20]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col0 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col1 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4 {
+
+ color: lawngreen;
+
+ background-color: black;
+
+ border-color: white;
+
+ }
+
+ </style>
+
+ <table id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0" class="data row8 col0">
+
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+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
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+ <th id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb" class="row_heading level4 row9">
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+
+ -0.12982
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Sharing-Styles">Sharing Styles<a class="anchor-link" href="#Sharing-Styles">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with <code>df1.style.export</code>, and import it on the second DataFrame with <code>df1.style.set</code></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [21]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df2</span> <span class="o">=</span> <span class="o">-</span><span class="n">df</span>
+<span class="n">style1</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>
+<span class="n">style1</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[21]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
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+ <th class="col_heading level0 col3">D
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+ <th class="col_heading level0 col4">E
+
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+ <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [22]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">style2</span> <span class="o">=</span> <span class="n">df2</span><span class="o">.</span><span class="n">style</span>
+<span class="n">style2</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">style1</span><span class="o">.</span><span class="n">export</span><span class="p">())</span>
+<span class="n">style2</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[22]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
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+ #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col0 {
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+ color: red;
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+ color: red;
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+ }
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+ #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow0_col2 {
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+ color: black;
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+ #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col1 {
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+ }
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+ #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col2 {
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+ color: black;
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+ }
+
+ #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow1_col3 {
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+ color: red;
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+ }
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+ }
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+
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+
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+ }
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+
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+ }
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+
+ }
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+ #T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col4 {
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+
+ }
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+ </style>
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+
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+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row0">
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+ <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row1">
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+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row6">
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+
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+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row7">
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+
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+
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+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row8">
+
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+
+
+ <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col0" class="data row8 col0">
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+ <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col1" class="data row8 col1">
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+ <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ 2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fb" class="row_heading level4 row9">
+
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+
+
+ <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col0" class="data row9 col0">
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+
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+ <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ 0.12982
+
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+
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+
+
+ <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ 0.586538
+
+
+ <td id="T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ -0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Other-options">Other options<a class="anchor-link" href="#Other-options">¶</a></h2><p>You've seen a few methods for data-driven styling.
+<code>Styler</code> also provides a few other options for styles that don't depend on the data.</p>
+<ul>
+<li>precision</li>
+<li>captions</li>
+<li>table-wide styles</li>
+</ul>
+<p>Each of these can be specified in two ways:</p>
+<ul>
+<li>A keyword argument to <code>pandas.core.Styler</code></li>
+<li>A call to one of the <code>.set_</code> methods, e.g. <code>.set_caption</code></li>
+</ul>
+<p>The best method to use depends on the context. Use the <code>Styler</code> constructor when building many styled DataFrames that should all share the same properties. For interactive use, the<code>.set_</code> methods are more convenient.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Precision">Precision<a class="anchor-link" href="#Precision">¶</a></h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>You can control the precision of floats using pandas' regular <code>display.precision</code> option.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [23]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="k">with</span> <span class="n">pd</span><span class="o">.</span><span class="n">option_context</span><span class="p">(</span><span class="s">'display.precision'</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
+ <span class="n">html</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">style</span>
+ <span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>
+ <span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">))</span>
+<span class="n">html</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[23]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col1 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col3 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col4 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col1 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col2 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col3 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col1 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col3 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col4 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col1 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col3 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col1 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col3 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col1 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col3 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col4 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col1 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col2 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col3 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col1 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col3 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ </style>
+
+ <table id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.33
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.32
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.07
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.44
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.56
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.3
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.63
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.22
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.68
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.89
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.96
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.1
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.48
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.85
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.45
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.06
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.17
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.52
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.34
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.56
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.39
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.06
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.12
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.21
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.63
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.35
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.04
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.39
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.52
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.69
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.33
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.43
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.09
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.13
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.63
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.59
+
+
+ <td id="T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Or through a <code>set_precision</code> method.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [24]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span>\
+ <span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="n">color_negative_red</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">highlight_max</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">set_precision</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[24]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0 {
+
+ color: black;
+
+ : ;
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+ }
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+
+ color: black;
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+ : ;
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+ }
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+ : ;
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+
+ color: red;
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+ : ;
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+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1 {
+
+ color: red;
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+ : ;
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+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2 {
+
+ color: black;
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+ : ;
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+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4 {
+
+ color: black;
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+ background-color: yellow;
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+ }
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+
+ color: black;
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+ : ;
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1 {
+
+ color: black;
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+ : ;
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+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2 {
+
+ color: black;
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+ : ;
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+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3 {
+
+ color: red;
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+ : ;
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+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4 {
+
+ color: black;
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+ : ;
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+ }
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+
+ color: black;
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+ color: black;
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+ : ;
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+ }
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+
+ color: black;
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+ : ;
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+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4 {
+
+ color: black;
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+ : ;
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+ }
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+
+ color: black;
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+ : ;
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+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1 {
+
+ color: red;
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+ : ;
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+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2 {
+
+ color: black;
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+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4 {
+
+ color: red;
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+ : ;
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+ }
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+ color: black;
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+ : ;
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+ }
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+
+ color: black;
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+ : ;
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+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2 {
+
+ color: black;
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+ background-color: yellow;
+
+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3 {
+
+ color: red;
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+ : ;
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+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4 {
+
+ color: black;
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+ : ;
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+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1 {
+
+ color: black;
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+ : ;
+
+ }
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+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0 {
+
+ color: black;
+
+ background-color: yellow;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3 {
+
+ color: red;
+
+ : ;
+
+ }
+
+ #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4 {
+
+ color: black;
+
+ : ;
+
+ }
+
+ </style>
+
+ <table id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.33
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.32
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.07
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.44
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.56
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.3
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.63
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.22
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.68
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.89
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.96
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.1
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.48
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.85
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.45
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.06
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.17
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.52
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.34
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.56
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.39
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.06
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.12
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.21
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.63
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.35
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.04
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.39
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.52
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.69
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.33
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.43
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.09
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8394654_8bd5_11e5_b698_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.13
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.63
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.59
+
+
+ <td id="T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use <code>df.round(2).style</code> if you'd prefer to round from the start.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Captions">Captions<a class="anchor-link" href="#Captions">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Regular table captions can be added in a few ways.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [25]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">'Colormaps, with a caption.'</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[25]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col0 {
+
+ background-color: #e5ffe5;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col1 {
+
+ background-color: #188d18;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col2 {
+
+ background-color: #e5ffe5;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col3 {
+
+ background-color: #c7eec7;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col4 {
+
+ background-color: #a6dca6;
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col0 {
+
+ background-color: #ccf1cc;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col1 {
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col2 {
+
+ background-color: #e5ffe5;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col3 {
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+ background-color: #62b662;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col4 {
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+ background-color: #5cb35c;
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+ background-color: #e5ffe5;
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col2 {
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+ background-color: #56af56;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col3 {
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+ background-color: #56af56;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col4 {
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+ background-color: #008000;
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+ background-color: #99d599;
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+ }
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+ background-color: #80c780;
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+ }
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+ background-color: #108910;
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+ }
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+ }
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+ }
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+ background-color: #4fac4f;
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+ }
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+ background-color: #66b866;
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+ }
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+ background-color: #d2f4d2;
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+ }
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+
+ background-color: #389f38;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3 {
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+ background-color: #048204;
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+ }
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+
+ background-color: #70be70;
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+ }
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+ background-color: #4daa4d;
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+ }
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+ background-color: #6cbc6c;
+
+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2 {
+
+ background-color: #008000;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col3 {
+
+ background-color: #a3daa3;
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+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4 {
+
+ background-color: #0e880e;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col0 {
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+ background-color: #329c32;
+
+ }
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+ background-color: #5cb35c;
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+ }
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+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col2 {
+
+ background-color: #0e880e;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3 {
+
+ background-color: #cff3cf;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4 {
+
+ background-color: #4fac4f;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0 {
+
+ background-color: #198e19;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2 {
+
+ background-color: #dcfadc;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0 {
+
+ background-color: #008000;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1 {
+
+ background-color: #7ec67e;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2 {
+
+ background-color: #319b31;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3 {
+
+ background-color: #e5ffe5;
+
+ }
+
+ #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4 {
+
+ background-color: #5cb35c;
+
+ }
+
+ </style>
+
+ <table id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb">
+
+ <caption>Colormaps, with a caption.</caption>
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Table-Styles">Table Styles<a class="anchor-link" href="#Table-Styles">¶</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The next option you have are "table styles".
+These are styles that apply to the table as a whole, but don't look at the data.
+Certain sytlings, including pseudo-selectors like <code>:hover</code> can only be used this way.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [26]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">HTML</span>
+
+<span class="k">def</span> <span class="nf">hover</span><span class="p">(</span><span class="n">hover_color</span><span class="o">=</span><span class="s">"#ffff99"</span><span class="p">):</span>
+ <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"tr:hover"</span><span class="p">,</span>
+ <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"background-color"</span><span class="p">,</span> <span class="s">"%s"</span> <span class="o">%</span> <span class="n">hover_color</span><span class="p">)])</span>
+
+<span class="n">styles</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">hover</span><span class="p">(),</span>
+ <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th"</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"150%"</span><span class="p">),</span>
+ <span class="p">(</span><span class="s">"text-align"</span><span class="p">,</span> <span class="s">"center"</span><span class="p">)]),</span>
+ <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"caption"</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"caption-side"</span><span class="p">,</span> <span class="s">"bottom"</span><span class="p">)])</span>
+<span class="p">]</span>
+<span class="n">html</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">set_table_styles</span><span class="p">(</span><span class="n">styles</span><span class="p">)</span>
+ <span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">"Hover to highlight."</span><span class="p">))</span>
+<span class="n">html</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[26]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+ #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb tr:hover {
+
+ background-color: #ffff99;
+
+ }
+
+ #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb th {
+
+ font-size: 150%;
+
+ text-align: center;
+
+ }
+
+ #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb caption {
+
+ caption-side: bottom;
+
+ }
+
+
+ </style>
+
+ <table id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb">
+
+ <caption>Hover to highlight.</caption>
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><code>table_styles</code> should be a list of dictionaries.
+Each dictionary should have the <code>selector</code> and <code>props</code> keys.
+The value for <code>selector</code> should be a valid CSS selector.
+Recall that all the styles are already attached to an <code>id</code>, unique to
+each <code>Styler</code>. This selector is in addition to that <code>id</code>.
+The value for <code>props</code> should be a list of tuples of <code>('attribute', 'value')</code>.</p>
+<p><code>table_styles</code> are extremely flexible, but not as fun to type out by hand.
+We hope to collect some useful ones either in pandas, or preferable in a new package that <a href="#Extensibility">builds on top</a> the tools here.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Limitations">Limitations<a class="anchor-link" href="#Limitations">¶</a></h1><ul>
+<li>DataFrame only <code>(use Series.to_frame().style)</code></li>
+<li>The index and columns must be unique</li>
+<li>No large repr, and performance isn't great; this is intended for summary DataFrames</li>
+<li>You can only style the <em>values</em>, not the index or columns</li>
+<li>You can only apply styles, you can't insert new HTML entities</li>
+</ul>
+<p>Some of these will be addressed in the future.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Terms">Terms<a class="anchor-link" href="#Terms">¶</a></h1><ul>
+<li>Style function: a function that's passed into <code>Styler.apply</code> or <code>Styler.applymap</code> and returns values like <code>'css attribute: value'</code></li>
+<li>Builtin style functions: style functions that are methods on <code>Styler</code></li>
+<li>table style: a dictionary with the two keys <code>selector</code> and <code>props</code>. <code>selector</code> is the CSS selector that <code>props</code> will apply to. <code>props</code> is a list of <code>(attribute, value)</code> tuples. A list of table styles passed into <code>Styler</code>.</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Fun-stuff">Fun stuff<a class="anchor-link" href="#Fun-stuff">¶</a></h2><p>Here are a few interesting examples.</p>
+<p><code>Styler</code> interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [27]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">IPython.html</span> <span class="k">import</span> <span class="n">widgets</span>
+<span class="nd">@widgets</span><span class="o">.</span><span class="n">interact</span>
+<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">h_neg</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">359</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">h_pos</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">359</span><span class="p">),</span> <span class="n">s</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">99.9</span><span class="p">),</span> <span class="n">l</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">99.9</span><span class="p">)):</span>
+ <span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span>
+ <span class="n">cmap</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">palettes</span><span class="o">.</span><span class="n">diverging_palette</span><span class="p">(</span><span class="n">h_neg</span><span class="o">=</span><span class="n">h_neg</span><span class="p">,</span> <span class="n">h_pos</span><span class="o">=</span><span class="n">h_pos</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">l</span><span class="o">=</span><span class="n">l</span><span class="p">,</span>
+ <span class="n">as_cmap</span><span class="o">=</span><span class="k">True</span><span class="p">)</span>
+ <span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+
+<div class="output_html rendered_html output_subarea ">
+
+ <style type="text/css" >
+
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col0 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col1 {
+
+ background-color: #779894;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col2 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col3 {
+
+ background-color: #809f9b;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col4 {
+
+ background-color: #aec2bf;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col0 {
+
+ background-color: #799995;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col1 {
+
+ background-color: #8ba7a3;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col2 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col3 {
+
+ background-color: #dfe8e7;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4 {
+
+ background-color: #d7e2e0;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col0 {
+
+ background-color: #9cb5b1;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col2 {
+
+ background-color: #cedbd9;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col3 {
+
+ background-color: #cedbd9;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col4 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col0 {
+
+ background-color: #c1d1cf;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col1 {
+
+ background-color: #9cb5b1;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col2 {
+
+ background-color: #dce5e4;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col3 {
+
+ background-color: #668b86;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col4 {
+
+ background-color: #a9bebb;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col0 {
+
+ background-color: #e5eceb;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1 {
+
+ background-color: #6c908b;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col2 {
+
+ background-color: #678c87;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col3 {
+
+ background-color: #cedbd9;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4 {
+
+ background-color: #c5d4d2;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0 {
+
+ background-color: #e5eceb;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1 {
+
+ background-color: #71948f;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col2 {
+
+ background-color: #a4bbb8;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col3 {
+
+ background-color: #5a827d;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4 {
+
+ background-color: #f2f2f2;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0 {
+
+ background-color: #c1d1cf;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1 {
+
+ background-color: #edf3f2;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col2 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col3 {
+
+ background-color: #b3c6c4;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4 {
+
+ background-color: #698e89;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col0 {
+
+ background-color: #9cb5b1;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1 {
+
+ background-color: #d7e2e0;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col2 {
+
+ background-color: #698e89;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3 {
+
+ background-color: #759792;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4 {
+
+ background-color: #c5d4d2;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0 {
+
+ background-color: #799995;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2 {
+
+ background-color: #628882;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1 {
+
+ background-color: #e7eeed;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2 {
+
+ background-color: #9bb4b0;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3 {
+
+ background-color: #557e79;
+
+ }
+
+ #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4 {
+
+ background-color: #d7e2e0;
+
+ }
+
+ </style>
+
+ <table id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb">
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">A
+
+ <th class="col_heading level0 col1">B
+
+ <th class="col_heading level0 col2">C
+
+ <th class="col_heading level0 col3">D
+
+ <th class="col_heading level0 col4">E
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row0">
+
+ 0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 1.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.329212
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ nan
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.31628
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.99081
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row1">
+
+ 1
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ 2.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ -1.070816
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.438713
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ 0.564417
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 0.295722
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row2">
+
+ 2
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ 3.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ -1.626404
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ 0.219565
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col3" class="data row2 col3">
+
+ 0.678805
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col4" class="data row2 col4">
+
+ 1.889273
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row3">
+
+ 3
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col0" class="data row3 col0">
+
+ 4.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col1" class="data row3 col1">
+
+ 0.961538
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col2" class="data row3 col2">
+
+ 0.104011
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col3" class="data row3 col3">
+
+ -0.481165
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col4" class="data row3 col4">
+
+ 0.850229
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row4">
+
+ 4
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col0" class="data row4 col0">
+
+ 5.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1" class="data row4 col1">
+
+ 1.453425
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col2" class="data row4 col2">
+
+ 1.057737
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col3" class="data row4 col3">
+
+ 0.165562
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4" class="data row4 col4">
+
+ 0.515018
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row5">
+
+ 5
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0" class="data row5 col0">
+
+ 6.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1" class="data row5 col1">
+
+ -1.336936
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col2" class="data row5 col2">
+
+ 0.562861
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col3" class="data row5 col3">
+
+ 1.392855
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4" class="data row5 col4">
+
+ -0.063328
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row6">
+
+ 6
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0" class="data row6 col0">
+
+ 7.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1" class="data row6 col1">
+
+ 0.121668
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col2" class="data row6 col2">
+
+ 1.207603
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col3" class="data row6 col3">
+
+ -0.00204
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4" class="data row6 col4">
+
+ 1.627796
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row7">
+
+ 7
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col0" class="data row7 col0">
+
+ 8.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1" class="data row7 col1">
+
+ 0.354493
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col2" class="data row7 col2">
+
+ 1.037528
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3" class="data row7 col3">
+
+ -0.385684
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4" class="data row7 col4">
+
+ 0.519818
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row8">
+
+ 8
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0" class="data row8 col0">
+
+ 9.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1" class="data row8 col1">
+
+ 1.686583
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2" class="data row8 col2">
+
+ -1.325963
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3" class="data row8 col3">
+
+ 1.428984
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4" class="data row8 col4">
+
+ -2.089354
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fb" class="row_heading level4 row9">
+
+ 9
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0" class="data row9 col0">
+
+ 10.0
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1" class="data row9 col1">
+
+ -0.12982
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2" class="data row9 col2">
+
+ 0.631523
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3" class="data row9 col3">
+
+ -0.586538
+
+
+ <td id="T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4" class="data row9 col4">
+
+ 0.29072
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [28]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">magnify</span><span class="p">():</span>
+ <span class="k">return</span> <span class="p">[</span><span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th"</span><span class="p">,</span>
+ <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"4pt"</span><span class="p">)]),</span>
+ <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"th:hover"</span><span class="p">,</span>
+ <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">"font-size"</span><span class="p">,</span> <span class="s">"12pt"</span><span class="p">)]),</span>
+ <span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s">"tr:hover td:hover"</span><span class="p">,</span>
+ <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s">'max-width'</span><span class="p">,</span> <span class="s">'200px'</span><span class="p">),</span>
+ <span class="p">(</span><span class="s">'font-size'</span><span class="p">,</span> <span class="s">'12pt'</span><span class="p">)])</span>
+<span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [29]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">25</span><span class="p">)</span>
+<span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap</span><span class="o">=</span><span class="n">sns</span><span class="o">.</span><span class="n">diverging_palette</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">250</span><span class="p">,</span> <span class="n">as_cmap</span><span class="o">=</span><span class="k">True</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">25</span><span class="p">))</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">background_gradient</span><span class="p">(</span><span class="n">cmap</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">set_properties</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s">'max-width'</span><span class="p">:</span> <span class="s">'80px'</span><span class="p">,</span> <span class="s">'font-size'</span><span class="p">:</span> <span class="s">'1pt'</span><span class="p">})</span>\
+ <span class="o">.</span><span class="n">set_caption</span><span class="p">(</span><span class="s">"Hover to magify"</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">set_precision</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>\
+ <span class="o">.</span><span class="n">set_table_styles</span><span class="p">(</span><span class="n">magnify</span><span class="p">())</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[29]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+
+ <style type="text/css" >
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th {
+
+ font-size: 4pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th:hover {
+
+ font-size: 12pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb tr:hover td:hover {
+
+ max-width: 200px;
+
+ font-size: 12pt;
+
+ }
+
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0 {
+
+ background-color: #ecf2f8;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1 {
+
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ max-width: 80px;
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ font-size: 1pt;
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+ }
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+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col6 {
+
+ background-color: #f2f2f2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col7 {
+
+ background-color: #4479bb;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col8 {
+
+ background-color: #ec9faf;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col9 {
+
+ background-color: #c6d6ea;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col10 {
+
+ background-color: #f2f2f2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col11 {
+
+ background-color: #dae5f2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col12 {
+
+ background-color: #4c7ebd;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col13 {
+
+ background-color: #d1deee;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col14 {
+
+ background-color: #fae6ea;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col15 {
+
+ background-color: #f7d9df;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col16 {
+
+ background-color: #eeacba;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col17 {
+
+ background-color: #f6d1d8;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col18 {
+
+ background-color: #f6d2d9;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col19 {
+
+ background-color: #f4c9d2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col20 {
+
+ background-color: #bccfe7;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col21 {
+
+ background-color: #d73c5b;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col22 {
+
+ background-color: #9eb9db;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col23 {
+
+ background-color: #5485c1;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col24 {
+
+ background-color: #8babd4;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col0 {
+
+ background-color: #86a8d3;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col1 {
+
+ background-color: #5b89c3;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col2 {
+
+ background-color: #f2bfca;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col3 {
+
+ background-color: #f2bfca;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col4 {
+
+ background-color: #497dbd;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col5 {
+
+ background-color: #f2bfca;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col6 {
+
+ background-color: #f2f2f2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col7 {
+
+ background-color: #5686c1;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col8 {
+
+ background-color: #eda8b6;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col9 {
+
+ background-color: #d9e4f1;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col10 {
+
+ background-color: #d5e1f0;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col11 {
+
+ background-color: #bfd1e8;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col12 {
+
+ background-color: #5787c2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13 {
+
+ background-color: #fbeaed;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14 {
+
+ background-color: #f8dee3;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15 {
+
+ background-color: #f2f2f2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16 {
+
+ background-color: #eeaab7;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17 {
+
+ background-color: #f6d1d8;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18 {
+
+ background-color: #f7d7de;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19 {
+
+ background-color: #f2f2f2;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20 {
+
+ background-color: #b5cae4;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21 {
+
+ background-color: #d73c5b;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22 {
+
+ background-color: #9eb9db;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23 {
+
+ background-color: #4479bb;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24 {
+
+ background-color: #89aad4;
+
+ max-width: 80px;
+
+ font-size: 1pt;
+
+ }
+
+ </style>
+
+ <table id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb">
+
+ <caption>Hover to magify</caption>
+
+
+ <thead>
+
+ <tr>
+
+ <th class="blank">
+
+ <th class="col_heading level0 col0">0
+
+ <th class="col_heading level0 col1">1
+
+ <th class="col_heading level0 col2">2
+
+ <th class="col_heading level0 col3">3
+
+ <th class="col_heading level0 col4">4
+
+ <th class="col_heading level0 col5">5
+
+ <th class="col_heading level0 col6">6
+
+ <th class="col_heading level0 col7">7
+
+ <th class="col_heading level0 col8">8
+
+ <th class="col_heading level0 col9">9
+
+ <th class="col_heading level0 col10">10
+
+ <th class="col_heading level0 col11">11
+
+ <th class="col_heading level0 col12">12
+
+ <th class="col_heading level0 col13">13
+
+ <th class="col_heading level0 col14">14
+
+ <th class="col_heading level0 col15">15
+
+ <th class="col_heading level0 col16">16
+
+ <th class="col_heading level0 col17">17
+
+ <th class="col_heading level0 col18">18
+
+ <th class="col_heading level0 col19">19
+
+ <th class="col_heading level0 col20">20
+
+ <th class="col_heading level0 col21">21
+
+ <th class="col_heading level0 col22">22
+
+ <th class="col_heading level0 col23">23
+
+ <th class="col_heading level0 col24">24
+
+ </tr>
+
+ </thead>
+ <tbody>
+
+ <tr>
+
+ <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row0">
+
+ 0
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0" class="data row0 col0">
+
+ 0.23
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1" class="data row0 col1">
+
+ 1.03
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col2" class="data row0 col2">
+
+ -0.84
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col3" class="data row0 col3">
+
+ -0.59
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col4" class="data row0 col4">
+
+ -0.96
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col5" class="data row0 col5">
+
+ -0.22
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col6" class="data row0 col6">
+
+ -0.62
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col7" class="data row0 col7">
+
+ 1.84
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col8" class="data row0 col8">
+
+ -2.05
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col9" class="data row0 col9">
+
+ 0.87
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col10" class="data row0 col10">
+
+ -0.92
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col11" class="data row0 col11">
+
+ -0.23
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col12" class="data row0 col12">
+
+ 2.15
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col13" class="data row0 col13">
+
+ -1.33
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col14" class="data row0 col14">
+
+ 0.08
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col15" class="data row0 col15">
+
+ -1.25
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col16" class="data row0 col16">
+
+ 1.2
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col17" class="data row0 col17">
+
+ -1.05
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col18" class="data row0 col18">
+
+ 1.06
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col19" class="data row0 col19">
+
+ -0.42
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col20" class="data row0 col20">
+
+ 2.29
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col21" class="data row0 col21">
+
+ -2.59
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col22" class="data row0 col22">
+
+ 2.82
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col23" class="data row0 col23">
+
+ 0.68
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col24" class="data row0 col24">
+
+ -1.58
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row1">
+
+ 1
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col0" class="data row1 col0">
+
+ -1.75
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col1" class="data row1 col1">
+
+ 1.56
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col2" class="data row1 col2">
+
+ -1.13
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col3" class="data row1 col3">
+
+ -1.1
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col4" class="data row1 col4">
+
+ 1.03
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col5" class="data row1 col5">
+
+ 0.0
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col6" class="data row1 col6">
+
+ -2.46
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col7" class="data row1 col7">
+
+ 3.45
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col8" class="data row1 col8">
+
+ -1.66
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col9" class="data row1 col9">
+
+ 1.27
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col10" class="data row1 col10">
+
+ -0.52
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col11" class="data row1 col11">
+
+ -0.02
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col12" class="data row1 col12">
+
+ 1.52
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col13" class="data row1 col13">
+
+ -1.09
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col14" class="data row1 col14">
+
+ -1.86
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col15" class="data row1 col15">
+
+ -1.13
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col16" class="data row1 col16">
+
+ -0.68
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col17" class="data row1 col17">
+
+ -0.81
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col18" class="data row1 col18">
+
+ 0.35
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col19" class="data row1 col19">
+
+ -0.06
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col20" class="data row1 col20">
+
+ 1.79
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col21" class="data row1 col21">
+
+ -2.82
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col22" class="data row1 col22">
+
+ 2.26
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col23" class="data row1 col23">
+
+ 0.78
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col24" class="data row1 col24">
+
+ 0.44
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row2">
+
+ 2
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col0" class="data row2 col0">
+
+ -0.65
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col1" class="data row2 col1">
+
+ 3.22
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col2" class="data row2 col2">
+
+ -1.76
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col3" class="data row2 col3">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col22" class="data row16 col22">
+
+ 2.07
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col23" class="data row16 col23">
+
+ 6.16
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col24" class="data row16 col24">
+
+ 4.94
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row17">
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+ 4.57
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col2" class="data row17 col2">
+
+ -3.53
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col3" class="data row17 col3">
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+ -3.76
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col4" class="data row17 col4">
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+ 6.58
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col5" class="data row17 col5">
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+ -2.58
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+
+ -0.75
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+ 3.63
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col10" class="data row17 col10">
+
+ -0.29
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col11" class="data row17 col11">
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+ 0.56
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col12" class="data row17 col12">
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+ 5.76
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col14" class="data row17 col14">
+
+ -2.27
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col15" class="data row17 col15">
+
+ -2.31
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col16" class="data row17 col16">
+
+ -4.95
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col17" class="data row17 col17">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col18" class="data row17 col18">
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+ -3.06
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col19" class="data row17 col19">
+
+ -2.43
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col20" class="data row17 col20">
+
+ 0.84
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col21" class="data row17 col21">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col22" class="data row17 col22">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col23" class="data row17 col23">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col24" class="data row17 col24">
+
+ 4.7
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+
+ </tr>
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+ <tr>
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+ <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row18">
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+ 5.99
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col1" class="data row18 col1">
+
+ 5.82
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col2" class="data row18 col2">
+
+ -2.85
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col3" class="data row18 col3">
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col4" class="data row18 col4">
+
+ 7.12
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col5" class="data row18 col5">
+
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col6" class="data row18 col6">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col7" class="data row18 col7">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col10" class="data row18 col10">
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+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col14" class="data row18 col14">
+
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col15" class="data row18 col15">
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col16" class="data row18 col16">
+
+ -4.23
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col17" class="data row18 col17">
+
+ -2.55
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col18" class="data row18 col18">
+
+ -2.46
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col19" class="data row18 col19">
+
+ -2.89
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col20" class="data row18 col20">
+
+ 1.9
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col21" class="data row18 col21">
+
+ -9.74
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col22" class="data row18 col22">
+
+ 3.43
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col23" class="data row18 col23">
+
+ 7.07
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col24" class="data row18 col24">
+
+ 4.39
+
+
+ </tr>
+
+ <tr>
+
+ <th id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb" class="row_heading level24 row19">
+
+ 19
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col0" class="data row19 col0">
+
+ 4.03
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col1" class="data row19 col1">
+
+ 6.23
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col2" class="data row19 col2">
+
+ -4.1
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col3" class="data row19 col3">
+
+ -4.11
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col4" class="data row19 col4">
+
+ 7.19
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col5" class="data row19 col5">
+
+ -4.1
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col6" class="data row19 col6">
+
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col7" class="data row19 col7">
+
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col8" class="data row19 col8">
+
+ -5.21
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col9" class="data row19 col9">
+
+ -0.24
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+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col10" class="data row19 col10">
+
+ 0.01
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col11" class="data row19 col11">
+
+ 1.16
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col12" class="data row19 col12">
+
+ 6.43
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13" class="data row19 col13">
+
+ -1.97
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14" class="data row19 col14">
+
+ -2.64
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15" class="data row19 col15">
+
+ -1.66
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16" class="data row19 col16">
+
+ -5.2
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17" class="data row19 col17">
+
+ -3.25
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18" class="data row19 col18">
+
+ -2.87
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19" class="data row19 col19">
+
+ -1.65
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20" class="data row19 col20">
+
+ 1.64
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21" class="data row19 col21">
+
+ -10.66
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22" class="data row19 col22">
+
+ 2.83
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23" class="data row19 col23">
+
+ 7.48
+
+
+ <td id="T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24" class="data row19 col24">
+
+ 3.94
+
+
+ </tr>
+
+ </tbody>
+ </table>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Extensibility">Extensibility<a class="anchor-link" href="#Extensibility">¶</a></h1><p>The core of pandas is, and will remain, its "high-performance, easy-to-use data structures".
+With that in mind, we hope that <code>DataFrame.style</code> accomplishes two goals</p>
+<ul>
+<li>Provide an API that is pleasing to use interactively and is "good enough" for many tasks</li>
+<li>Provide the foundations for dedicated libraries to build on</li>
+</ul>
+<p>If you build a great library on top of this, let us know and we'll <a href="http://pandas.pydata.org/pandas-docs/stable/ecosystem.html">link</a> to it.</p>
+<h2 id="Subclassing">Subclassing<a class="anchor-link" href="#Subclassing">¶</a></h2><p>This section contains a bit of information about the implementation of <code>Styler</code>.
+Since the feature is so new all of this is subject to change, even more so than the end-use API.</p>
+<p>As users apply styles (via <code>.apply</code>, <code>.applymap</code> or one of the builtins), we don't actually calculate anything.
+Instead, we append functions and arguments to a list <code>self._todo</code>.
+When asked (typically in <code>.render</code> we'll walk through the list and execute each function (this is in <code>self._compute()</code>.
+These functions update an internal <code>defaultdict(list)</code>, <code>self.ctx</code> which maps DataFrame row / column positions to CSS attribute, value pairs.</p>
+<p>We take the extra step through <code>self._todo</code> so that we can export styles and set them on other <code>Styler</code>s.</p>
+<p>Rendering uses <a href="http://jinja.pocoo.org/">Jinja</a> templates.
+The <code>.translate</code> method takes <code>self.ctx</code> and builds another dictionary ready to be passed into <code>Styler.template.render</code>, the Jinja template.</p>
+<h2 id="Alternate-templates">Alternate templates<a class="anchor-link" href="#Alternate-templates">¶</a></h2><p>We've used <a href="http://jinja.pocoo.org/">Jinja</a> templates to build up the HTML.
+The template is stored as a class variable <code>Styler.template.</code>. Subclasses can override that.</p>
+<div class="highlight"><pre><span class="k">class</span> <span class="nc">CustomStyle</span><span class="p">(</span><span class="n">Styler</span><span class="p">):</span>
+ <span class="n">template</span> <span class="o">=</span> <span class="n">Template</span><span class="p">(</span><span class="s">"""..."""</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+ </div>
+ </div>
+</body>
+</html>
diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb
new file mode 100644
index 0000000000000..28eb1cd09bacd
--- /dev/null
+++ b/doc/source/html-styling.ipynb
@@ -0,0 +1,21062 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Conditional Formatting\n",
+ "\n",
+ "*New in version 0.17.1*\n",
+ "\n",
+ "<p style=\"color: red\">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your [feedback](https://github.com/pydata/pandas/issues).*<p style=\"color: red\">\n",
+ "\n",
+ "You can apply **conditional formatting**, the visual styling of a DataFrame\n",
+ "depending on the data within, by using the ``DataFrame.style`` property.\n",
+ "This is a property that returns a ``pandas.Styler`` object, which has\n",
+ "useful methods for formatting and displaying DataFrames.\n",
+ "\n",
+ "The styling is accomplished using CSS.\n",
+ "You write \"style functions\" that take scalars, `DataFrame`s or `Series`, and return *like-indexed* DataFrames or Series with CSS `\"attribute: value\"` pairs for the values.\n",
+ "These functions can be incrementally passed to the `Styler` which collects the styles before rendering.\n",
+ "\n",
+ "### Contents\n",
+ "\n",
+ "- [Building Styles](#Building-Styles)\n",
+ "- [Finer Control: Slicing](#Finer-Control:-Slicing)\n",
+ "- [Builtin Styles](#Builtin-Styles)\n",
+ "- [Other options](#Other-options)\n",
+ "- [Sharing Styles](#Sharing-Styles)\n",
+ "- [Limitations](#Limitations)\n",
+ "- [Terms](#Terms)\n",
+ "- [Extensibility](#Extensibility)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Building Styles\n",
+ "\n",
+ "Pass your style functions into one of the following methods:\n",
+ "\n",
+ "- `Styler.applymap`: elementwise\n",
+ "- `Styler.apply`: column-/row-/table-wise\n",
+ "\n",
+ "Both of those methods take a function (and some other keyword arguments) and applies your function to the DataFrame in a certain way.\n",
+ "`Styler.applymap` works through the DataFrame elementwise.\n",
+ "`Styler.apply` passes each column or row into your DataFrame one-at-a-time or the entire table at once, depending on the `axis` keyword argument.\n",
+ "For columnwise use `axis=0`, rowwise use `axis=1`, and for the entire table at once use `axis=None`.\n",
+ "\n",
+ "The result of the function application, a CSS attribute-value pair, is stored in an internal dictionary on your ``Styler`` object.\n",
+ "\n",
+ "Let's see some examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "np.random.seed(24)\n",
+ "df = pd.DataFrame({'A': np.linspace(1, 10, 10)})\n",
+ "df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],\n",
+ " axis=1)\n",
+ "df.iloc[0, 2] = np.nan"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's a boring example of rendering a DataFrame, without any (visible) styles:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7b9e9c2_8bd5_11e5_a332_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x10fc7a9b0>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Note*: The `DataFrame.style` attribute is a propetry that returns a `Styler` object. `Styler` has a `_repr_html_` method defined on it so they are rendered automatically. If you want the actual HTML back for further processing or for writing to file call the `.render()` method which returns a string.\n",
+ "\n",
+ "The above output looks very similar to the standard DataFrame HTML representation. But we've done some work behind the scenes to attach CSS classes to each cell. We can view these by calling the `.render` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['',\n",
+ " ' <style type=\"text/css\" >',\n",
+ " ' ',\n",
+ " ' ',\n",
+ " ' #T_e7c1f51a_8bd5_11e5_803e_a45e60bd97fbrow0_col2 {',\n",
+ " ' ',\n",
+ " ' background-color: red;',\n",
+ " ' ',\n",
+ " ' }',\n",
+ " ' ']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.highlight_null().render().split('\\n')[:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `row0_col2` is the identifier for that particular cell. We've also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn't collied with the styling from another within the same notebook or page (you can set the `uuid` if you'd like to tie together the styling of two DataFrames).\n",
+ "\n",
+ "When writing style functions, you take care of producing the CSS attribute / value pairs you want. Pandas matches those up with the CSS classes that identify each cell."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's write a simple style function that will color negative numbers red and positive numbers black."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def color_negative_red(val):\n",
+ " \"\"\"\n",
+ " Takes a scalar and returns a string with\n",
+ " the css property `'color: red'` for negative\n",
+ " strings, black otherwise.\n",
+ " \"\"\"\n",
+ " color = 'red' if val < 0 else 'black'\n",
+ " return 'color: %s' % color"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, the cell's style depends only on it's own value.\n",
+ "That means we should use the `Styler.applymap` method which works elementwise."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7c86100_8bd5_11e5_98d6_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335eac8>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s = df.style.applymap(color_negative_red)\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice the similarity with the standard `df.applymap`, which operates on DataFrames elementwise. We want you to be able to resuse your existing knowledge of how to interact with DataFrames.\n",
+ "\n",
+ "Notice also that our function returned a string containing the CSS attribute and value, separated by a colon just like in a `<style>` tag. This will be a common theme."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now suppose you wanted to highlight the maximum value in each column.\n",
+ "We can't use `.applymap` anymore since that operated elementwise.\n",
+ "Instead, we'll turn to `.apply` which operates columnwise (or rowwise using the `axis` keyword). Later on we'll see that something like `highlight_max` is already defined on `Styler` so you wouldn't need to write this yourself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def highlight_max(s):\n",
+ " '''\n",
+ " highlight the maximum in a Series yellow.\n",
+ " '''\n",
+ " is_max = s == s.max()\n",
+ " return ['background-color: yellow' if v else '' for v in is_max]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7cef01a_8bd5_11e5_b697_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335eb70>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.apply(highlight_max)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We encourage you to use method chains to build up a style piecewise, before finally rending at the end of the chain."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
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+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
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+ " }\n",
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+ " \n",
+ " color: black;\n",
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+ " \n",
+ " : ;\n",
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+ " \n",
+ " color: black;\n",
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+ " \n",
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+ " \n",
+ " color: black;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
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+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
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+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7d5f8ee_8bd5_11e5_9f02_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e630>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.\\\n",
+ " applymap(color_negative_red).\\\n",
+ " apply(highlight_max)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Above we used `Styler.apply` to pass in each column one at a time.\n",
+ "\n",
+ "<p style=\"background-color: #DEDEBE\">*Debugging Tip*: If you're having trouble writing your style function, try just passing it into <code style=\"background-color: #DEDEBE\">df.apply</code>. <code style=\"background-color: #DEDEBE\">Styler.apply</code> uses that internally, so the result should be the same.</p>\n",
+ "\n",
+ "What if you wanted to highlight just the maximum value in the entire table?\n",
+ "Use `.apply(function, axis=None)` to indicate that your function wants the entire table, not one column or row at a time. Let's try that next.\n",
+ "\n",
+ "We'll rewrite our `highlight-max` to handle either Series (from `.apply(axis=0 or 1)`) or DataFrames (from `.apply(axis=None)`). We'll also allow the color to be adjustable, to demonstrate that `.apply`, and `.applymap` pass along keyword arguments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def highlight_max(data, color='yellow'):\n",
+ " '''\n",
+ " highlight the maximum in a Series or DataFrame\n",
+ " '''\n",
+ " attr = 'background-color: {}'.format(color)\n",
+ " if data.ndim == 1: # Series from .apply(axis=0) or axis=1\n",
+ " is_max = data == data.max()\n",
+ " return [attr if v else '' for v in is_max]\n",
+ " else: # from .apply(axis=None)\n",
+ " is_max = data == data.max().max()\n",
+ " return pd.DataFrame(np.where(is_max, attr, ''),\n",
+ " index=data.index, columns=data.columns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: darkorange;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
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+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
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+ " \n",
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+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
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+ " 0\n",
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+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
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+ " \n",
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+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
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+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
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+ " \n",
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+ " \n",
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+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
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+ " 2.0\n",
+ " \n",
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+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
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+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
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+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
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+ " \n",
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+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
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+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
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+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7dc6308_8bd5_11e5_9502_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x113367eb8>"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.apply(highlight_max, color='darkorange', axis=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Building Styles Summary\n",
+ "\n",
+ "Style functions should return strings with one or more CSS `attribute: value` delimited by semicolons. Use\n",
+ "\n",
+ "- `Styler.applymap(func)` for elementwise styles\n",
+ "- `Styler.apply(func, axis=0)` for columnwise styles\n",
+ "- `Styler.apply(func, axis=1)` for rowwise styles\n",
+ "- `Styler.apply(func, axis=None)` for tablewise styles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Finer Control: Slicing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Both `Styler.apply`, and `Styler.applymap` accept a `subset` keyword.\n",
+ "This allows you to apply styles to specific rows or columns, without having to code that logic into your `style` function.\n",
+ "\n",
+ "The value passed to `subset` behaves simlar to slicing a DataFrame.\n",
+ "\n",
+ "- A scalar is treated as a column label\n",
+ "- A list (or series or numpy array)\n",
+ "- A tuple is treated as `(row_indexer, column_indexer)`\n",
+ "\n",
+ "Consider using `pd.IndexSlice` to construct the tuple for the last one."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_e7e11b78_8bd5_11e5_8ada_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
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+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e978>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.apply(highlight_max, subset=['B', 'C', 'D'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For row and column slicing, any valid indexer to `.loc` will work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e48dba_8bd5_11e5_b1bc_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e940>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.applymap(color_negative_red,\n",
+ " subset=pd.IndexSlice[2:5, ['B', 'D']])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Only label-based slicing is supported right now, not positional.\n",
+ "\n",
+ "If your style function uses a `subset` or `axis` keyword argument, consider wrapping your function in a `functools.partial`, partialing out that keyword.\n",
+ "\n",
+ "```python\n",
+ "my_func2 = functools.partial(my_func, subset=42)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Builtin Styles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7e9e458_8bd5_11e5_816a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e2b0>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.highlight_null(null_color='red')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ "\n",
+ " <table id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\">\n",
+ " \n",
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+ " \n",
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+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
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+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
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+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
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+ " \n",
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+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f06b0a_8bd5_11e5_ab2c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e048>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "\n",
+ "cm = sns.light_palette(\"green\", as_cmap=True)\n",
+ "\n",
+ "s = df.style.background_gradient(cmap=cm)\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " background-color: #e5e419;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " background-color: #46327e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " background-color: #3b528b;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " background-color: #433e85;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " background-color: #bddf26;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4 {\n",
+ " \n",
+ " background-color: #25838e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0 {\n",
+ " \n",
+ " background-color: #21918c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2 {\n",
+ " \n",
+ " background-color: #35b779;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0 {\n",
+ " \n",
+ " background-color: #5ec962;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " background-color: #95d840;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2 {\n",
+ " \n",
+ " background-color: #26ad81;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " background-color: #2cb17e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " background-color: #1f9e89;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " background-color: #1f958b;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7f6e65e_8bd5_11e5_942d_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e9b0>"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Uses the full color range\n",
+ "df.loc[:4].style.background_gradient(cmap='viridis')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " background-color: #31688e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " background-color: #efe51c;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: #440154;\n",
+ " \n",
+ " background-color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " background-color: #277f8e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " background-color: #31688e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " background-color: #21918c;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " background-color: #25858e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " background-color: #31688e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " background-color: #d5e21a;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4 {\n",
+ " \n",
+ " background-color: #29af7f;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0 {\n",
+ " \n",
+ " background-color: #35b779;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1 {\n",
+ " \n",
+ " background-color: #31688e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2 {\n",
+ " \n",
+ " background-color: #6ccd5a;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0 {\n",
+ " \n",
+ " background-color: #90d743;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " background-color: #b8de29;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2 {\n",
+ " \n",
+ " background-color: #5ac864;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " background-color: #31688e;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " background-color: #63cb5f;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " background-color: #fde725;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " background-color: #46c06f;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " background-color: #3bbb75;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e7fdeae4_8bd5_11e5_b0ba_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x113367ba8>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Compreess the color range\n",
+ "(df.loc[:4]\n",
+ " .style\n",
+ " .background_gradient(cmap='viridis', low=.5, high=0)\n",
+ " .highlight_null('red'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can include \"bar charts\" in your DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
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+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);\n",
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+ " }\n",
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+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);\n",
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+ " }\n",
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+ " \n",
+ " width: 10em;\n",
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+ " \n",
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+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 22.22222222222222%, transparent 0%);\n",
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+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);\n",
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+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 33.333333333333336%, transparent 0%);\n",
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+ " }\n",
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+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 78.1150827834652%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 44.44444444444444%, transparent 0%);\n",
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+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
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+ " background: linear-gradient(90deg,#d65f5f 92.96229618327422%, transparent 0%);\n",
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+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 55.55555555555556%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 8.737388253449494%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 66.66666666666667%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 52.764243600289866%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 77.77777777777777%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 59.79187201238315%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 88.88888888888889%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 100.00000000000001%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " width: 10em;\n",
+ " \n",
+ " height: 80%;\n",
+ " \n",
+ " background: linear-gradient(90deg,#d65f5f 45.17326030334935%, transparent 0%);\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e802cdae_8bd5_11e5_acc8_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
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+ " \n",
+ " </tbody>\n",
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+ {
+ "cell_type": "markdown",
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+ "There's also `.highlight_min` and `.highlight_max`."
+ ]
+ },
+ {
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+ " \n",
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+ " \n",
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+ " \n",
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+ " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
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+ " <td id=\"T_e81733fe_8bd5_11e5_871a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
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+ " \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e390>"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.highlight_min(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use `Styler.set_properties` when the style doesn't actually depend on the values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " color: lawngreen;\n",
+ " \n",
+ " background-color: black;\n",
+ " \n",
+ " border-color: white;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e81d9d50_8bd5_11e5_a344_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x113367ef0>"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.set_properties(**{'background-color': 'black',\n",
+ " 'color': 'lawngreen',\n",
+ " 'border-color': 'white'})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sharing Styles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with `df1.style.export`, and import it on the second DataFrame with `df1.style.set`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ " \n",
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+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col0 {\n",
+ " \n",
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+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " color: black;\n",
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+ " \n",
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+ " }\n",
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+ " \n",
+ " color: black;\n",
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+ " }\n",
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+ " \n",
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+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
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+ " }\n",
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+ " \n",
+ " color: black;\n",
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+ " }\n",
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+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
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+ " \n",
+ " color: black;\n",
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+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
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+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
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+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
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+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " color: black;\n",
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+ " }\n",
+ " \n",
+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " }\n",
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+ " #T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
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+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
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+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8233c92_8bd5_11e5_aa43_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
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+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x1133670f0>"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "df2 = -df\n",
+ "style1 = df.style.applymap(color_negative_red)\n",
+ "style1"
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+ "collapsed": false
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+ " \n",
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+ " <td id=\"T_e8287d92_8bd5_11e5_ad98_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
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+ " \n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x113367470>"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "style2 = df2.style\n",
+ "style2.use(style1.export())\n",
+ "style2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Other options\n",
+ "\n",
+ "You've seen a few methods for data-driven styling.\n",
+ "`Styler` also provides a few other options for styles that don't depend on the data.\n",
+ "\n",
+ "- precision\n",
+ "- captions\n",
+ "- table-wide styles\n",
+ "\n",
+ "Each of these can be specified in two ways:\n",
+ "\n",
+ "- A keyword argument to `pandas.core.Styler`\n",
+ "- A call to one of the `.set_` methods, e.g. `.set_caption`\n",
+ "\n",
+ "The best method to use depends on the context. Use the `Styler` constructor when building many styled DataFrames that should all share the same properties. For interactive use, the`.set_` methods are more convenient."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Precision"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can control the precision of floats using pandas' regular `display.precision` option."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
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+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.32\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.07\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.3\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.89\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.96\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.48\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.85\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.52\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.06\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.12\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.63\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.52\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.69\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.09\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e83075ae_8bd5_11e5_88d4_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x1133679b0>"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "with pd.option_context('display.precision', 2):\n",
+ " html = (df.style\n",
+ " .applymap(color_negative_red)\n",
+ " .apply(highlight_max))\n",
+ "html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or through a `set_precision` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " background-color: yellow;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " color: red;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " color: black;\n",
+ " \n",
+ " : ;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.32\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.07\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.3\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.89\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.96\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.48\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.85\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.52\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.06\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.12\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.63\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.52\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.69\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.09\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8394654_8bd5_11e5_b698_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11338e3c8>"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style\\\n",
+ " .applymap(color_negative_red)\\\n",
+ " .apply(highlight_max)\\\n",
+ " .set_precision(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use `df.round(2).style` if you'd prefer to round from the start."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Captions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Regular table captions can be added in a few ways."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ " \n",
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+ " \n",
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+ " \n",
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+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3 {\n",
+ " \n",
+ " background-color: #cff3cf;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4 {\n",
+ " \n",
+ " background-color: #4fac4f;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0 {\n",
+ " \n",
+ " background-color: #198e19;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2 {\n",
+ " \n",
+ " background-color: #dcfadc;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: #008000;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " background-color: #7ec67e;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " background-color: #319b31;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3 {\n",
+ " \n",
+ " background-color: #e5ffe5;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " background-color: #5cb35c;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\">\n",
+ " \n",
+ " <caption>Colormaps, with a caption.</caption>\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8430ee4_8bd5_11e5_9d59_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x113367978>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.style.set_caption('Colormaps, with a caption.')\\\n",
+ " .background_gradient(cmap=cm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Table Styles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The next option you have are \"table styles\".\n",
+ "These are styles that apply to the table as a whole, but don't look at the data.\n",
+ "Certain sytlings, including pseudo-selectors like `:hover` can only be used this way."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb tr:hover {\n",
+ " \n",
+ " background-color: #ffff99;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb th {\n",
+ " \n",
+ " font-size: 150%;\n",
+ " \n",
+ " text-align: center;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8482f22_8bd5_11e5_9937_a45e60bd97fb caption {\n",
+ " \n",
+ " caption-side: bottom;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\">\n",
+ " \n",
+ " <caption>Hover to highlight.</caption>\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8482f22_8bd5_11e5_9937_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11338e4a8>"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import HTML\n",
+ "\n",
+ "def hover(hover_color=\"#ffff99\"):\n",
+ " return dict(selector=\"tr:hover\",\n",
+ " props=[(\"background-color\", \"%s\" % hover_color)])\n",
+ "\n",
+ "styles = [\n",
+ " hover(),\n",
+ " dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n",
+ " (\"text-align\", \"center\")]),\n",
+ " dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])\n",
+ "]\n",
+ "html = (df.style.set_table_styles(styles)\n",
+ " .set_caption(\"Hover to highlight.\"))\n",
+ "html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`table_styles` should be a list of dictionaries.\n",
+ "Each dictionary should have the `selector` and `props` keys.\n",
+ "The value for `selector` should be a valid CSS selector.\n",
+ "Recall that all the styles are already attached to an `id`, unique to\n",
+ "each `Styler`. This selector is in addition to that `id`.\n",
+ "The value for `props` should be a list of tuples of `('attribute', 'value')`.\n",
+ "\n",
+ "`table_styles` are extremely flexible, but not as fun to type out by hand.\n",
+ "We hope to collect some useful ones either in pandas, or preferable in a new package that [builds on top](#Extensibility) the tools here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Limitations\n",
+ "\n",
+ "- DataFrame only `(use Series.to_frame().style)`\n",
+ "- The index and columns must be unique\n",
+ "- No large repr, and performance isn't great; this is intended for summary DataFrames\n",
+ "- You can only style the *values*, not the index or columns\n",
+ "- You can only apply styles, you can't insert new HTML entities\n",
+ "\n",
+ "Some of these will be addressed in the future.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Terms\n",
+ "\n",
+ "- Style function: a function that's passed into `Styler.apply` or `Styler.applymap` and returns values like `'css attribute: value'`\n",
+ "- Builtin style functions: style functions that are methods on `Styler`\n",
+ "- table style: a dictionary with the two keys `selector` and `props`. `selector` is the CSS selector that `props` will apply to. `props` is a list of `(attribute, value)` tuples. A list of table styles passed into `Styler`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fun stuff\n",
+ "\n",
+ "Here are a few interesting examples.\n",
+ "\n",
+ "`Styler` interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
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+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3 {\n",
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+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4 {\n",
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+ " background-color: #c5d4d2;\n",
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+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0 {\n",
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+ " background-color: #799995;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1 {\n",
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+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2 {\n",
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+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3 {\n",
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+ " background-color: #557e79;\n",
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+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: #557e79;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1 {\n",
+ " \n",
+ " background-color: #e7eeed;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2 {\n",
+ " \n",
+ " background-color: #9bb4b0;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3 {\n",
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+ " background-color: #557e79;\n",
+ " \n",
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+ " \n",
+ " #T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4 {\n",
+ " \n",
+ " background-color: #d7e2e0;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\">\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">A\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">B\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">C\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">D\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">E\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 1.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.329212\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " nan\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.31628\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.99081\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " 2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " -1.070816\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.438713\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " 0.564417\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 0.295722\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " -1.626404\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " 0.219565\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.678805\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 1.889273\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " 4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 0.961538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " 0.104011\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " -0.481165\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 0.850229\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " 5.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 1.453425\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " 1.057737\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " 0.165562\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 0.515018\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " 6.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " -1.336936\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " 0.562861\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " 1.392855\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " -0.063328\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " 7.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 0.121668\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " 1.207603\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -0.00204\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 1.627796\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " 8.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 0.354493\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " 1.037528\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.385684\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 0.519818\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 9.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 1.686583\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -1.325963\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " 1.428984\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " -2.089354\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fb\" class=\"row_heading level4 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 10.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " -0.12982\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " 0.631523\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.586538\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8588302_8bd5_11e5_aa89_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 0.29072\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11338e0f0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.html import widgets\n",
+ "@widgets.interact\n",
+ "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n",
+ " return df.style.background_gradient(\n",
+ " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n",
+ " as_cmap=True)\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def magnify():\n",
+ " return [dict(selector=\"th\",\n",
+ " props=[(\"font-size\", \"4pt\")]),\n",
+ " dict(selector=\"th:hover\",\n",
+ " props=[(\"font-size\", \"12pt\")]),\n",
+ " dict(selector=\"tr:hover td:hover\",\n",
+ " props=[('max-width', '200px'),\n",
+ " ('font-size', '12pt')])\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <style type=\"text/css\" >\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th {\n",
+ " \n",
+ " font-size: 4pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb th:hover {\n",
+ " \n",
+ " font-size: 12pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb tr:hover td:hover {\n",
+ " \n",
+ " max-width: 200px;\n",
+ " \n",
+ " font-size: 12pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0 {\n",
+ " \n",
+ " background-color: #ecf2f8;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1 {\n",
+ " \n",
+ " background-color: #b8cce5;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col2 {\n",
+ " \n",
+ " background-color: #efb1be;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col3 {\n",
+ " \n",
+ " background-color: #f3c2cc;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col4 {\n",
+ " \n",
+ " background-color: #eeaab7;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col5 {\n",
+ " \n",
+ " background-color: #f8dce2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col6 {\n",
+ " \n",
+ " background-color: #f2c1cb;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col7 {\n",
+ " \n",
+ " background-color: #83a6d2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col8 {\n",
+ " \n",
+ " background-color: #de5f79;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col9 {\n",
+ " \n",
+ " background-color: #c3d4e9;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col10 {\n",
+ " \n",
+ " background-color: #eeacba;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col11 {\n",
+ " \n",
+ " background-color: #f7dae0;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col12 {\n",
+ " \n",
+ " background-color: #6f98ca;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col13 {\n",
+ " \n",
+ " background-color: #e890a1;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col14 {\n",
+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col15 {\n",
+ " \n",
+ " background-color: #ea96a7;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col16 {\n",
+ " \n",
+ " background-color: #adc4e1;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col17 {\n",
+ " \n",
+ " background-color: #eca3b1;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col18 {\n",
+ " \n",
+ " background-color: #b7cbe5;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col19 {\n",
+ " \n",
+ " background-color: #f5ced6;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col20 {\n",
+ " \n",
+ " background-color: #6590c7;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col21 {\n",
+ " \n",
+ " background-color: #d73c5b;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col22 {\n",
+ " \n",
+ " background-color: #4479bb;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col23 {\n",
+ " \n",
+ " background-color: #cfddee;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col24 {\n",
+ " \n",
+ " background-color: #e58094;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col0 {\n",
+ " \n",
+ " background-color: #e4798e;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col1 {\n",
+ " \n",
+ " background-color: #aec5e1;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col2 {\n",
+ " \n",
+ " background-color: #eb9ead;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col3 {\n",
+ " \n",
+ " background-color: #ec9faf;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col4 {\n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f2c1cb;\n",
+ " \n",
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+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #b1c7e2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
+ " background-color: #d4e0ef;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
+ " background-color: #c1d3e8;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col13 {\n",
+ " \n",
+ " background-color: #cedced;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #e7899c;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col15 {\n",
+ " \n",
+ " background-color: #eeacba;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #e2eaf4;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col17 {\n",
+ " \n",
+ " background-color: #f7d6dd;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col18 {\n",
+ " \n",
+ " background-color: #a8c0df;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col19 {\n",
+ " \n",
+ " background-color: #f5ccd4;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #fae4e9;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f3c2cc;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f2c1cb;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col10 {\n",
+ " \n",
+ " background-color: #cbdaec;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " background-color: #c5d5ea;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col12 {\n",
+ " \n",
+ " background-color: #fae6ea;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col13 {\n",
+ " \n",
+ " background-color: #c6d6ea;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col14 {\n",
+ " \n",
+ " background-color: #eca3b1;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " background-color: #fae4e9;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
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+ " max-width: 80px;\n",
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+ " font-size: 1pt;\n",
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+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #f6d1d8;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #a3bddd;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #d73c5b;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
+ " background-color: #81a4d1;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f4c9d2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " background-color: #628fc6;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
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+ " \n",
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+ " \n",
+ " background-color: #f5ccd4;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #eaf0f7;\n",
+ " \n",
+ " max-width: 80px;\n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " max-width: 80px;\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
+ " background-color: #4479bb;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " background-color: #f3c5ce;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " background-color: #4d7fbe;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #e994a5;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #f6d5db;\n",
+ " \n",
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+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f5ccd4;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #d5e1f0;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #f0b4c0;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #da4966;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #d73c5b;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #ea96a7;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #ecf2f8;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
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+ " \n",
+ " background-color: #e3748a;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col20 {\n",
+ " \n",
+ " background-color: #dde7f3;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col21 {\n",
+ " \n",
+ " background-color: #dc516d;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col22 {\n",
+ " \n",
+ " background-color: #91b0d7;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col23 {\n",
+ " \n",
+ " background-color: #a8c0df;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col24 {\n",
+ " \n",
+ " background-color: #5c8ac4;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col0 {\n",
+ " \n",
+ " background-color: #dce6f2;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #a9c1e0;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #efb2bf;\n",
+ " \n",
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+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " background-color: #f9e3e7;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #5e8bc4;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
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+ " \n",
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+ " \n",
+ " background-color: #f2bfca;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
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+ " \n",
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+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col6 {\n",
+ " \n",
+ " background-color: #fae6ea;\n",
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+ " \n",
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+ " \n",
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+ " \n",
+ " background-color: #5787c2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13 {\n",
+ " \n",
+ " background-color: #fbeaed;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14 {\n",
+ " \n",
+ " background-color: #f8dee3;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15 {\n",
+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16 {\n",
+ " \n",
+ " background-color: #eeaab7;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17 {\n",
+ " \n",
+ " background-color: #f6d1d8;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18 {\n",
+ " \n",
+ " background-color: #f7d7de;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19 {\n",
+ " \n",
+ " background-color: #f2f2f2;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20 {\n",
+ " \n",
+ " background-color: #b5cae4;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21 {\n",
+ " \n",
+ " background-color: #d73c5b;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22 {\n",
+ " \n",
+ " background-color: #9eb9db;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23 {\n",
+ " \n",
+ " background-color: #4479bb;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " #T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24 {\n",
+ " \n",
+ " background-color: #89aad4;\n",
+ " \n",
+ " max-width: 80px;\n",
+ " \n",
+ " font-size: 1pt;\n",
+ " \n",
+ " }\n",
+ " \n",
+ " </style>\n",
+ "\n",
+ " <table id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\">\n",
+ " \n",
+ " <caption>Hover to magify</caption>\n",
+ " \n",
+ "\n",
+ " <thead>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th class=\"blank\">\n",
+ " \n",
+ " <th class=\"col_heading level0 col0\">0\n",
+ " \n",
+ " <th class=\"col_heading level0 col1\">1\n",
+ " \n",
+ " <th class=\"col_heading level0 col2\">2\n",
+ " \n",
+ " <th class=\"col_heading level0 col3\">3\n",
+ " \n",
+ " <th class=\"col_heading level0 col4\">4\n",
+ " \n",
+ " <th class=\"col_heading level0 col5\">5\n",
+ " \n",
+ " <th class=\"col_heading level0 col6\">6\n",
+ " \n",
+ " <th class=\"col_heading level0 col7\">7\n",
+ " \n",
+ " <th class=\"col_heading level0 col8\">8\n",
+ " \n",
+ " <th class=\"col_heading level0 col9\">9\n",
+ " \n",
+ " <th class=\"col_heading level0 col10\">10\n",
+ " \n",
+ " <th class=\"col_heading level0 col11\">11\n",
+ " \n",
+ " <th class=\"col_heading level0 col12\">12\n",
+ " \n",
+ " <th class=\"col_heading level0 col13\">13\n",
+ " \n",
+ " <th class=\"col_heading level0 col14\">14\n",
+ " \n",
+ " <th class=\"col_heading level0 col15\">15\n",
+ " \n",
+ " <th class=\"col_heading level0 col16\">16\n",
+ " \n",
+ " <th class=\"col_heading level0 col17\">17\n",
+ " \n",
+ " <th class=\"col_heading level0 col18\">18\n",
+ " \n",
+ " <th class=\"col_heading level0 col19\">19\n",
+ " \n",
+ " <th class=\"col_heading level0 col20\">20\n",
+ " \n",
+ " <th class=\"col_heading level0 col21\">21\n",
+ " \n",
+ " <th class=\"col_heading level0 col22\">22\n",
+ " \n",
+ " <th class=\"col_heading level0 col23\">23\n",
+ " \n",
+ " <th class=\"col_heading level0 col24\">24\n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row0\">\n",
+ " \n",
+ " 0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n",
+ " \n",
+ " 0.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n",
+ " \n",
+ " 1.03\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n",
+ " \n",
+ " -0.84\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n",
+ " \n",
+ " -0.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n",
+ " \n",
+ " -0.96\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n",
+ " \n",
+ " -0.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n",
+ " \n",
+ " -0.62\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n",
+ " \n",
+ " 1.84\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n",
+ " \n",
+ " -2.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n",
+ " \n",
+ " 0.87\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n",
+ " \n",
+ " -0.92\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n",
+ " \n",
+ " -0.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n",
+ " \n",
+ " 2.15\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n",
+ " \n",
+ " -1.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n",
+ " \n",
+ " 0.08\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n",
+ " \n",
+ " -1.25\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n",
+ " \n",
+ " 1.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n",
+ " \n",
+ " -1.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n",
+ " \n",
+ " 1.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n",
+ " \n",
+ " -0.42\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n",
+ " \n",
+ " 2.29\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n",
+ " \n",
+ " -2.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n",
+ " \n",
+ " 2.82\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n",
+ " \n",
+ " 0.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n",
+ " \n",
+ " -1.58\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row1\">\n",
+ " \n",
+ " 1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n",
+ " \n",
+ " -1.75\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n",
+ " \n",
+ " 1.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n",
+ " \n",
+ " -1.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n",
+ " \n",
+ " -1.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n",
+ " \n",
+ " 1.03\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n",
+ " \n",
+ " 0.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n",
+ " \n",
+ " -2.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n",
+ " \n",
+ " 3.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n",
+ " \n",
+ " -1.66\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n",
+ " \n",
+ " 1.27\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n",
+ " \n",
+ " -0.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n",
+ " \n",
+ " -0.02\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n",
+ " \n",
+ " 1.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n",
+ " \n",
+ " -1.09\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n",
+ " \n",
+ " -1.86\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n",
+ " \n",
+ " -1.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n",
+ " \n",
+ " -0.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n",
+ " \n",
+ " -0.81\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n",
+ " \n",
+ " 0.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n",
+ " \n",
+ " -0.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n",
+ " \n",
+ " 1.79\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n",
+ " \n",
+ " -2.82\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n",
+ " \n",
+ " 2.26\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n",
+ " \n",
+ " 0.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n",
+ " \n",
+ " 0.44\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row2\">\n",
+ " \n",
+ " 2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n",
+ " \n",
+ " -0.65\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n",
+ " \n",
+ " 3.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n",
+ " \n",
+ " -1.76\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n",
+ " \n",
+ " 0.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n",
+ " \n",
+ " 2.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n",
+ " \n",
+ " -0.37\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n",
+ " \n",
+ " -3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n",
+ " \n",
+ " 3.73\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n",
+ " \n",
+ " -1.87\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n",
+ " \n",
+ " 2.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n",
+ " \n",
+ " 0.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n",
+ " \n",
+ " -0.24\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n",
+ " \n",
+ " -0.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n",
+ " \n",
+ " -0.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n",
+ " \n",
+ " -3.02\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n",
+ " \n",
+ " -0.82\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n",
+ " \n",
+ " -0.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n",
+ " \n",
+ " -0.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n",
+ " \n",
+ " 0.86\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n",
+ " \n",
+ " -0.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col20\" class=\"data row2 col20\">\n",
+ " \n",
+ " 1.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col21\" class=\"data row2 col21\">\n",
+ " \n",
+ " -4.89\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col22\" class=\"data row2 col22\">\n",
+ " \n",
+ " 3.03\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col23\" class=\"data row2 col23\">\n",
+ " \n",
+ " 1.91\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow2_col24\" class=\"data row2 col24\">\n",
+ " \n",
+ " 0.61\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row3\">\n",
+ " \n",
+ " 3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n",
+ " \n",
+ " -1.62\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n",
+ " \n",
+ " 3.71\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n",
+ " \n",
+ " -2.31\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n",
+ " \n",
+ " 0.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n",
+ " \n",
+ " 4.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col5\" class=\"data row3 col5\">\n",
+ " \n",
+ " -0.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col6\" class=\"data row3 col6\">\n",
+ " \n",
+ " -3.86\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col7\" class=\"data row3 col7\">\n",
+ " \n",
+ " 4.16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n",
+ " \n",
+ " -2.15\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n",
+ " \n",
+ " 1.08\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n",
+ " \n",
+ " 0.12\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n",
+ " \n",
+ " 0.6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n",
+ " \n",
+ " -0.89\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n",
+ " \n",
+ " 0.27\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n",
+ " \n",
+ " -3.67\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n",
+ " \n",
+ " -2.71\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n",
+ " \n",
+ " -0.31\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n",
+ " \n",
+ " -1.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n",
+ " \n",
+ " 1.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n",
+ " \n",
+ " -1.83\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n",
+ " \n",
+ " 0.91\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n",
+ " \n",
+ " -5.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n",
+ " \n",
+ " 2.81\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n",
+ " \n",
+ " 2.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n",
+ " \n",
+ " 0.28\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row4\">\n",
+ " \n",
+ " 4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n",
+ " \n",
+ " -3.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n",
+ " \n",
+ " 4.48\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n",
+ " \n",
+ " -1.86\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n",
+ " \n",
+ " -1.7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n",
+ " \n",
+ " 5.19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n",
+ " \n",
+ " -1.02\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n",
+ " \n",
+ " -3.81\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n",
+ " \n",
+ " 4.72\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n",
+ " \n",
+ " -0.72\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n",
+ " \n",
+ " 1.08\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n",
+ " \n",
+ " -0.18\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n",
+ " \n",
+ " 0.83\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n",
+ " \n",
+ " -0.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n",
+ " \n",
+ " -1.08\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n",
+ " \n",
+ " -4.27\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n",
+ " \n",
+ " -2.88\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n",
+ " \n",
+ " -0.97\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n",
+ " \n",
+ " -1.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n",
+ " \n",
+ " 1.53\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n",
+ " \n",
+ " -1.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n",
+ " \n",
+ " 2.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n",
+ " \n",
+ " -6.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n",
+ " \n",
+ " 3.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n",
+ " \n",
+ " 2.49\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n",
+ " \n",
+ " 2.09\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row5\">\n",
+ " \n",
+ " 5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n",
+ " \n",
+ " -0.84\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n",
+ " \n",
+ " 4.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n",
+ " \n",
+ " -1.65\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n",
+ " \n",
+ " -2.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n",
+ " \n",
+ " 5.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n",
+ " \n",
+ " -0.99\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n",
+ " \n",
+ " -4.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n",
+ " \n",
+ " 3.94\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n",
+ " \n",
+ " -1.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n",
+ " \n",
+ " -0.94\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n",
+ " \n",
+ " 1.24\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n",
+ " \n",
+ " 0.09\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n",
+ " \n",
+ " -1.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n",
+ " \n",
+ " -0.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n",
+ " \n",
+ " -4.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n",
+ " \n",
+ " -0.85\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n",
+ " \n",
+ " -2.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n",
+ " \n",
+ " -1.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n",
+ " \n",
+ " 0.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n",
+ " \n",
+ " -1.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n",
+ " \n",
+ " 1.54\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n",
+ " \n",
+ " -6.51\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n",
+ " \n",
+ " 2.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n",
+ " \n",
+ " 2.14\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n",
+ " \n",
+ " 3.77\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row6\">\n",
+ " \n",
+ " 6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n",
+ " \n",
+ " -0.74\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n",
+ " \n",
+ " 5.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n",
+ " \n",
+ " -2.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n",
+ " \n",
+ " -1.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n",
+ " \n",
+ " 4.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n",
+ " \n",
+ " -1.85\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n",
+ " \n",
+ " -3.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n",
+ " \n",
+ " 3.76\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n",
+ " \n",
+ " -3.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n",
+ " \n",
+ " -1.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n",
+ " \n",
+ " 0.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n",
+ " \n",
+ " 0.57\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n",
+ " \n",
+ " -1.82\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n",
+ " \n",
+ " 0.54\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n",
+ " \n",
+ " -4.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n",
+ " \n",
+ " -1.83\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n",
+ " \n",
+ " -4.03\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col17\" class=\"data row6 col17\">\n",
+ " \n",
+ " -2.62\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col18\" class=\"data row6 col18\">\n",
+ " \n",
+ " -0.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col19\" class=\"data row6 col19\">\n",
+ " \n",
+ " -4.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col20\" class=\"data row6 col20\">\n",
+ " \n",
+ " 1.93\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col21\" class=\"data row6 col21\">\n",
+ " \n",
+ " -8.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col22\" class=\"data row6 col22\">\n",
+ " \n",
+ " 3.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col23\" class=\"data row6 col23\">\n",
+ " \n",
+ " 2.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n",
+ " \n",
+ " 5.81\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row7\">\n",
+ " \n",
+ " 7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n",
+ " \n",
+ " -0.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n",
+ " \n",
+ " 4.69\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n",
+ " \n",
+ " -2.3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n",
+ " \n",
+ " -0.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n",
+ " \n",
+ " 5.93\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n",
+ " \n",
+ " -2.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n",
+ " \n",
+ " -1.83\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n",
+ " \n",
+ " 5.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n",
+ " \n",
+ " -4.5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n",
+ " \n",
+ " -3.16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n",
+ " \n",
+ " -1.73\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n",
+ " \n",
+ " 0.18\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n",
+ " \n",
+ " 0.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n",
+ " \n",
+ " 0.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n",
+ " \n",
+ " -5.99\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n",
+ " \n",
+ " -0.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n",
+ " \n",
+ " -6.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n",
+ " \n",
+ " -3.89\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n",
+ " \n",
+ " 0.71\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n",
+ " \n",
+ " -3.95\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n",
+ " \n",
+ " 0.67\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n",
+ " \n",
+ " -7.26\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n",
+ " \n",
+ " 2.97\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n",
+ " \n",
+ " 3.39\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n",
+ " \n",
+ " 6.66\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row8\">\n",
+ " \n",
+ " 8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n",
+ " \n",
+ " 0.92\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n",
+ " \n",
+ " 5.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n",
+ " \n",
+ " -3.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n",
+ " \n",
+ " -0.65\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n",
+ " \n",
+ " 5.99\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n",
+ " \n",
+ " -3.19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n",
+ " \n",
+ " -1.83\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n",
+ " \n",
+ " 5.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n",
+ " \n",
+ " -3.53\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n",
+ " \n",
+ " -1.3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n",
+ " \n",
+ " -1.61\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n",
+ " \n",
+ " 0.82\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n",
+ " \n",
+ " -2.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n",
+ " \n",
+ " -0.4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n",
+ " \n",
+ " -6.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n",
+ " \n",
+ " -0.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n",
+ " \n",
+ " -6.6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n",
+ " \n",
+ " -3.48\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n",
+ " \n",
+ " -0.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n",
+ " \n",
+ " -4.6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n",
+ " \n",
+ " 0.51\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n",
+ " \n",
+ " -5.85\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n",
+ " \n",
+ " 3.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n",
+ " \n",
+ " 2.4\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n",
+ " \n",
+ " 5.08\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row9\">\n",
+ " \n",
+ " 9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n",
+ " \n",
+ " 0.38\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n",
+ " \n",
+ " 5.54\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n",
+ " \n",
+ " -4.49\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n",
+ " \n",
+ " -0.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n",
+ " \n",
+ " 7.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n",
+ " \n",
+ " -2.64\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n",
+ " \n",
+ " -0.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n",
+ " \n",
+ " 5.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n",
+ " \n",
+ " -1.96\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n",
+ " \n",
+ " -0.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n",
+ " \n",
+ " -0.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n",
+ " \n",
+ " 0.26\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n",
+ " \n",
+ " -3.37\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n",
+ " \n",
+ " -0.82\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n",
+ " \n",
+ " -6.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n",
+ " \n",
+ " -2.61\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n",
+ " \n",
+ " -8.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n",
+ " \n",
+ " -4.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n",
+ " \n",
+ " 0.41\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n",
+ " \n",
+ " -4.71\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n",
+ " \n",
+ " 1.89\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n",
+ " \n",
+ " -6.93\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n",
+ " \n",
+ " 2.14\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n",
+ " \n",
+ " 3.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n",
+ " \n",
+ " 5.16\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row10\">\n",
+ " \n",
+ " 10\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n",
+ " \n",
+ " 2.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n",
+ " \n",
+ " 5.84\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n",
+ " \n",
+ " -3.9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n",
+ " \n",
+ " -0.98\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n",
+ " \n",
+ " 7.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n",
+ " \n",
+ " -2.49\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n",
+ " \n",
+ " -0.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n",
+ " \n",
+ " 5.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n",
+ " \n",
+ " -2.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n",
+ " \n",
+ " -0.71\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n",
+ " \n",
+ " -0.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n",
+ " \n",
+ " 1.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n",
+ " \n",
+ " -2.79\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n",
+ " \n",
+ " 0.48\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n",
+ " \n",
+ " -5.97\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n",
+ " \n",
+ " -3.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n",
+ " \n",
+ " -7.77\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n",
+ " \n",
+ " -5.49\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n",
+ " \n",
+ " -0.7\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n",
+ " \n",
+ " -4.61\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n",
+ " \n",
+ " -0.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n",
+ " \n",
+ " -7.72\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n",
+ " \n",
+ " 1.54\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n",
+ " \n",
+ " 5.02\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n",
+ " \n",
+ " 5.81\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row11\">\n",
+ " \n",
+ " 11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n",
+ " \n",
+ " 1.86\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n",
+ " \n",
+ " 4.47\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n",
+ " \n",
+ " -2.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n",
+ " \n",
+ " -1.38\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n",
+ " \n",
+ " 5.9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n",
+ " \n",
+ " -0.49\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n",
+ " \n",
+ " 0.02\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n",
+ " \n",
+ " 5.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n",
+ " \n",
+ " -1.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n",
+ " \n",
+ " -0.6\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n",
+ " \n",
+ " 0.49\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n",
+ " \n",
+ " 1.96\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n",
+ " \n",
+ " -1.47\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n",
+ " \n",
+ " 1.88\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n",
+ " \n",
+ " -5.92\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n",
+ " \n",
+ " -4.55\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n",
+ " \n",
+ " -8.15\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n",
+ " \n",
+ " -3.42\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n",
+ " \n",
+ " -2.24\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n",
+ " \n",
+ " -4.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n",
+ " \n",
+ " -1.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n",
+ " \n",
+ " -7.9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n",
+ " \n",
+ " 1.36\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n",
+ " \n",
+ " 5.31\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n",
+ " \n",
+ " 5.83\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row12\">\n",
+ " \n",
+ " 12\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n",
+ " \n",
+ " 3.19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n",
+ " \n",
+ " 4.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n",
+ " \n",
+ " -3.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n",
+ " \n",
+ " -2.27\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n",
+ " \n",
+ " 5.93\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n",
+ " \n",
+ " -2.64\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n",
+ " \n",
+ " 0.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n",
+ " \n",
+ " 6.72\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n",
+ " \n",
+ " -2.84\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n",
+ " \n",
+ " -0.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n",
+ " \n",
+ " 1.89\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n",
+ " \n",
+ " 2.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n",
+ " \n",
+ " -1.53\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n",
+ " \n",
+ " 0.75\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n",
+ " \n",
+ " -5.27\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n",
+ " \n",
+ " -4.53\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n",
+ " \n",
+ " -7.57\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n",
+ " \n",
+ " -2.85\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n",
+ " \n",
+ " -2.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n",
+ " \n",
+ " -4.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n",
+ " \n",
+ " -1.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n",
+ " \n",
+ " -8.99\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n",
+ " \n",
+ " 2.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n",
+ " \n",
+ " 6.42\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n",
+ " \n",
+ " 5.6\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row13\">\n",
+ " \n",
+ " 13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n",
+ " \n",
+ " 2.31\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n",
+ " \n",
+ " 4.45\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n",
+ " \n",
+ " -3.87\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n",
+ " \n",
+ " -2.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n",
+ " \n",
+ " 6.76\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n",
+ " \n",
+ " -3.25\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n",
+ " \n",
+ " -2.17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n",
+ " \n",
+ " 7.99\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n",
+ " \n",
+ " -2.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n",
+ " \n",
+ " -0.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n",
+ " \n",
+ " 0.71\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n",
+ " \n",
+ " 2.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n",
+ " \n",
+ " -0.16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n",
+ " \n",
+ " -0.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n",
+ " \n",
+ " -5.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n",
+ " \n",
+ " -3.79\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n",
+ " \n",
+ " -7.58\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n",
+ " \n",
+ " -4.0\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n",
+ " \n",
+ " 0.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n",
+ " \n",
+ " -3.67\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n",
+ " \n",
+ " -1.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n",
+ " \n",
+ " -8.71\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n",
+ " \n",
+ " 2.47\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n",
+ " \n",
+ " 5.87\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n",
+ " \n",
+ " 6.71\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row14\">\n",
+ " \n",
+ " 14\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n",
+ " \n",
+ " 3.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n",
+ " \n",
+ " 4.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n",
+ " \n",
+ " -3.88\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n",
+ " \n",
+ " -1.58\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n",
+ " \n",
+ " 6.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n",
+ " \n",
+ " -3.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n",
+ " \n",
+ " -1.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n",
+ " \n",
+ " 5.57\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n",
+ " \n",
+ " -2.93\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n",
+ " \n",
+ " -0.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n",
+ " \n",
+ " -0.97\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n",
+ " \n",
+ " 1.72\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n",
+ " \n",
+ " 3.61\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n",
+ " \n",
+ " 0.29\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n",
+ " \n",
+ " -4.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n",
+ " \n",
+ " -4.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n",
+ " \n",
+ " -6.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n",
+ " \n",
+ " -4.5\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n",
+ " \n",
+ " -2.19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n",
+ " \n",
+ " -2.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n",
+ " \n",
+ " -1.64\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n",
+ " \n",
+ " -9.36\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n",
+ " \n",
+ " 3.36\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n",
+ " \n",
+ " 6.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n",
+ " \n",
+ " 7.53\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row15\">\n",
+ " \n",
+ " 15\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n",
+ " \n",
+ " 5.64\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n",
+ " \n",
+ " 5.31\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n",
+ " \n",
+ " -3.98\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n",
+ " \n",
+ " -2.26\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n",
+ " \n",
+ " 5.91\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n",
+ " \n",
+ " -3.3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n",
+ " \n",
+ " -1.03\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n",
+ " \n",
+ " 5.68\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n",
+ " \n",
+ " -3.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n",
+ " \n",
+ " -0.33\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n",
+ " \n",
+ " -1.16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n",
+ " \n",
+ " 2.19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n",
+ " \n",
+ " 4.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n",
+ " \n",
+ " 1.01\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n",
+ " \n",
+ " -3.22\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n",
+ " \n",
+ " -4.31\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n",
+ " \n",
+ " -5.74\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n",
+ " \n",
+ " -4.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n",
+ " \n",
+ " -2.3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n",
+ " \n",
+ " -1.36\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n",
+ " \n",
+ " -1.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n",
+ " \n",
+ " -11.27\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n",
+ " \n",
+ " 2.59\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n",
+ " \n",
+ " 6.69\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n",
+ " \n",
+ " 5.91\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row16\">\n",
+ " \n",
+ " 16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n",
+ " \n",
+ " 4.08\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n",
+ " \n",
+ " 4.34\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n",
+ " \n",
+ " -2.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n",
+ " \n",
+ " -3.3\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n",
+ " \n",
+ " 6.04\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n",
+ " \n",
+ " -2.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n",
+ " \n",
+ " -0.47\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n",
+ " \n",
+ " 5.28\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n",
+ " \n",
+ " -4.84\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n",
+ " \n",
+ " 1.58\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n",
+ " \n",
+ " 0.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n",
+ " \n",
+ " 0.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n",
+ " \n",
+ " 5.79\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n",
+ " \n",
+ " 1.8\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n",
+ " \n",
+ " -3.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n",
+ " \n",
+ " -3.85\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n",
+ " \n",
+ " -5.53\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n",
+ " \n",
+ " -2.97\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n",
+ " \n",
+ " -2.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n",
+ " \n",
+ " -1.15\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n",
+ " \n",
+ " -0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n",
+ " \n",
+ " -13.13\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n",
+ " \n",
+ " 2.07\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n",
+ " \n",
+ " 6.16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n",
+ " \n",
+ " 4.94\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row17\">\n",
+ " \n",
+ " 17\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n",
+ " \n",
+ " 5.64\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n",
+ " \n",
+ " 4.57\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n",
+ " \n",
+ " -3.53\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n",
+ " \n",
+ " -3.76\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n",
+ " \n",
+ " 6.58\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n",
+ " \n",
+ " -2.58\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n",
+ " \n",
+ " -0.75\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n",
+ " \n",
+ " 6.58\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n",
+ " \n",
+ " -4.78\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n",
+ " \n",
+ " 3.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n",
+ " \n",
+ " -0.29\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n",
+ " \n",
+ " 0.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n",
+ " \n",
+ " 5.76\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n",
+ " \n",
+ " 2.05\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n",
+ " \n",
+ " -2.27\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n",
+ " \n",
+ " -2.31\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n",
+ " \n",
+ " -4.95\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n",
+ " \n",
+ " -3.16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n",
+ " \n",
+ " -3.06\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n",
+ " \n",
+ " -2.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n",
+ " \n",
+ " 0.84\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n",
+ " \n",
+ " -12.57\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n",
+ " \n",
+ " 3.56\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n",
+ " \n",
+ " 7.36\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n",
+ " \n",
+ " 4.7\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row18\">\n",
+ " \n",
+ " 18\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n",
+ " \n",
+ " 5.99\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n",
+ " \n",
+ " 5.82\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n",
+ " \n",
+ " -2.85\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n",
+ " \n",
+ " -4.15\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n",
+ " \n",
+ " 7.12\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n",
+ " \n",
+ " -3.32\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n",
+ " \n",
+ " -1.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n",
+ " \n",
+ " 7.93\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n",
+ " \n",
+ " -4.85\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n",
+ " \n",
+ " 1.44\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n",
+ " \n",
+ " -0.63\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n",
+ " \n",
+ " 0.35\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n",
+ " \n",
+ " 7.47\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n",
+ " \n",
+ " 0.87\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n",
+ " \n",
+ " -1.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n",
+ " \n",
+ " -2.09\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n",
+ " \n",
+ " -4.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n",
+ " \n",
+ " -2.55\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n",
+ " \n",
+ " -2.46\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n",
+ " \n",
+ " -2.89\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n",
+ " \n",
+ " 1.9\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n",
+ " \n",
+ " -9.74\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n",
+ " \n",
+ " 3.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n",
+ " \n",
+ " 7.07\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n",
+ " \n",
+ " 4.39\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " \n",
+ " <th id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fb\" class=\"row_heading level24 row19\">\n",
+ " \n",
+ " 19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n",
+ " \n",
+ " 4.03\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n",
+ " \n",
+ " 6.23\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n",
+ " \n",
+ " -4.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n",
+ " \n",
+ " -4.11\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n",
+ " \n",
+ " 7.19\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n",
+ " \n",
+ " -4.1\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n",
+ " \n",
+ " -1.52\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n",
+ " \n",
+ " 6.53\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n",
+ " \n",
+ " -5.21\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n",
+ " \n",
+ " -0.24\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n",
+ " \n",
+ " 0.01\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n",
+ " \n",
+ " 1.16\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n",
+ " \n",
+ " 6.43\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n",
+ " \n",
+ " -1.97\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n",
+ " \n",
+ " -2.64\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n",
+ " \n",
+ " -1.66\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n",
+ " \n",
+ " -5.2\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n",
+ " \n",
+ " -3.25\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n",
+ " \n",
+ " -2.87\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n",
+ " \n",
+ " -1.65\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n",
+ " \n",
+ " 1.64\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n",
+ " \n",
+ " -10.66\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n",
+ " \n",
+ " 2.83\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n",
+ " \n",
+ " 7.48\n",
+ " \n",
+ " \n",
+ " <td id=\"T_e8924e52_8bd5_11e5_bfc1_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n",
+ " \n",
+ " 3.94\n",
+ " \n",
+ " \n",
+ " </tr>\n",
+ " \n",
+ " </tbody>\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<pandas.core.style.Styler at 0x11335e6a0>"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.random.seed(25)\n",
+ "cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)\n",
+ "df = pd.DataFrame(np.random.randn(20, 25)).cumsum()\n",
+ "\n",
+ "df.style.background_gradient(cmap, axis=1)\\\n",
+ " .set_properties(**{'max-width': '80px', 'font-size': '1pt'})\\\n",
+ " .set_caption(\"Hover to magify\")\\\n",
+ " .set_precision(2)\\\n",
+ " .set_table_styles(magnify())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Extensibility\n",
+ "\n",
+ "The core of pandas is, and will remain, its \"high-performance, easy-to-use data structures\".\n",
+ "With that in mind, we hope that `DataFrame.style` accomplishes two goals\n",
+ "\n",
+ "- Provide an API that is pleasing to use interactively and is \"good enough\" for many tasks\n",
+ "- Provide the foundations for dedicated libraries to build on\n",
+ "\n",
+ "If you build a great library on top of this, let us know and we'll [link](http://pandas.pydata.org/pandas-docs/stable/ecosystem.html) to it.\n",
+ "\n",
+ "## Subclassing\n",
+ "\n",
+ "This section contains a bit of information about the implementation of `Styler`.\n",
+ "Since the feature is so new all of this is subject to change, even more so than the end-use API.\n",
+ "\n",
+ "As users apply styles (via `.apply`, `.applymap` or one of the builtins), we don't actually calculate anything.\n",
+ "Instead, we append functions and arguments to a list `self._todo`.\n",
+ "When asked (typically in `.render` we'll walk through the list and execute each function (this is in `self._compute()`.\n",
+ "These functions update an internal `defaultdict(list)`, `self.ctx` which maps DataFrame row / column positions to CSS attribute, value pairs.\n",
+ "\n",
+ "We take the extra step through `self._todo` so that we can export styles and set them on other `Styler`s.\n",
+ "\n",
+ "Rendering uses [Jinja](http://jinja.pocoo.org/) templates.\n",
+ "The `.translate` method takes `self.ctx` and builds another dictionary ready to be passed into `Styler.template.render`, the Jinja template.\n",
+ "\n",
+ "\n",
+ "## Alternate templates\n",
+ "\n",
+ "We've used [Jinja](http://jinja.pocoo.org/) templates to build up the HTML.\n",
+ "The template is stored as a class variable ``Styler.template.``. Subclasses can override that.\n",
+ "\n",
+ "```python\n",
+ "class CustomStyle(Styler):\n",
+ " template = Template(\"\"\"...\"\"\")\n",
+ "```"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.4.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/doc/source/index.rst.template b/doc/source/index.rst.template
index 621bd33ba5a41..6011c22e9cc2e 100644
--- a/doc/source/index.rst.template
+++ b/doc/source/index.rst.template
@@ -136,6 +136,7 @@ See the package overview for more detail about what's in the library.
timedeltas
categorical
visualization
+ style
io
remote_data
enhancingperf
diff --git a/doc/source/install.rst b/doc/source/install.rst
index 54e7b2d4df350..9accc188d567f 100644
--- a/doc/source/install.rst
+++ b/doc/source/install.rst
@@ -254,6 +254,7 @@ Optional Dependencies
* Needed for Excel I/O
* `XlsxWriter <https://pypi.python.org/pypi/XlsxWriter>`__
* Alternative Excel writer
+* `Jinja2 <http://jinja.pocoo.org/>`__: Template engine for conditional HTML formatting.
* `boto <https://pypi.python.org/pypi/boto>`__: necessary for Amazon S3
access.
* `blosc <https://pypi.python.org/pypi/blosc>`__: for msgpack compression using ``blosc``
diff --git a/doc/source/style.rst b/doc/source/style.rst
new file mode 100644
index 0000000000000..c373a75c98345
--- /dev/null
+++ b/doc/source/style.rst
@@ -0,0 +1,6 @@
+.. _style:
+
+.. currentmodule:: pandas
+
+.. raw:: html
+ :file: html-styling.html
diff --git a/doc/source/whatsnew/v0.17.1.txt b/doc/source/whatsnew/v0.17.1.txt
index 151131a52a4bb..7178be1ffefd2 100755
--- a/doc/source/whatsnew/v0.17.1.txt
+++ b/doc/source/whatsnew/v0.17.1.txt
@@ -15,6 +15,21 @@ Highlights include:
:local:
:backlinks: none
+New features
+~~~~~~~~~~~~
+
+.. _whatsnew_0171.style
+
+Conditional HTML Formatting
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We've added *experimental* support for conditional HTML formatting,
+the visual styling of a DataFrame based on the data.
+The styling is accomplished with HTML and CSS.
+Acesses the styler class with :attr:`pandas.DataFrame.style`, attribute,
+an instance of :class:`~pandas.core.style.Styler` with your data attached.
+See the :ref:`example notebook <style>` for more.
+
.. _whatsnew_0171.enhancements:
Enhancements
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 22d0026f27742..1e40d77839d3c 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -577,6 +577,19 @@ def _repr_html_(self):
else:
return None
+ @property
+ def style(self):
+ """
+ Property returning a Styler object containing methods for
+ building a styled HTML representation fo the DataFrame.
+
+ See Also
+ --------
+ pandas.core.Styler
+ """
+ from pandas.core.style import Styler
+ return Styler(self)
+
def iteritems(self):
"""
Iterator over (column name, Series) pairs.
@@ -1518,6 +1531,7 @@ def to_html(self, buf=None, columns=None, col_space=None, colSpace=None,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions)
+ # TODO: a generic formatter wld b in DataFrameFormatter
formatter.to_html(classes=classes, notebook=notebook)
if buf is None:
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 0f3795fcad0c3..25a110ee43039 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -524,7 +524,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False):
Parameters
----------
indexer : tuple, slice, scalar
- The indexer used to get the locations that will be set to
+ The indexer used to get the locations that will be set to
`ser`
ser : pd.Series
@@ -532,7 +532,7 @@ def _align_series(self, indexer, ser, multiindex_indexer=False):
multiindex_indexer : boolean, optional
Defaults to False. Should be set to True if `indexer` was from
- a `pd.MultiIndex`, to avoid unnecessary broadcasting.
+ a `pd.MultiIndex`, to avoid unnecessary broadcasting.
Returns:
@@ -1806,3 +1806,46 @@ def maybe_droplevels(index, key):
pass
return index
+
+
+def _non_reducing_slice(slice_):
+ """
+ Ensurse that a slice doesn't reduce to a Series or Scalar.
+
+ Any user-paseed `subset` should have this called on it
+ to make sure we're always working with DataFrames.
+ """
+ # default to column slice, like DataFrame
+ # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
+ kinds = tuple(list(compat.string_types) +
+ [ABCSeries, np.ndarray, Index, list])
+ if isinstance(slice_, kinds):
+ slice_ = IndexSlice[:, slice_]
+
+ def pred(part):
+ # true when slice does *not* reduce
+ return isinstance(part, slice) or com.is_list_like(part)
+
+ if not com.is_list_like(slice_):
+ if not isinstance(slice_, slice):
+ # a 1-d slice, like df.loc[1]
+ slice_ = [[slice_]]
+ else:
+ # slice(a, b, c)
+ slice_ = [slice_] # to tuplize later
+ else:
+ slice_ = [part if pred(part) else [part] for part in slice_]
+ return tuple(slice_)
+
+
+def _maybe_numeric_slice(df, slice_, include_bool=False):
+ """
+ want nice defaults for background_gradient that don't break
+ with non-numeric data. But if slice_ is passed go with that.
+ """
+ if slice_ is None:
+ dtypes = [np.number]
+ if include_bool:
+ dtypes.append(bool)
+ slice_ = IndexSlice[:, df.select_dtypes(include=dtypes).columns]
+ return slice_
diff --git a/pandas/core/style.py b/pandas/core/style.py
new file mode 100644
index 0000000000000..11170a6fdbe33
--- /dev/null
+++ b/pandas/core/style.py
@@ -0,0 +1,720 @@
+"""
+Module for applying conditional formatting to
+DataFrames and Series.
+"""
+from functools import partial
+from contextlib import contextmanager
+from uuid import uuid1
+import copy
+from collections import defaultdict
+
+try:
+ from jinja2 import Template
+except ImportError:
+ msg = "pandas.Styler requires jinja2. "\
+ "Please install with `conda install Jinja2`\n"\
+ "or `pip install Jinja2`"
+ raise ImportError(msg)
+
+import numpy as np
+import pandas as pd
+from pandas.compat import lzip
+from pandas.core.indexing import _maybe_numeric_slice, _non_reducing_slice
+try:
+ import matplotlib.pyplot as plt
+ from matplotlib import colors
+ has_mpl = True
+except ImportError:
+ has_mpl = False
+ no_mpl_message = "{0} requires matplotlib."
+
+
+@contextmanager
+def _mpl(func):
+ if has_mpl:
+ yield plt, colors
+ else:
+ raise ImportError(no_mpl_message.format(func.__name__))
+
+
+class Styler(object):
+ """
+ Helps style a DataFrame or Series according to the
+ data with HTML and CSS.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ data: Series or DataFrame
+ precision: int
+ precision to round floats to, defaults to pd.options.display.precision
+ table_styles: list-like, default None
+ list of {selector: (attr, value)} dicts; see Notes
+ uuid: str, default None
+ a unique identifier to avoid CSS collisons; generated automatically
+ caption: str, default None
+ caption to attach to the table
+
+ Attributes
+ ----------
+ tempate: Jinja Template
+
+ Notes
+ -----
+ Most styling will be done by passing style functions into
+ Styler.apply or Styler.applymap. Style functions should
+ return values with strings containing CSS 'attr: value' that will
+ be applied to the indicated cells.
+
+ If using in the Jupyter notebook, Styler has defined a _repr_html_
+ to automatically render itself. Otherwise call Styler.render to get
+ the genterated HTML.
+
+ See Also
+ --------
+ pandas.DataFrame.style
+ """
+ template = Template("""
+ <style type="text/css" >
+ {% for s in table_styles %}
+ #T_{{uuid}} {{s.selector}} {
+ {% for p,val in s.props %}
+ {{p}}: {{val}};
+ {% endfor %}
+ }
+ {% endfor %}
+ {% for s in cellstyle %}
+ #T_{{uuid}}{{s.selector}} {
+ {% for p,val in s.props %}
+ {{p}}: {{val}};
+ {% endfor %}
+ }
+ {% endfor %}
+ </style>
+
+ <table id="T_{{uuid}}">
+ {% if caption %}
+ <caption>{{caption}}</caption>
+ {% endif %}
+
+ <thead>
+ {% for r in head %}
+ <tr>
+ {% for c in r %}
+ <{{c.type}} class="{{c.class}}">{{c.value}}
+ {% endfor %}
+ </tr>
+ {% endfor %}
+ </thead>
+ <tbody>
+ {% for r in body %}
+ <tr>
+ {% for c in r %}
+ <{{c.type}} id="T_{{uuid}}{{c.id}}" class="{{c.class}}">
+ {% if c.value is number %}
+ {{c.value|round(precision)}}
+ {% else %}
+ {{c.value}}
+ {% endif %}
+ {% endfor %}
+ </tr>
+ {% endfor %}
+ </tbody>
+ </table>
+ """)
+
+ def __init__(self, data, precision=None, table_styles=None, uuid=None,
+ caption=None):
+ self.ctx = defaultdict(list)
+ self._todo = []
+
+ if not isinstance(data, (pd.Series, pd.DataFrame)):
+ raise TypeError
+ if data.ndim == 1:
+ data = data.to_frame()
+ if not data.index.is_unique or not data.columns.is_unique:
+ raise ValueError("style is not supported for non-unique indicies.")
+
+ self.data = data
+ self.index = data.index
+ self.columns = data.columns
+
+ self.uuid = uuid
+ self.table_styles = table_styles
+ self.caption = caption
+ if precision is None:
+ precision = pd.options.display.precision
+ self.precision = precision
+
+ def _repr_html_(self):
+ '''
+ Hooks into Jupyter notebook rich display system.
+ '''
+ return self.render()
+
+ def _translate(self):
+ """
+ Convert the DataFrame in `self.data` and the attrs from `_build_styles`
+ into a dictionary of {head, body, uuid, cellstyle}
+ """
+ table_styles = self.table_styles or []
+ caption = self.caption
+ ctx = self.ctx
+ precision = self.precision
+ uuid = self.uuid or str(uuid1()).replace("-", "_")
+ ROW_HEADING_CLASS = "row_heading"
+ COL_HEADING_CLASS = "col_heading"
+ DATA_CLASS = "data"
+ BLANK_CLASS = "blank"
+ BLANK_VALUE = ""
+
+ cell_context = dict()
+
+ n_rlvls = self.data.index.nlevels
+ n_clvls = self.data.columns.nlevels
+ rlabels = self.data.index.tolist()
+ clabels = self.data.columns.tolist()
+
+ idx_values = self.data.index.format(sparsify=False, adjoin=False,
+ names=False)
+ idx_values = lzip(*idx_values)
+
+ if n_rlvls == 1:
+ rlabels = [[x] for x in rlabels]
+ if n_clvls == 1:
+ clabels = [[x] for x in clabels]
+ clabels = list(zip(*clabels))
+
+ cellstyle = []
+ head = []
+
+ for r in range(n_clvls):
+ row_es = [{"type": "th", "value": BLANK_VALUE,
+ "class": " ".join([BLANK_CLASS])}] * n_rlvls
+ for c in range(len(clabels[0])):
+ cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c]
+ cs.extend(cell_context.get(
+ "col_headings", {}).get(r, {}).get(c, []))
+ row_es.append({"type": "th", "value": clabels[r][c],
+ "class": " ".join(cs)})
+ head.append(row_es)
+
+ body = []
+ for r, idx in enumerate(self.data.index):
+ cs = [ROW_HEADING_CLASS, "level%s" % c, "row%s" % r]
+ cs.extend(cell_context.get(
+ "row_headings", {}).get(r, {}).get(c, []))
+ row_es = [{"type": "th",
+ "value": rlabels[r][c],
+ "class": " ".join(cs)}
+ for c in range(len(rlabels[r]))]
+
+ for c, col in enumerate(self.data.columns):
+ cs = [DATA_CLASS, "row%s" % r, "col%s" % c]
+ cs.extend(cell_context.get("data", {}).get(r, {}).get(c, []))
+ row_es.append({"type": "td", "value": self.data.iloc[r][c],
+ "class": " ".join(cs), "id": "_".join(cs[1:])})
+ props = []
+ for x in ctx[r, c]:
+ # have to handle empty styles like ['']
+ if x.count(":"):
+ props.append(x.split(":"))
+ else:
+ props.append(['', ''])
+ cellstyle.append(
+ {'props': props,
+ 'selector': "row%s_col%s" % (r, c)}
+ )
+ body.append(row_es)
+
+ return dict(head=head, cellstyle=cellstyle, body=body, uuid=uuid,
+ precision=precision, table_styles=table_styles,
+ caption=caption)
+
+ def render(self):
+ """
+ Render the built up styles to HTML
+
+ .. versionadded:: 0.17.1
+
+ Returns
+ -------
+ rendered: str
+ the rendered HTML
+
+ Notes
+ -----
+ ``Styler`` objects have defined the ``_repr_html_`` method
+ which automatically calls ``self.render()`` when it's the
+ last item in a Notebook cell. When calling ``Styler.render()``
+ directly, wrap the resul in ``IPython.display.HTML`` to view
+ the rendered HTML in the notebook.
+ """
+ self._compute()
+ d = self._translate()
+ # filter out empty styles, every cell will have a class
+ # but the list of props may just be [['', '']].
+ # so we have the neested anys below
+ trimmed = [x for x in d['cellstyle'] if
+ any(any(y) for y in x['props'])]
+ d['cellstyle'] = trimmed
+ return self.template.render(**d)
+
+ def _update_ctx(self, attrs):
+ """
+ update the state of the Styler. Collects a mapping
+ of {index_label: ['<property>: <value>']}
+
+ attrs: Series or DataFrame
+ should contain strings of '<property>: <value>;<prop2>: <val2>'
+ Whitespace shouldn't matter and the final trailing ';' shouldn't
+ matter.
+ """
+ for row_label, v in attrs.iterrows():
+ for col_label, col in v.iteritems():
+ i = self.index.get_indexer([row_label])[0]
+ j = self.columns.get_indexer([col_label])[0]
+ for pair in col.rstrip(";").split(";"):
+ self.ctx[(i, j)].append(pair)
+
+ def _copy(self, deepcopy=False):
+ styler = Styler(self.data, precision=self.precision,
+ caption=self.caption, uuid=self.uuid,
+ table_styles=self.table_styles)
+ if deepcopy:
+ styler.ctx = copy.deepcopy(self.ctx)
+ styler._todo = copy.deepcopy(self._todo)
+ else:
+ styler.ctx = self.ctx
+ styler._todo = self._todo
+ return styler
+
+ def __copy__(self):
+ """
+ Deep copy by default.
+ """
+ return self._copy(deepcopy=False)
+
+ def __deepcopy__(self, memo):
+ return self._copy(deepcopy=True)
+
+ def clear(self):
+ self.ctx.clear()
+ self._todo = []
+
+ def _compute(self):
+ '''
+ Execute the style functions built up in `self._todo`.
+
+ Relies on the conventions that all style functions go through
+ .apply or .applymap. The append styles to apply as tuples of
+
+ (application method, *args, **kwargs)
+ '''
+ r = self
+ for func, args, kwargs in self._todo:
+ r = func(self)(*args, **kwargs)
+ return r
+
+ def _apply(self, func, axis=0, subset=None, **kwargs):
+ subset = slice(None) if subset is None else subset
+ subset = _non_reducing_slice(subset)
+ if axis is not None:
+ result = self.data.loc[subset].apply(func, axis=axis, **kwargs)
+ else:
+ # like tee
+ result = func(self.data.loc[subset], **kwargs)
+ self._update_ctx(result)
+ return self
+
+ def apply(self, func, axis=0, subset=None, **kwargs):
+ """
+ Apply a function column-wise, row-wise, or table-wase,
+ updating the HTML representation with the result.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ func: function
+ axis: int, str or None
+ apply to each column (``axis=0`` or ``'index'``)
+ or to each row (``axis=1`` or ``'columns'``) or
+ to the entire DataFrame at once with ``axis=None``.
+ subset: IndexSlice
+ a valid indexer to limit ``data`` to *before* applying the
+ function. Consider using a pandas.IndexSlice
+ kwargs: dict
+ pass along to ``func``
+
+ Returns
+ -------
+ self
+
+ Notes
+ -----
+ This is similar to DataFrame.apply, except that axis=None applies
+ the function to the entire DataFrame at once, rather tha column
+ or rowwise.
+ """
+ self._todo.append((lambda instance: getattr(instance, '_apply'),
+ (func, axis, subset),
+ kwargs))
+ return self
+
+ def _applymap(self, func, subset=None, **kwargs):
+ func = partial(func, **kwargs) # applymap doesn't take kwargs?
+ if subset is None:
+ subset = pd.IndexSlice[:]
+ subset = _non_reducing_slice(subset)
+ result = self.data.loc[subset].applymap(func)
+ self._update_ctx(result)
+ return self
+
+ def applymap(self, func, subset=None, **kwargs):
+ """
+ Apply a function elementwise, updating the HTML
+ representation with the result.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ func : function
+ subset : IndexSlice
+ a valid indexer to limit ``data`` to *before* applying the
+ function. Consider using a pandas.IndexSlice
+ kwargs : dict
+ pass along to ``func``
+
+ Returns
+ -------
+ self
+
+ """
+ self._todo.append((lambda instance: getattr(instance, '_applymap'),
+ (func, subset),
+ kwargs))
+ return self
+
+ def set_precision(self, precision):
+ """
+ Set the precision used to render.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ precision: int
+
+ Returns
+ -------
+ self
+ """
+ self.precision = precision
+ return self
+
+ def export(self):
+ """
+ Export the styles to applied to the current Styler.
+ Can be applied to a second style with `.use`.
+
+ .. versionadded:: 0.17.1
+
+ Returns
+ -------
+ styles: list
+
+ See Also
+ --------
+ Styler.use
+ """
+ return self._todo
+
+ def use(self, styles):
+ """
+ Set the styles on the current Styler, possibly using styles
+ from `Styler.export`
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ styles: list
+ list of style functions
+
+ Returns
+ -------
+ self
+
+ See Also
+ --------
+ Styler.export
+ """
+ self._todo.extend(styles)
+ return self
+
+ def set_uuid(self, uuid):
+ """
+ Set the uuid for a Styler.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ uuid: str
+
+ Returns
+ -------
+ self
+ """
+ self.uuid = uuid
+ return self
+
+ def set_caption(self, caption):
+ """
+ Se the caption on a Styler
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ caption: str
+
+ Returns
+ -------
+ self
+ """
+ self.caption = caption
+ return self
+
+ def set_table_styles(self, table_styles):
+ """
+ Set the table styles on a Styler
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ table_styles: list
+
+ Returns
+ -------
+ self
+ """
+ self.table_styles = table_styles
+ return self
+
+ # -----------------------------------------------------------------------
+ # A collection of "builtin" styles
+ # -----------------------------------------------------------------------
+
+ @staticmethod
+ def _highlight_null(v, null_color):
+ return 'background-color: %s' % null_color if pd.isnull(v) else ''
+
+ def highlight_null(self, null_color='red'):
+ """
+ Shade the background ``null_color`` for missing values.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ null_color: str
+
+ Returns
+ -------
+ self
+ """
+ self.applymap(self._highlight_null, null_color=null_color)
+ return self
+
+ def background_gradient(self, cmap='PuBu', low=0, high=0,
+ axis=0, subset=None):
+ """
+ Color the background in a gradient according to
+ the data in each column (optionally row).
+ Requires matplotlib.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ cmap: str or colormap
+ matplotlib colormap
+ low, high: float
+ compress the range by these values.
+ axis: int or str
+ 1 or 'columns' for colunwise, 0 or 'index' for rowwise
+ subset: IndexSlice
+ a valid slice for ``data`` to limit the style application to
+
+ Returns
+ -------
+ self
+
+ Notes
+ -----
+ Tune ``low`` and ``high`` to keep the text legible by
+ not using the entire range of the color map. These extend
+ the range of the data by ``low * (x.max() - x.min())``
+ and ``high * (x.max() - x.min())`` before normalizing.
+ """
+ subset = _maybe_numeric_slice(self.data, subset)
+ subset = _non_reducing_slice(subset)
+ self.apply(self._background_gradient, cmap=cmap, subset=subset,
+ axis=axis, low=low, high=high)
+ return self
+
+ @staticmethod
+ def _background_gradient(s, cmap='PuBu', low=0, high=0):
+ """Color background in a range according to the data."""
+ with _mpl(Styler.background_gradient) as (plt, colors):
+ rng = s.max() - s.min()
+ # extend lower / upper bounds, compresses color range
+ norm = colors.Normalize(s.min() - (rng * low),
+ s.max() + (rng * high))
+ # matplotlib modifies inplace?
+ # https://github.com/matplotlib/matplotlib/issues/5427
+ normed = norm(s.values)
+ c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]
+ return ['background-color: %s' % color for color in c]
+
+ def set_properties(self, subset=None, **kwargs):
+ """
+ Convience method for setting one or more non-data dependent
+ properties or each cell.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice
+ a valid slice for ``data`` to limit the style application to
+ kwargs: dict
+ property: value pairs to be set for each cell
+
+ Returns
+ -------
+ self : Styler
+
+ Examples
+ --------
+ >>> df = pd.DataFrame(np.random.randn(10, 4))
+ >>> df.style.set_properties(color="white", align="right")
+ """
+ values = ';'.join('{p}: {v}'.format(p=p, v=v) for p, v in
+ kwargs.items())
+ f = lambda x: values
+ return self.applymap(f, subset=subset)
+
+ @staticmethod
+ def _bar(s, color, width):
+ normed = width * (s - s.min()) / (s.max() - s.min())
+ attrs = 'width: 10em; height: 80%;'\
+ 'background: linear-gradient(90deg,'\
+ '{c} {w}%, transparent 0%)'
+ return [attrs.format(c=color, w=x) for x in normed]
+
+ def bar(self, subset=None, axis=0, color='#d65f5f', width=100):
+ """
+ Color the background ``color`` proptional to the values in each column.
+ Excludes non-numeric data by default.
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ a valid slice for ``data`` to limit the style application to
+ axis: int
+ color: str
+ width: float
+ A number between 0 or 100. The largest value will cover ``width``
+ percent of the cell's width
+
+ Returns
+ -------
+ self
+ """
+ subset = _maybe_numeric_slice(self.data, subset)
+ subset = _non_reducing_slice(subset)
+ self.apply(self._bar, subset=subset, axis=axis, color=color,
+ width=width)
+ return self
+
+ def highlight_max(self, subset=None, color='yellow', axis=0):
+ """
+ Highlight the maximum by shading the background
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ a valid slice for ``data`` to limit the style application to
+ color: str, default 'yellow'
+ axis: int, str, or None; default None
+ 0 or 'index' for columnwise, 1 or 'columns' for rowwise
+ or ``None`` for tablewise (the default)
+
+ Returns
+ -------
+ self
+ """
+ return self._highlight_handler(subset=subset, color=color, axis=axis,
+ max_=True)
+
+ def highlight_min(self, subset=None, color='yellow', axis=0):
+ """
+ Highlight the minimum by shading the background
+
+ .. versionadded:: 0.17.1
+
+ Parameters
+ ----------
+ subset: IndexSlice, default None
+ a valid slice for ``data`` to limit the style application to
+ color: str, default 'yellow'
+ axis: int, str, or None; default None
+ 0 or 'index' for columnwise, 1 or 'columns' for rowwise
+ or ``None`` for tablewise (the default)
+
+ Returns
+ -------
+ self
+ """
+ return self._highlight_handler(subset=subset, color=color, axis=axis,
+ max_=False)
+
+ def _highlight_handler(self, subset=None, color='yellow', axis=None,
+ max_=True):
+ subset = _non_reducing_slice(_maybe_numeric_slice(self.data, subset))
+ self.apply(self._highlight_extrema, color=color, axis=axis,
+ subset=subset, max_=max_)
+ return self
+
+ @staticmethod
+ def _highlight_extrema(data, color='yellow', max_=True):
+ '''
+ highlight the min or max in a Series or DataFrame
+ '''
+ attr = 'background-color: {0}'.format(color)
+ if data.ndim == 1: # Series from .apply
+ if max_:
+ extrema = data == data.max()
+ else:
+ extrema = data == data.min()
+ return [attr if v else '' for v in extrema]
+ else: # DataFrame from .tee
+ if max_:
+ extrema = data == data.max().max()
+ else:
+ extrema = data == data.min().min()
+ return pd.DataFrame(np.where(extrema, attr, ''),
+ index=data.index, columns=data.columns)
+
+
+
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py
index 36e825924995a..0eb6a6a70f8b7 100644
--- a/pandas/tests/test_indexing.py
+++ b/pandas/tests/test_indexing.py
@@ -15,6 +15,7 @@
import pandas as pd
import pandas.core.common as com
from pandas import option_context
+from pandas.core.indexing import _non_reducing_slice, _maybe_numeric_slice
from pandas.core.api import (DataFrame, Index, Series, Panel, isnull,
MultiIndex, Float64Index, Timestamp, Timedelta)
from pandas.util.testing import (assert_almost_equal, assert_series_equal,
@@ -4728,6 +4729,49 @@ def test_large_mi_dataframe_indexing(self):
result = MultiIndex.from_arrays([range(10**6), range(10**6)])
assert(not (10**6, 0) in result)
+ def test_non_reducing_slice(self):
+ df = pd.DataFrame([[0, 1], [2, 3]])
+
+ slices = [
+ # pd.IndexSlice[:, :],
+ pd.IndexSlice[:, 1],
+ pd.IndexSlice[1, :],
+ pd.IndexSlice[[1], [1]],
+ pd.IndexSlice[1, [1]],
+ pd.IndexSlice[[1], 1],
+ pd.IndexSlice[1],
+ pd.IndexSlice[1, 1],
+ slice(None, None, None),
+ [0, 1],
+ np.array([0, 1]),
+ pd.Series([0, 1])
+ ]
+ for slice_ in slices:
+ tslice_ = _non_reducing_slice(slice_)
+ self.assertTrue(isinstance(df.loc[tslice_], DataFrame))
+
+ def test_list_slice(self):
+ # like dataframe getitem
+ slices = [['A'], pd.Series(['A']), np.array(['A'])]
+ df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['A', 'B'])
+ expected = pd.IndexSlice[:, ['A']]
+ for subset in slices:
+ result = _non_reducing_slice(subset)
+ tm.assert_frame_equal(df.loc[result], df.loc[expected])
+
+ def test_maybe_numeric_slice(self):
+ df = pd.DataFrame({'A': [1, 2], 'B': ['c', 'd'], 'C': [True, False]})
+ result = _maybe_numeric_slice(df, slice_=None)
+ expected = pd.IndexSlice[:, ['A']]
+ self.assertEqual(result, expected)
+
+ result = _maybe_numeric_slice(df, None, include_bool=True)
+ expected = pd.IndexSlice[:, ['A', 'C']]
+ result = _maybe_numeric_slice(df, [1])
+ expected = [1]
+ self.assertEqual(result, expected)
+
+
class TestCategoricalIndex(tm.TestCase):
def setUp(self):
diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py
new file mode 100644
index 0000000000000..4c875cebe149a
--- /dev/null
+++ b/pandas/tests/test_style.py
@@ -0,0 +1,372 @@
+import os
+from nose import SkipTest
+
+# this is a mess. Getting failures on a python 2.7 build with
+# whenever we try to import jinja, whether it's installed or not.
+# so we're explicitly skipping that one *before* we try to import
+# jinja. We still need to export the imports as globals,
+# since importing Styler tries to import jinja2.
+job_name = os.environ.get('JOB_NAME', None)
+if job_name == '27_slow_nnet_LOCALE':
+ raise SkipTest("No jinja")
+try:
+ from pandas.core.style import Styler
+except ImportError:
+ raise SkipTest("No Jinja2")
+
+import copy
+
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+from pandas.util.testing import TestCase
+import pandas.util.testing as tm
+
+
+class TestStyler(TestCase):
+
+ def setUp(self):
+ np.random.seed(24)
+ self.s = DataFrame({'A': np.random.permutation(range(6))})
+ self.df = DataFrame({'A': [0, 1], 'B': np.random.randn(2)})
+ self.f = lambda x: x
+ self.g = lambda x: x
+
+ def h(x, foo='bar'):
+ return pd.Series(['color: %s' % foo], index=x.index, name=x.name)
+
+ self.h = h
+ self.styler = Styler(self.df)
+ self.attrs = pd.DataFrame({'A': ['color: red', 'color: blue']})
+ self.dataframes = [
+ self.df,
+ pd.DataFrame({'f': [1., 2.], 'o': ['a', 'b'],
+ 'c': pd.Categorical(['a', 'b'])})
+ ]
+
+ def test_update_ctx(self):
+ self.styler._update_ctx(self.attrs)
+ expected = {(0, 0): ['color: red'],
+ (1, 0): ['color: blue']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_update_ctx_flatten_multi(self):
+ attrs = DataFrame({"A": ['color: red; foo: bar',
+ 'color: blue; foo: baz']})
+ self.styler._update_ctx(attrs)
+ expected = {(0, 0): ['color: red', ' foo: bar'],
+ (1, 0): ['color: blue', ' foo: baz']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_update_ctx_flatten_multi_traliing_semi(self):
+ attrs = DataFrame({"A": ['color: red; foo: bar;',
+ 'color: blue; foo: baz;']})
+ self.styler._update_ctx(attrs)
+ expected = {(0, 0): ['color: red', ' foo: bar'],
+ (1, 0): ['color: blue', ' foo: baz']}
+ self.assertEqual(self.styler.ctx, expected)
+
+ def test_copy(self):
+ s2 = copy.copy(self.styler)
+ self.assertTrue(self.styler is not s2)
+ self.assertTrue(self.styler.ctx is s2.ctx) # shallow
+ self.assertTrue(self.styler._todo is s2._todo)
+
+ self.styler._update_ctx(self.attrs)
+ self.styler.highlight_max()
+ self.assertEqual(self.styler.ctx, s2.ctx)
+ self.assertEqual(self.styler._todo, s2._todo)
+
+ def test_deepcopy(self):
+ s2 = copy.deepcopy(self.styler)
+ self.assertTrue(self.styler is not s2)
+ self.assertTrue(self.styler.ctx is not s2.ctx)
+ self.assertTrue(self.styler._todo is not s2._todo)
+
+ self.styler._update_ctx(self.attrs)
+ self.styler.highlight_max()
+ self.assertNotEqual(self.styler.ctx, s2.ctx)
+ self.assertEqual(s2._todo, [])
+ self.assertNotEqual(self.styler._todo, s2._todo)
+
+ def test_clear(self):
+ s = self.df.style.highlight_max()._compute()
+ self.assertTrue(len(s.ctx) > 0)
+ self.assertTrue(len(s._todo) > 0)
+ s.clear()
+ self.assertTrue(len(s.ctx) == 0)
+ self.assertTrue(len(s._todo) == 0)
+
+ def test_render(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ style = lambda x: pd.Series(["color: red", "color: blue"], name=x.name)
+ s = Styler(df, uuid='AB').apply(style).apply(style, axis=1)
+ s.render()
+ # it worked?
+
+ def test_render_double(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ style = lambda x: pd.Series(["color: red; border: 1px",
+ "color: blue; border: 2px"], name=x.name)
+ s = Styler(df, uuid='AB').apply(style)
+ s.render()
+ # it worked?
+
+ def test_set_properties(self):
+ df = pd.DataFrame({"A": [0, 1]})
+ result = df.style.set_properties(color='white',
+ size='10px')._compute().ctx
+ # order is deterministic
+ v = ["color: white", "size: 10px"]
+ expected = {(0, 0): v, (1, 0): v}
+ self.assertEqual(result.keys(), expected.keys())
+ for v1, v2 in zip(result.values(), expected.values()):
+ self.assertEqual(sorted(v1), sorted(v2))
+
+ def test_set_properties_subset(self):
+ df = pd.DataFrame({'A': [0, 1]})
+ result = df.style.set_properties(subset=pd.IndexSlice[0, 'A'],
+ color='white')._compute().ctx
+ expected = {(0, 0): ['color: white']}
+ self.assertEqual(result, expected)
+
+ def test_apply_axis(self):
+ df = pd.DataFrame({'A': [0, 0], 'B': [1, 1]})
+ f = lambda x: ['val: %s' % x.max() for v in x]
+ result = df.style.apply(f, axis=1)
+ self.assertEqual(len(result._todo), 1)
+ self.assertEqual(len(result.ctx), 0)
+ result._compute()
+ expected = {(0, 0): ['val: 1'], (0, 1): ['val: 1'],
+ (1, 0): ['val: 1'], (1, 1): ['val: 1']}
+ self.assertEqual(result.ctx, expected)
+
+ result = df.style.apply(f, axis=0)
+ expected = {(0, 0): ['val: 0'], (0, 1): ['val: 1'],
+ (1, 0): ['val: 0'], (1, 1): ['val: 1']}
+ result._compute()
+ self.assertEqual(result.ctx, expected)
+ result = df.style.apply(f) # default
+ result._compute()
+ self.assertEqual(result.ctx, expected)
+
+ def test_apply_subset(self):
+ axes = [0, 1]
+ slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
+ pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
+ pd.IndexSlice[:2, ['A', 'B']]]
+ for ax in axes:
+ for slice_ in slices:
+ result = self.df.style.apply(self.h, axis=ax, subset=slice_,
+ foo='baz')._compute().ctx
+ expected = dict(((r, c), ['color: baz'])
+ for r, row in enumerate(self.df.index)
+ for c, col in enumerate(self.df.columns)
+ if row in self.df.loc[slice_].index
+ and col in self.df.loc[slice_].columns)
+ self.assertEqual(result, expected)
+
+ def test_applymap_subset(self):
+ def f(x):
+ return 'foo: bar'
+
+ slices = [pd.IndexSlice[:], pd.IndexSlice[:, ['A']],
+ pd.IndexSlice[[1], :], pd.IndexSlice[[1], ['A']],
+ pd.IndexSlice[:2, ['A', 'B']]]
+
+ for slice_ in slices:
+ result = self.df.style.applymap(f, subset=slice_)._compute().ctx
+ expected = dict(((r, c), ['foo: bar'])
+ for r, row in enumerate(self.df.index)
+ for c, col in enumerate(self.df.columns)
+ if row in self.df.loc[slice_].index
+ and col in self.df.loc[slice_].columns)
+ self.assertEqual(result, expected)
+
+ def test_empty(self):
+ df = pd.DataFrame({'A': [1, 0]})
+ s = df.style
+ s.ctx = {(0, 0): ['color: red'],
+ (1, 0): ['']}
+
+ result = s._translate()['cellstyle']
+ expected = [{'props': [['color', ' red']], 'selector': 'row0_col0'},
+ {'props': [['', '']], 'selector': 'row1_col0'}]
+ self.assertEqual(result, expected)
+
+ def test_bar(self):
+ df = pd.DataFrame({'A': [0, 1, 2]})
+ result = df.style.bar()._compute().ctx
+ expected = {
+ (0, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#d65f5f 0.0%, transparent 0%)'],
+ (1, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#d65f5f 50.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,#d65f5f 100.0%, transparent 0%)']
+ }
+ self.assertEqual(result, expected)
+
+ result = df.style.bar(color='red', width=50)._compute().ctx
+ expected = {
+ (0, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 0.0%, transparent 0%)'],
+ (1, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 25.0%, transparent 0%)'],
+ (2, 0): ['width: 10em', ' height: 80%',
+ 'background: linear-gradient('
+ '90deg,red 50.0%, transparent 0%)']
+ }
+ self.assertEqual(result, expected)
+
+ df['C'] = ['a'] * len(df)
+ result = df.style.bar(color='red', width=50)._compute().ctx
+ self.assertEqual(result, expected)
+ df['C'] = df['C'].astype('category')
+ result = df.style.bar(color='red', width=50)._compute().ctx
+ self.assertEqual(result, expected)
+
+ def test_highlight_null(self, null_color='red'):
+ df = pd.DataFrame({'A': [0, np.nan]})
+ result = df.style.highlight_null()._compute().ctx
+ expected = {(0, 0): [''],
+ (1, 0): ['background-color: red']}
+ self.assertEqual(result, expected)
+
+ def test_nonunique_raises(self):
+ df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
+ with tm.assertRaises(ValueError):
+ df.style
+
+ with tm.assertRaises(ValueError):
+ Styler(df)
+
+ def test_caption(self):
+ styler = Styler(self.df, caption='foo')
+ result = styler.render()
+ self.assertTrue(all(['caption' in result, 'foo' in result]))
+
+ styler = self.df.style
+ result = styler.set_caption('baz')
+ self.assertTrue(styler is result)
+ self.assertEqual(styler.caption, 'baz')
+
+ def test_uuid(self):
+ styler = Styler(self.df, uuid='abc123')
+ result = styler.render()
+ self.assertTrue('abc123' in result)
+
+ styler = self.df.style
+ result = styler.set_uuid('aaa')
+ self.assertTrue(result is styler)
+ self.assertEqual(result.uuid, 'aaa')
+
+ def test_table_styles(self):
+ style = [{'selector': 'th', 'props': [('foo', 'bar')]}]
+ styler = Styler(self.df, table_styles=style)
+ result = ' '.join(styler.render().split())
+ self.assertTrue('th { foo: bar; }' in result)
+
+ styler = self.df.style
+ result = styler.set_table_styles(style)
+ self.assertTrue(styler is result)
+ self.assertEqual(styler.table_styles, style)
+
+ def test_precision(self):
+ with pd.option_context('display.precision', 10):
+ s = Styler(self.df)
+ self.assertEqual(s.precision, 10)
+ s = Styler(self.df, precision=2)
+ self.assertEqual(s.precision, 2)
+
+ s2 = s.set_precision(4)
+ self.assertTrue(s is s2)
+ self.assertEqual(s.precision, 4)
+
+ def test_apply_none(self):
+ def f(x):
+ return pd.DataFrame(np.where(x == x.max(), 'color: red', ''),
+ index=x.index, columns=x.columns)
+ result = (pd.DataFrame([[1, 2], [3, 4]])
+ .style.apply(f, axis=None)._compute().ctx)
+ self.assertEqual(result[(1, 1)], ['color: red'])
+
+ def test_trim(self):
+ result = self.df.style.render() # trim=True
+ self.assertEqual(result.count('#'), 0)
+
+ result = self.df.style.highlight_max().render()
+ self.assertEqual(result.count('#'), len(self.df.columns))
+
+ def test_highlight_max(self):
+ df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
+ # max(df) = min(-df)
+ for max_ in [True, False]:
+ if max_:
+ attr = 'highlight_max'
+ else:
+ df = -df
+ attr = 'highlight_min'
+ result = getattr(df.style, attr)()._compute().ctx
+ self.assertEqual(result[(1, 1)], ['background-color: yellow'])
+
+ result = getattr(df.style, attr)(color='green')._compute().ctx
+ self.assertEqual(result[(1, 1)], ['background-color: green'])
+
+ result = getattr(df.style, attr)(subset='A')._compute().ctx
+ self.assertEqual(result[(1, 0)], ['background-color: yellow'])
+
+ result = getattr(df.style, attr)(axis=0)._compute().ctx
+ expected = {(1, 0): ['background-color: yellow'],
+ (1, 1): ['background-color: yellow'],
+ (0, 1): [''], (0, 0): ['']}
+ self.assertEqual(result, expected)
+
+ result = getattr(df.style, attr)(axis=1)._compute().ctx
+ expected = {(0, 1): ['background-color: yellow'],
+ (1, 1): ['background-color: yellow'],
+ (0, 0): [''], (1, 0): ['']}
+ self.assertEqual(result, expected)
+
+ # separate since we cant negate the strs
+ df['C'] = ['a', 'b']
+ result = df.style.highlight_max()._compute().ctx
+ expected = {(1, 1): ['background-color: yellow']}
+
+ result = df.style.highlight_min()._compute().ctx
+ expected = {(0, 0): ['background-color: yellow']}
+
+ def test_export(self):
+ f = lambda x: 'color: red' if x > 0 else 'color: blue'
+ g = lambda x, y, z: 'color: %s' if x > 0 else 'color: %s' % z
+ style1 = self.styler
+ style1.applymap(f)\
+ .applymap(g, y='a', z='b')\
+ .highlight_max()
+ result = style1.export()
+ style2 = self.df.style
+ style2.use(result)
+ self.assertEqual(style1._todo, style2._todo)
+ style2.render()
+
+
+@tm.mplskip
+class TestStylerMatplotlibDep(TestCase):
+
+ def test_background_gradient(self):
+ df = pd.DataFrame([[1, 2], [2, 4]], columns=['A', 'B'])
+ for axis in [0, 1, 'index', 'columns']:
+ for cmap in [None, 'YlOrRd']:
+ result = df.style.background_gradient(cmap=cmap)._compute().ctx
+ self.assertTrue(all("#" in x[0] for x in result.values()))
+ self.assertEqual(result[(0, 0)], result[(0, 1)])
+ self.assertEqual(result[(1, 0)], result[(1, 1)])
+
+ result = (df.style.background_gradient(subset=pd.IndexSlice[1, 'A'])
+ ._compute().ctx)
+ self.assertEqual(result[(1, 0)], ['background-color: #fff7fb'])
diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py
index 2b5443e6ff0d2..13c0b6a08f6e7 100644
--- a/pandas/tests/test_testing.py
+++ b/pandas/tests/test_testing.py
@@ -11,7 +11,8 @@
from pandas.util.testing import (
assert_almost_equal, assertRaisesRegexp, raise_with_traceback,
assert_index_equal, assert_series_equal, assert_frame_equal,
- assert_numpy_array_equal, assert_isinstance, RNGContext
+ assert_numpy_array_equal, assert_isinstance, RNGContext,
+ assertRaises, skip_if_no_package_deco
)
from pandas.compat import is_platform_windows
@@ -627,6 +628,23 @@ def test_locale(self):
self.assertTrue(len(locales) >= 1)
+def test_skiptest_deco():
+ from nose import SkipTest
+ @skip_if_no_package_deco("fakepackagename")
+ def f():
+ pass
+ with assertRaises(SkipTest):
+ f()
+
+ @skip_if_no_package_deco("numpy")
+ def f():
+ pass
+ # hack to ensure that SkipTest is *not* raised
+ with assertRaises(ValueError):
+ f()
+ raise ValueError
+
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index be8b0df73593f..ba32bc1f3e377 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -1578,7 +1578,18 @@ def skip_if_no_package(*args, **kwargs):
exc_failed_check=SkipTest,
*args, **kwargs)
-#
+def skip_if_no_package_deco(pkg_name, version=None, app='pandas'):
+ from nose import SkipTest
+
+ def deco(func):
+ @wraps(func)
+ def wrapper(*args, **kwargs):
+ package_check(pkg_name, version=version, app=app,
+ exc_failed_import=SkipTest, exc_failed_check=SkipTest)
+ return func(*args, **kwargs)
+ return wrapper
+ return deco
+ #
# Additional tags decorators for nose
#
| closes https://github.com/pydata/pandas/issues/3190
closes #4315
Not close to being done, but I wanted to put this here before the meeting. Maybe someone will have a chance to check it out.
~~http://nbviewer.ipython.org/github/TomAugspurger/pandas/blob/638bd3e361633a4c446ee02534e07b8a9332258a/style.ipynb~~
~~https://github.com/TomAugspurger/pandas/blob/stylely/style.ipynb~~
[latest example notebook](http://nbviewer.ipython.org/gist/TomAugspurger/82a53e11993469cb29a1)
| https://api.github.com/repos/pandas-dev/pandas/pulls/10250 | 2015-06-02T12:41:28Z | 2015-11-16T00:02:02Z | 2015-11-16T00:02:02Z | 2020-06-24T18:30:57Z |
Datetime resolution coercion | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index 9421ab0f841ac..2479e8aac8fd6 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -152,3 +152,4 @@ Bug Fixes
- Bug to handle masking empty ``DataFrame``(:issue:`10126`)
- Bug where MySQL interface could not handle numeric table/column names (:issue:`10255`)
+- Bug where ``read_csv`` and similar failed if making ``MultiIndex`` and ``date_parser`` returned ``datetime64`` array of other time resolution than ``[ns]``.
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index 59ecb29146315..d4c19ce5e339c 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -2057,18 +2057,20 @@ def converter(*date_cols):
infer_datetime_format=infer_datetime_format
)
except:
- return lib.try_parse_dates(strs, dayfirst=dayfirst)
+ return tools.to_datetime(
+ lib.try_parse_dates(strs, dayfirst=dayfirst))
else:
try:
- result = date_parser(*date_cols)
+ result = tools.to_datetime(date_parser(*date_cols))
if isinstance(result, datetime.datetime):
raise Exception('scalar parser')
return result
except Exception:
try:
- return lib.try_parse_dates(_concat_date_cols(date_cols),
- parser=date_parser,
- dayfirst=dayfirst)
+ return tools.to_datetime(
+ lib.try_parse_dates(_concat_date_cols(date_cols),
+ parser=date_parser,
+ dayfirst=dayfirst))
except Exception:
return generic_parser(date_parser, *date_cols)
diff --git a/pandas/io/tests/test_date_converters.py b/pandas/io/tests/test_date_converters.py
index ee537d94c4013..2b23556706f0c 100644
--- a/pandas/io/tests/test_date_converters.py
+++ b/pandas/io/tests/test_date_converters.py
@@ -11,7 +11,7 @@
import numpy as np
from numpy.testing.decorators import slow
-from pandas import DataFrame, Series, Index, isnull
+from pandas import DataFrame, Series, Index, MultiIndex, isnull
import pandas.io.parsers as parsers
from pandas.io.parsers import (read_csv, read_table, read_fwf,
TextParser)
@@ -119,6 +119,31 @@ def test_generic(self):
self.assertIn('ym', df)
self.assertEqual(df.ym.ix[0], date(2001, 1, 1))
+ def test_dateparser_resolution_if_not_ns(self):
+ # issue 10245
+ data = """\
+date,time,prn,rxstatus
+2013-11-03,19:00:00,126,00E80000
+2013-11-03,19:00:00,23,00E80000
+2013-11-03,19:00:00,13,00E80000
+"""
+
+ def date_parser(date, time):
+ datetime = np.array(date + 'T' + time + 'Z', dtype='datetime64[s]')
+ return datetime
+
+ df = read_csv(StringIO(data), date_parser=date_parser,
+ parse_dates={'datetime': ['date', 'time']},
+ index_col=['datetime', 'prn'])
+
+ datetimes = np.array(['2013-11-03T19:00:00Z']*3, dtype='datetime64[s]')
+ df_correct = DataFrame(data={'rxstatus': ['00E80000']*3},
+ index=MultiIndex.from_tuples(
+ [(datetimes[0], 126),
+ (datetimes[1], 23),
+ (datetimes[2], 13)],
+ names=['datetime', 'prn']))
+ assert_frame_equal(df, df_correct)
if __name__ == '__main__':
import nose
| Fixes #10245
Without this fix, if the `date_parse` function passed to e.g. `read_csv` outputs a `np.datetime64` array, then it must be of `ns` resolution. With this fix, all time resolutions may be used.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10249 | 2015-06-02T12:00:16Z | 2015-06-08T13:28:39Z | 2015-06-08T13:28:39Z | 2015-06-08T13:50:57Z |
Added link to aggregation and plotting time series | diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst
index f69f926296020..49ff987ca3549 100644
--- a/doc/source/cookbook.rst
+++ b/doc/source/cookbook.rst
@@ -745,6 +745,9 @@ Timeseries
`Vectorized Lookup
<http://stackoverflow.com/questions/13893227/vectorized-look-up-of-values-in-pandas-dataframe>`__
+`Aggregation and plotting time series
+<http://nipunbatra.github.io/2015/06/timeseries/>`__
+
Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series.
`How to rearrange a python pandas DataFrame?
<http://stackoverflow.com/questions/15432659/how-to-rearrange-a-python-pandas-dataframe>`__
| https://api.github.com/repos/pandas-dev/pandas/pulls/10248 | 2015-06-02T09:52:26Z | 2015-07-28T21:59:19Z | 2015-07-28T21:59:19Z | 2015-07-28T21:59:23Z | |
BUG: SparseSeries.abs() resets name | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 9b87c6c1332ab..dd151682339e1 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -83,7 +83,7 @@ Bug Fixes
- Bug in `plot` not defaulting to matplotlib `axes.grid` setting (:issue:`9792`)
- Bug in ``Series.align`` resets ``name`` when ``fill_value`` is specified (:issue:`10067`)
-
+- Bug in ``SparseSeries.abs`` resets ``name`` (:issue:`10241`)
- Bug in GroupBy.get_group raises ValueError when group key contains NaT (:issue:`6992`)
diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py
index 2c328e51b5090..f53cc66bee961 100644
--- a/pandas/sparse/series.py
+++ b/pandas/sparse/series.py
@@ -399,7 +399,7 @@ def abs(self):
res_sp_values = np.abs(self.sp_values)
return self._constructor(res_sp_values, index=self.index,
sparse_index=self.sp_index,
- fill_value=self.fill_value)
+ fill_value=self.fill_value).__finalize__(self)
def get(self, label, default=None):
"""
diff --git a/pandas/sparse/tests/test_sparse.py b/pandas/sparse/tests/test_sparse.py
index dd1d10f3d15ed..a7a78ba226a0b 100644
--- a/pandas/sparse/tests/test_sparse.py
+++ b/pandas/sparse/tests/test_sparse.py
@@ -509,6 +509,21 @@ def _check_inplace_op(iop, op):
_check_inplace_op(
getattr(operator, "i%s" % op), getattr(operator, op))
+ def test_abs(self):
+ s = SparseSeries([1, 2, -3], name='x')
+ expected = SparseSeries([1, 2, 3], name='x')
+ result = s.abs()
+ assert_sp_series_equal(result, expected)
+ self.assertEqual(result.name, 'x')
+
+ result = abs(s)
+ assert_sp_series_equal(result, expected)
+ self.assertEqual(result.name, 'x')
+
+ result = np.abs(s)
+ assert_sp_series_equal(result, expected)
+ self.assertEqual(result.name, 'x')
+
def test_reindex(self):
def _compare_with_series(sps, new_index):
spsre = sps.reindex(new_index)
diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py
index d7d83887298b1..57fd465993e14 100644
--- a/pandas/tests/test_panel.py
+++ b/pandas/tests/test_panel.py
@@ -404,6 +404,8 @@ def test_abs(self):
expected = np.abs(s)
assert_series_equal(result, expected)
assert_series_equal(result2, expected)
+ self.assertEqual(result.name, 'A')
+ self.assertEqual(result2.name, 'A')
class CheckIndexing(object):
| ```
# OK: name is preserved
s = pd.Series([1, 2, -3], name='a')
s.abs()
#0 1
#1 2
#2 3
# Name: a, dtype: int64
# NG: name is reset
s = pd.SparseSeries([1, 0, -3], name='a')
s.name
# 'a'
s.abs()
#0 1
#1 0
#2 3
# dtype: int64
# BlockIndex
# Block locations: array([0], dtype=int32)
# Block lengths: array([3], dtype=int32)
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10241 | 2015-05-30T21:47:52Z | 2015-06-02T10:39:04Z | 2015-06-02T10:39:04Z | 2015-06-02T19:26:12Z |
BUG: Series arithmetic methods incorrectly hold name | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 9b87c6c1332ab..842d7cf84e05a 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -73,7 +73,7 @@ Bug Fixes
- Bug in getting timezone data with ``dateutil`` on various platforms ( :issue:`9059`, :issue:`8639`, :issue:`9663`, :issue:`10121`)
- Bug in display datetimes with mixed frequencies uniformly; display 'ms' datetimes to the proper precision. (:issue:`10170`)
-
+- Bung in ``Series`` arithmetic methods may incorrectly hold names (:issue:`10068`)
- Bug in ``DatetimeIndex`` and ``TimedeltaIndex`` names are lost after timedelta arithmetics ( :issue:`9926`)
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 6367fb4fe0396..c54bd96f64c73 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1508,7 +1508,12 @@ def _binop(self, other, func, level=None, fill_value=None):
result = func(this_vals, other_vals)
name = _maybe_match_name(self, other)
- return self._constructor(result, index=new_index).__finalize__(self)
+ result = self._constructor(result, index=new_index, name=name)
+ result = result.__finalize__(self)
+ if name is None:
+ # When name is None, __finalize__ overwrites current name
+ result.name = None
+ return result
def combine(self, other, func, fill_value=nan):
"""
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 5a5b5fa2b226b..bbe942e607faf 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -264,10 +264,11 @@ def test_tab_completion(self):
self.assertTrue('dt' not in dir(s))
def test_binop_maybe_preserve_name(self):
-
# names match, preserve
result = self.ts * self.ts
self.assertEqual(result.name, self.ts.name)
+ result = self.ts.mul(self.ts)
+ self.assertEqual(result.name, self.ts.name)
result = self.ts * self.ts[:-2]
self.assertEqual(result.name, self.ts.name)
@@ -277,6 +278,22 @@ def test_binop_maybe_preserve_name(self):
cp.name = 'something else'
result = self.ts + cp
self.assertIsNone(result.name)
+ result = self.ts.add(cp)
+ self.assertIsNone(result.name)
+
+ ops = ['add', 'sub', 'mul', 'div', 'truediv', 'floordiv', 'mod', 'pow']
+ ops = ops + ['r' + op for op in ops]
+ for op in ops:
+ # names match, preserve
+ s = self.ts.copy()
+ result = getattr(s, op)(s)
+ self.assertEqual(result.name, self.ts.name)
+
+ # names don't match, don't preserve
+ cp = self.ts.copy()
+ cp.name = 'changed'
+ result = getattr(s, op)(cp)
+ self.assertIsNone(result.name)
def test_combine_first_name(self):
result = self.ts.combine_first(self.ts[:5])
| Closes #10068.
Should handle the case `_maybe_match_name` is `None`, because of continuous `__finalize__`.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10240 | 2015-05-30T21:14:49Z | 2015-06-02T10:38:48Z | 2015-06-02T10:38:48Z | 2015-06-02T19:26:12Z |
ENH: duplicated and drop_duplicates now accept keep kw | diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index 9f58ee2f8b99b..251d94cbdd911 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -1178,8 +1178,7 @@ takes as an argument the columns to use to identify duplicated rows.
- ``drop_duplicates`` removes duplicate rows.
By default, the first observed row of a duplicate set is considered unique, but
-each method has a ``take_last`` parameter that indicates the last observed row
-should be taken instead.
+each method has a ``keep`` parameter to specify targets to be kept.
.. ipython:: python
@@ -1187,8 +1186,11 @@ should be taken instead.
'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
'c' : np.random.randn(7)})
df2.duplicated(['a','b'])
+ df2.duplicated(['a','b'], keep='last')
+ df2.duplicated(['a','b'], keep=False)
df2.drop_duplicates(['a','b'])
- df2.drop_duplicates(['a','b'], take_last=True)
+ df2.drop_duplicates(['a','b'], keep='last')
+ df2.drop_duplicates(['a','b'], keep=False)
An alternative way to drop duplicates on the index is ``.groupby(level=0)`` combined with ``first()`` or ``last()``.
@@ -1199,7 +1201,7 @@ An alternative way to drop duplicates on the index is ``.groupby(level=0)`` comb
df3.groupby(level=0).first()
# a bit more verbose
- df3.reset_index().drop_duplicates(subset='b', take_last=False).set_index('b')
+ df3.reset_index().drop_duplicates(subset='b', keep='first').set_index('b')
.. _indexing.dictionarylike:
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 770ad8a268f11..e43ed5745eea3 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -142,6 +142,15 @@ Other enhancements
- ``pd.merge`` will now allow duplicate column names if they are not merged upon (:issue:`10639`).
- ``pd.pivot`` will now allow passing index as ``None`` (:issue:`3962`).
+- ``drop_duplicates`` and ``duplicated`` now accept ``keep`` keyword to target first, last, and all duplicates. ``take_last`` keyword is deprecated, see :ref:`deprecations <whatsnew_0170.deprecations>` (:issue:`6511`, :issue:`8505`)
+
+.. ipython :: python
+
+ s = pd.Series(['A', 'B', 'C', 'A', 'B', 'D'])
+ s.drop_duplicates()
+ s.drop_duplicates(keep='last')
+ s.drop_duplicates(keep=False)
+
.. _whatsnew_0170.api:
@@ -520,6 +529,7 @@ Deprecations
===================== =================================
- ``Categorical.name`` was deprecated to make ``Categorical`` more ``numpy.ndarray`` like. Use ``Series(cat, name="whatever")`` instead (:issue:`10482`).
+- ``drop_duplicates`` and ``duplicated``'s ``take_last`` keyword was removed in favor of ``keep``. (:issue:`6511`, :issue:`8505`)
.. _whatsnew_0170.prior_deprecations:
diff --git a/pandas/core/base.py b/pandas/core/base.py
index c3004aec60cc5..6d1c89a7a2f89 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -6,7 +6,7 @@
from pandas.core import common as com
import pandas.core.nanops as nanops
import pandas.lib as lib
-from pandas.util.decorators import Appender, cache_readonly
+from pandas.util.decorators import Appender, cache_readonly, deprecate_kwarg
from pandas.core.strings import StringMethods
from pandas.core.common import AbstractMethodError
@@ -543,8 +543,12 @@ def _dir_deletions(self):
Parameters
----------
- take_last : boolean, default False
- Take the last observed index in a group. Default first
+
+ keep : {'first', 'last', False}, default 'first'
+ - ``first`` : Drop duplicates except for the first occurrence.
+ - ``last`` : Drop duplicates except for the last occurrence.
+ - False : Drop all duplicates.
+ take_last : deprecated
%(inplace)s
Returns
@@ -552,9 +556,10 @@ def _dir_deletions(self):
deduplicated : %(klass)s
""")
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(_shared_docs['drop_duplicates'] % _indexops_doc_kwargs)
- def drop_duplicates(self, take_last=False, inplace=False):
- duplicated = self.duplicated(take_last=take_last)
+ def drop_duplicates(self, keep='first', inplace=False):
+ duplicated = self.duplicated(keep=keep)
result = self[np.logical_not(duplicated)]
if inplace:
return self._update_inplace(result)
@@ -566,18 +571,22 @@ def drop_duplicates(self, take_last=False, inplace=False):
Parameters
----------
- take_last : boolean, default False
- Take the last observed index in a group. Default first
+ keep : {'first', 'last', False}, default 'first'
+ - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
+ - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
+ - False : Mark all duplicates as ``True``.
+ take_last : deprecated
Returns
-------
duplicated : %(duplicated)s
""")
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(_shared_docs['duplicated'] % _indexops_doc_kwargs)
- def duplicated(self, take_last=False):
+ def duplicated(self, keep='first'):
keys = com._ensure_object(self.values)
- duplicated = lib.duplicated(keys, take_last=take_last)
+ duplicated = lib.duplicated(keys, keep=keep)
try:
return self._constructor(duplicated,
index=self.index).__finalize__(self)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index d8948bc82fe61..fe9c9bece1f79 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2866,8 +2866,9 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None,
else:
return result
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@deprecate_kwarg(old_arg_name='cols', new_arg_name='subset')
- def drop_duplicates(self, subset=None, take_last=False, inplace=False):
+ def drop_duplicates(self, subset=None, keep='first', inplace=False):
"""
Return DataFrame with duplicate rows removed, optionally only
considering certain columns
@@ -2877,8 +2878,11 @@ def drop_duplicates(self, subset=None, take_last=False, inplace=False):
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns
- take_last : boolean, default False
- Take the last observed row in a row. Defaults to the first row
+ keep : {'first', 'last', False}, default 'first'
+ - ``first`` : Drop duplicates except for the first occurrence.
+ - ``last`` : Drop duplicates except for the last occurrence.
+ - False : Drop all duplicates.
+ take_last : deprecated
inplace : boolean, default False
Whether to drop duplicates in place or to return a copy
cols : kwargs only argument of subset [deprecated]
@@ -2887,7 +2891,7 @@ def drop_duplicates(self, subset=None, take_last=False, inplace=False):
-------
deduplicated : DataFrame
"""
- duplicated = self.duplicated(subset, take_last=take_last)
+ duplicated = self.duplicated(subset, keep=keep)
if inplace:
inds, = (-duplicated).nonzero()
@@ -2896,8 +2900,9 @@ def drop_duplicates(self, subset=None, take_last=False, inplace=False):
else:
return self[-duplicated]
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@deprecate_kwarg(old_arg_name='cols', new_arg_name='subset')
- def duplicated(self, subset=None, take_last=False):
+ def duplicated(self, subset=None, keep='first'):
"""
Return boolean Series denoting duplicate rows, optionally only
considering certain columns
@@ -2907,9 +2912,13 @@ def duplicated(self, subset=None, take_last=False):
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns
- take_last : boolean, default False
- For a set of distinct duplicate rows, flag all but the last row as
- duplicated. Default is for all but the first row to be flagged
+ keep : {'first', 'last', False}, default 'first'
+ - ``first`` : Mark duplicates as ``True`` except for the
+ first occurrence.
+ - ``last`` : Mark duplicates as ``True`` except for the
+ last occurrence.
+ - False : Mark all duplicates as ``True``.
+ take_last : deprecated
cols : kwargs only argument of subset [deprecated]
Returns
@@ -2935,7 +2944,7 @@ def f(vals):
labels, shape = map(list, zip( * map(f, vals)))
ids = get_group_index(labels, shape, sort=False, xnull=False)
- return Series(duplicated_int64(ids, take_last), index=self.index)
+ return Series(duplicated_int64(ids, keep), index=self.index)
#----------------------------------------------------------------------
# Sorting
diff --git a/pandas/core/index.py b/pandas/core/index.py
index a9878f493251b..0af2f1f90b53f 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -16,7 +16,7 @@
from pandas.lib import Timestamp, Timedelta, is_datetime_array
from pandas.core.base import PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin, _shared_docs, PandasDelegate
from pandas.util.decorators import (Appender, Substitution, cache_readonly,
- deprecate)
+ deprecate, deprecate_kwarg)
import pandas.core.common as com
from pandas.core.common import (isnull, array_equivalent, is_dtype_equal, is_object_dtype,
_values_from_object, is_float, is_integer, is_iterator, is_categorical_dtype,
@@ -2623,13 +2623,15 @@ def drop(self, labels, errors='raise'):
indexer = indexer[~mask]
return self.delete(indexer)
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(_shared_docs['drop_duplicates'] % _index_doc_kwargs)
- def drop_duplicates(self, take_last=False):
- return super(Index, self).drop_duplicates(take_last=take_last)
+ def drop_duplicates(self, keep='first'):
+ return super(Index, self).drop_duplicates(keep=keep)
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(_shared_docs['duplicated'] % _index_doc_kwargs)
- def duplicated(self, take_last=False):
- return super(Index, self).duplicated(take_last=take_last)
+ def duplicated(self, keep='first'):
+ return super(Index, self).duplicated(keep=keep)
def _evaluate_with_timedelta_like(self, other, op, opstr):
raise TypeError("can only perform ops with timedelta like values")
@@ -3056,10 +3058,11 @@ def _engine(self):
def is_unique(self):
return not self.duplicated().any()
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(_shared_docs['duplicated'] % _index_doc_kwargs)
- def duplicated(self, take_last=False):
+ def duplicated(self, keep='first'):
from pandas.hashtable import duplicated_int64
- return duplicated_int64(self.codes.astype('i8'), take_last)
+ return duplicated_int64(self.codes.astype('i8'), keep)
def get_loc(self, key, method=None):
"""
@@ -4219,15 +4222,16 @@ def _has_complex_internals(self):
def is_unique(self):
return not self.duplicated().any()
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(_shared_docs['duplicated'] % _index_doc_kwargs)
- def duplicated(self, take_last=False):
+ def duplicated(self, keep='first'):
from pandas.core.groupby import get_group_index
from pandas.hashtable import duplicated_int64
shape = map(len, self.levels)
ids = get_group_index(self.labels, shape, sort=False, xnull=False)
- return duplicated_int64(ids, take_last)
+ return duplicated_int64(ids, keep)
def get_value(self, series, key):
# somewhat broken encapsulation
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 6586fa10935e6..87fde996aaa67 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -46,7 +46,7 @@
import pandas.core.datetools as datetools
import pandas.core.format as fmt
import pandas.core.nanops as nanops
-from pandas.util.decorators import Appender, cache_readonly
+from pandas.util.decorators import Appender, cache_readonly, deprecate_kwarg
import pandas.lib as lib
import pandas.tslib as tslib
@@ -1155,14 +1155,15 @@ def mode(self):
from pandas.core.algorithms import mode
return mode(self)
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(base._shared_docs['drop_duplicates'] % _shared_doc_kwargs)
- def drop_duplicates(self, take_last=False, inplace=False):
- return super(Series, self).drop_duplicates(take_last=take_last,
- inplace=inplace)
+ def drop_duplicates(self, keep='first', inplace=False):
+ return super(Series, self).drop_duplicates(keep=keep, inplace=inplace)
+ @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'})
@Appender(base._shared_docs['duplicated'] % _shared_doc_kwargs)
- def duplicated(self, take_last=False):
- return super(Series, self).duplicated(take_last=take_last)
+ def duplicated(self, keep='first'):
+ return super(Series, self).duplicated(keep=keep)
def idxmin(self, axis=None, out=None, skipna=True):
"""
diff --git a/pandas/hashtable.pyx b/pandas/hashtable.pyx
index 3b3ea9fa032f8..7dbd1b45c938f 100644
--- a/pandas/hashtable.pyx
+++ b/pandas/hashtable.pyx
@@ -1026,25 +1026,41 @@ def mode_int64(int64_t[:] values):
@cython.wraparound(False)
@cython.boundscheck(False)
-def duplicated_int64(ndarray[int64_t, ndim=1] values, int take_last):
+def duplicated_int64(ndarray[int64_t, ndim=1] values, object keep='first'):
cdef:
- int ret = 0
+ int ret = 0, value, k
Py_ssize_t i, n = len(values)
kh_int64_t * table = kh_init_int64()
ndarray[uint8_t, ndim=1, cast=True] out = np.empty(n, dtype='bool')
kh_resize_int64(table, min(n, _SIZE_HINT_LIMIT))
- with nogil:
- if take_last:
+ if keep not in ('last', 'first', False):
+ raise ValueError('keep must be either "first", "last" or False')
+
+ if keep == 'last':
+ with nogil:
for i from n > i >=0:
kh_put_int64(table, values[i], &ret)
out[i] = ret == 0
- else:
+ elif keep == 'first':
+ with nogil:
for i from 0 <= i < n:
kh_put_int64(table, values[i], &ret)
out[i] = ret == 0
-
+ else:
+ with nogil:
+ for i from 0 <= i < n:
+ value = values[i]
+ k = kh_get_int64(table, value)
+ if k != table.n_buckets:
+ out[table.vals[k]] = 1
+ out[i] = 1
+ else:
+ k = kh_put_int64(table, value, &ret)
+ table.keys[k] = value
+ table.vals[k] = i
+ out[i] = 0
kh_destroy_int64(table)
return out
diff --git a/pandas/lib.pyx b/pandas/lib.pyx
index e839210fbbada..07f0c89535a77 100644
--- a/pandas/lib.pyx
+++ b/pandas/lib.pyx
@@ -1348,35 +1348,47 @@ def fast_zip_fillna(list ndarrays, fill_value=pandas_null):
return result
-def duplicated(ndarray[object] values, take_last=False):
+
+def duplicated(ndarray[object] values, object keep='first'):
cdef:
Py_ssize_t i, n
- set seen = set()
+ dict seen = dict()
object row
n = len(values)
cdef ndarray[uint8_t] result = np.zeros(n, dtype=np.uint8)
- if take_last:
+ if keep == 'last':
for i from n > i >= 0:
row = values[i]
-
if row in seen:
result[i] = 1
else:
- seen.add(row)
+ seen[row] = i
result[i] = 0
- else:
+ elif keep == 'first':
for i from 0 <= i < n:
row = values[i]
if row in seen:
result[i] = 1
else:
- seen.add(row)
+ seen[row] = i
result[i] = 0
+ elif keep is False:
+ for i from 0 <= i < n:
+ row = values[i]
+ if row in seen:
+ result[i] = 1
+ result[seen[row]] = 1
+ else:
+ seen[row] = i
+ result[i] = 0
+ else:
+ raise ValueError('keep must be either "first", "last" or False')
return result.view(np.bool_)
+
def generate_slices(ndarray[int64_t] labels, Py_ssize_t ngroups):
cdef:
Py_ssize_t i, group_size, n, start
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index d47e7dbe751c7..066b359d72b5c 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -683,6 +683,10 @@ def test_factorize(self):
def test_duplicated_drop_duplicates(self):
# GH 4060
+
+ import warnings
+ warnings.simplefilter('always')
+
for original in self.objs:
if isinstance(original, Index):
@@ -714,15 +718,36 @@ def test_duplicated_drop_duplicates(self):
self.assertTrue(duplicated.dtype == bool)
tm.assert_index_equal(idx.drop_duplicates(), original)
- last_base = [False] * len(idx)
- last_base[3] = True
- last_base[5] = True
- expected = np.array(last_base)
- duplicated = idx.duplicated(take_last=True)
+ base = [False] * len(idx)
+ base[3] = True
+ base[5] = True
+ expected = np.array(base)
+
+ duplicated = idx.duplicated(keep='last')
+ tm.assert_numpy_array_equal(duplicated, expected)
+ self.assertTrue(duplicated.dtype == bool)
+ result = idx.drop_duplicates(keep='last')
+ tm.assert_index_equal(result, idx[~expected])
+
+ # deprecate take_last
+ with tm.assert_produces_warning(FutureWarning):
+ duplicated = idx.duplicated(take_last=True)
+ tm.assert_numpy_array_equal(duplicated, expected)
+ self.assertTrue(duplicated.dtype == bool)
+ with tm.assert_produces_warning(FutureWarning):
+ result = idx.drop_duplicates(take_last=True)
+ tm.assert_index_equal(result, idx[~expected])
+
+ base = [False] * len(original) + [True, True]
+ base[3] = True
+ base[5] = True
+ expected = np.array(base)
+
+ duplicated = idx.duplicated(keep=False)
tm.assert_numpy_array_equal(duplicated, expected)
self.assertTrue(duplicated.dtype == bool)
- tm.assert_index_equal(idx.drop_duplicates(take_last=True),
- idx[~np.array(last_base)])
+ result = idx.drop_duplicates(keep=False)
+ tm.assert_index_equal(result, idx[~expected])
with tm.assertRaisesRegexp(TypeError,
"drop_duplicates\(\) got an unexpected keyword argument"):
@@ -745,13 +770,29 @@ def test_duplicated_drop_duplicates(self):
tm.assert_series_equal(s.duplicated(), expected)
tm.assert_series_equal(s.drop_duplicates(), original)
- last_base = [False] * len(idx)
- last_base[3] = True
- last_base[5] = True
- expected = Series(last_base, index=idx, name='a')
- tm.assert_series_equal(s.duplicated(take_last=True), expected)
- tm.assert_series_equal(s.drop_duplicates(take_last=True),
- s[~np.array(last_base)])
+ base = [False] * len(idx)
+ base[3] = True
+ base[5] = True
+ expected = Series(base, index=idx, name='a')
+
+ tm.assert_series_equal(s.duplicated(keep='last'), expected)
+ tm.assert_series_equal(s.drop_duplicates(keep='last'),
+ s[~np.array(base)])
+
+ # deprecate take_last
+ with tm.assert_produces_warning(FutureWarning):
+ tm.assert_series_equal(s.duplicated(take_last=True), expected)
+ with tm.assert_produces_warning(FutureWarning):
+ tm.assert_series_equal(s.drop_duplicates(take_last=True),
+ s[~np.array(base)])
+ base = [False] * len(original) + [True, True]
+ base[3] = True
+ base[5] = True
+ expected = Series(base, index=idx, name='a')
+
+ tm.assert_series_equal(s.duplicated(keep=False), expected)
+ tm.assert_series_equal(s.drop_duplicates(keep=False),
+ s[~np.array(base)])
s.drop_duplicates(inplace=True)
tm.assert_series_equal(s, original)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 77ef5fecf22c9..72eea5162caa5 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -7848,7 +7848,7 @@ def test_dropna_multiple_axes(self):
inp.dropna(how='all', axis=(0, 1), inplace=True)
assert_frame_equal(inp, expected)
- def test_drop_duplicates(self):
+ def test_aaa_drop_duplicates(self):
df = DataFrame({'AAA': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'bar', 'foo'],
'B': ['one', 'one', 'two', 'two',
@@ -7861,10 +7861,21 @@ def test_drop_duplicates(self):
expected = df[:2]
assert_frame_equal(result, expected)
- result = df.drop_duplicates('AAA', take_last=True)
+ result = df.drop_duplicates('AAA', keep='last')
expected = df.ix[[6, 7]]
assert_frame_equal(result, expected)
+ result = df.drop_duplicates('AAA', keep=False)
+ expected = df.ix[[]]
+ assert_frame_equal(result, expected)
+ self.assertEqual(len(result), 0)
+
+ # deprecate take_last
+ with tm.assert_produces_warning(FutureWarning):
+ result = df.drop_duplicates('AAA', take_last=True)
+ expected = df.ix[[6, 7]]
+ assert_frame_equal(result, expected)
+
# multi column
expected = df.ix[[0, 1, 2, 3]]
result = df.drop_duplicates(np.array(['AAA', 'B']))
@@ -7872,6 +7883,15 @@ def test_drop_duplicates(self):
result = df.drop_duplicates(['AAA', 'B'])
assert_frame_equal(result, expected)
+ result = df.drop_duplicates(('AAA', 'B'), keep='last')
+ expected = df.ix[[0, 5, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates(('AAA', 'B'), keep=False)
+ expected = df.ix[[0]]
+ assert_frame_equal(result, expected)
+
+ # deprecate take_last
result = df.drop_duplicates(('AAA', 'B'), take_last=True)
expected = df.ix[[0, 5, 6, 7]]
assert_frame_equal(result, expected)
@@ -7884,10 +7904,53 @@ def test_drop_duplicates(self):
expected = df2.drop_duplicates(['AAA', 'B'])
assert_frame_equal(result, expected)
+ result = df2.drop_duplicates(keep='last')
+ expected = df2.drop_duplicates(['AAA', 'B'], keep='last')
+ assert_frame_equal(result, expected)
+
+ result = df2.drop_duplicates(keep=False)
+ expected = df2.drop_duplicates(['AAA', 'B'], keep=False)
+ assert_frame_equal(result, expected)
+
+ # deprecate take_last
result = df2.drop_duplicates(take_last=True)
expected = df2.drop_duplicates(['AAA', 'B'], take_last=True)
assert_frame_equal(result, expected)
+ def test_drop_duplicates_for_take_all(self):
+ df = DataFrame({'AAA': ['foo', 'bar', 'baz', 'bar',
+ 'foo', 'bar', 'qux', 'foo'],
+ 'B': ['one', 'one', 'two', 'two',
+ 'two', 'two', 'one', 'two'],
+ 'C': [1, 1, 2, 2, 2, 2, 1, 2],
+ 'D': lrange(8)})
+
+ # single column
+ result = df.drop_duplicates('AAA')
+ expected = df.iloc[[0, 1, 2, 6]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('AAA', keep='last')
+ expected = df.iloc[[2, 5, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('AAA', keep=False)
+ expected = df.iloc[[2, 6]]
+ assert_frame_equal(result, expected)
+
+ # multiple columns
+ result = df.drop_duplicates(['AAA', 'B'])
+ expected = df.iloc[[0, 1, 2, 3, 4, 6]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates(['AAA', 'B'], keep='last')
+ expected = df.iloc[[0, 1, 2, 5, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates(['AAA', 'B'], keep=False)
+ expected = df.iloc[[0, 1, 2, 6]]
+ assert_frame_equal(result, expected)
+
def test_drop_duplicates_deprecated_warning(self):
df = DataFrame({'AAA': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'bar', 'foo'],
@@ -7914,6 +7977,14 @@ def test_drop_duplicates_deprecated_warning(self):
self.assertRaises(TypeError, df.drop_duplicates,
kwargs={'subset': 'AAA', 'bad_arg': True})
+ # deprecate take_last
+ # Raises warning
+ with tm.assert_produces_warning(FutureWarning):
+ result = df.drop_duplicates(take_last=False, subset='AAA')
+ assert_frame_equal(result, expected)
+
+ self.assertRaises(ValueError, df.drop_duplicates, keep='invalid_name')
+
def test_drop_duplicates_tuple(self):
df = DataFrame({('AA', 'AB'): ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'bar', 'foo'],
@@ -7927,6 +7998,16 @@ def test_drop_duplicates_tuple(self):
expected = df[:2]
assert_frame_equal(result, expected)
+ result = df.drop_duplicates(('AA', 'AB'), keep='last')
+ expected = df.ix[[6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates(('AA', 'AB'), keep=False)
+ expected = df.ix[[]] # empty df
+ self.assertEqual(len(result), 0)
+ assert_frame_equal(result, expected)
+
+ # deprecate take_last
result = df.drop_duplicates(('AA', 'AB'), take_last=True)
expected = df.ix[[6, 7]]
assert_frame_equal(result, expected)
@@ -7950,6 +8031,16 @@ def test_drop_duplicates_NA(self):
expected = df.ix[[0, 2, 3]]
assert_frame_equal(result, expected)
+ result = df.drop_duplicates('A', keep='last')
+ expected = df.ix[[1, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('A', keep=False)
+ expected = df.ix[[]] # empty df
+ assert_frame_equal(result, expected)
+ self.assertEqual(len(result), 0)
+
+ # deprecate take_last
result = df.drop_duplicates('A', take_last=True)
expected = df.ix[[1, 6, 7]]
assert_frame_equal(result, expected)
@@ -7959,6 +8050,15 @@ def test_drop_duplicates_NA(self):
expected = df.ix[[0, 2, 3, 6]]
assert_frame_equal(result, expected)
+ result = df.drop_duplicates(['A', 'B'], keep='last')
+ expected = df.ix[[1, 5, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates(['A', 'B'], keep=False)
+ expected = df.ix[[6]]
+ assert_frame_equal(result, expected)
+
+ # deprecate take_last
result = df.drop_duplicates(['A', 'B'], take_last=True)
expected = df.ix[[1, 5, 6, 7]]
assert_frame_equal(result, expected)
@@ -7976,6 +8076,16 @@ def test_drop_duplicates_NA(self):
expected = df[:2]
assert_frame_equal(result, expected)
+ result = df.drop_duplicates('C', keep='last')
+ expected = df.ix[[3, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('C', keep=False)
+ expected = df.ix[[]] # empty df
+ assert_frame_equal(result, expected)
+ self.assertEqual(len(result), 0)
+
+ # deprecate take_last
result = df.drop_duplicates('C', take_last=True)
expected = df.ix[[3, 7]]
assert_frame_equal(result, expected)
@@ -7985,10 +8095,53 @@ def test_drop_duplicates_NA(self):
expected = df.ix[[0, 1, 2, 4]]
assert_frame_equal(result, expected)
+ result = df.drop_duplicates(['C', 'B'], keep='last')
+ expected = df.ix[[1, 3, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates(['C', 'B'], keep=False)
+ expected = df.ix[[1]]
+ assert_frame_equal(result, expected)
+
+ # deprecate take_last
result = df.drop_duplicates(['C', 'B'], take_last=True)
expected = df.ix[[1, 3, 6, 7]]
assert_frame_equal(result, expected)
+ def test_drop_duplicates_NA_for_take_all(self):
+ # none
+ df = DataFrame({'A': [None, None, 'foo', 'bar',
+ 'foo', 'baz', 'bar', 'qux'],
+ 'C': [1.0, np.nan, np.nan, np.nan, 1., 2., 3, 1.]})
+
+ # single column
+ result = df.drop_duplicates('A')
+ expected = df.iloc[[0, 2, 3, 5, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('A', keep='last')
+ expected = df.iloc[[1, 4, 5, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('A', keep=False)
+ expected = df.iloc[[5, 7]]
+ assert_frame_equal(result, expected)
+
+ # nan
+
+ # single column
+ result = df.drop_duplicates('C')
+ expected = df.iloc[[0, 1, 5, 6]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('C', keep='last')
+ expected = df.iloc[[3, 5, 6, 7]]
+ assert_frame_equal(result, expected)
+
+ result = df.drop_duplicates('C', keep=False)
+ expected = df.iloc[[5, 6]]
+ assert_frame_equal(result, expected)
+
def test_drop_duplicates_inplace(self):
orig = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'bar', 'foo'],
@@ -8004,6 +8157,20 @@ def test_drop_duplicates_inplace(self):
result = df
assert_frame_equal(result, expected)
+ df = orig.copy()
+ df.drop_duplicates('A', keep='last', inplace=True)
+ expected = orig.ix[[6, 7]]
+ result = df
+ assert_frame_equal(result, expected)
+
+ df = orig.copy()
+ df.drop_duplicates('A', keep=False, inplace=True)
+ expected = orig.ix[[]]
+ result = df
+ assert_frame_equal(result, expected)
+ self.assertEqual(len(df), 0)
+
+ # deprecate take_last
df = orig.copy()
df.drop_duplicates('A', take_last=True, inplace=True)
expected = orig.ix[[6, 7]]
@@ -8017,6 +8184,19 @@ def test_drop_duplicates_inplace(self):
result = df
assert_frame_equal(result, expected)
+ df = orig.copy()
+ df.drop_duplicates(['A', 'B'], keep='last', inplace=True)
+ expected = orig.ix[[0, 5, 6, 7]]
+ result = df
+ assert_frame_equal(result, expected)
+
+ df = orig.copy()
+ df.drop_duplicates(['A', 'B'], keep=False, inplace=True)
+ expected = orig.ix[[0]]
+ result = df
+ assert_frame_equal(result, expected)
+
+ # deprecate take_last
df = orig.copy()
df.drop_duplicates(['A', 'B'], take_last=True, inplace=True)
expected = orig.ix[[0, 5, 6, 7]]
@@ -8033,6 +8213,19 @@ def test_drop_duplicates_inplace(self):
result = df2
assert_frame_equal(result, expected)
+ df2 = orig2.copy()
+ df2.drop_duplicates(keep='last', inplace=True)
+ expected = orig2.drop_duplicates(['A', 'B'], keep='last')
+ result = df2
+ assert_frame_equal(result, expected)
+
+ df2 = orig2.copy()
+ df2.drop_duplicates(keep=False, inplace=True)
+ expected = orig2.drop_duplicates(['A', 'B'], keep=False)
+ result = df2
+ assert_frame_equal(result, expected)
+
+ # deprecate take_last
df2 = orig2.copy()
df2.drop_duplicates(take_last=True, inplace=True)
expected = orig2.drop_duplicates(['A', 'B'], take_last=True)
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index 15023b77694e6..c0f67d76b7d0d 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -4711,9 +4711,9 @@ def check(nlevels, with_nulls):
labels = [np.random.choice(n, k * n) for lev in levels]
mi = MultiIndex(levels=levels, labels=labels)
- for take_last in [False, True]:
- left = mi.duplicated(take_last=take_last)
- right = pd.lib.duplicated(mi.values, take_last=take_last)
+ for keep in ['first', 'last', False]:
+ left = mi.duplicated(keep=keep)
+ right = pd.lib.duplicated(mi.values, keep=keep)
tm.assert_numpy_array_equal(left, right)
# GH5873
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index 65ba5fd036a35..fbe4eefabe02d 100644
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -2135,6 +2135,21 @@ def test_duplicated_drop_duplicates(self):
expected = MultiIndex.from_arrays(([1, 2, 3, 2 ,3], [1, 1, 1, 2, 2]))
tm.assert_index_equal(idx.drop_duplicates(), expected)
+ expected = np.array([True, False, False, False, False, False])
+ duplicated = idx.duplicated(keep='last')
+ tm.assert_numpy_array_equal(duplicated, expected)
+ self.assertTrue(duplicated.dtype == bool)
+ expected = MultiIndex.from_arrays(([2, 3, 1, 2 ,3], [1, 1, 1, 2, 2]))
+ tm.assert_index_equal(idx.drop_duplicates(keep='last'), expected)
+
+ expected = np.array([True, False, False, True, False, False])
+ duplicated = idx.duplicated(keep=False)
+ tm.assert_numpy_array_equal(duplicated, expected)
+ self.assertTrue(duplicated.dtype == bool)
+ expected = MultiIndex.from_arrays(([2, 3, 2 ,3], [1, 1, 2, 2]))
+ tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
+
+ # deprecate take_last
expected = np.array([True, False, False, False, False, False])
duplicated = idx.duplicated(take_last=True)
tm.assert_numpy_array_equal(duplicated, expected)
diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py
index 66a38cd858846..31843616956f6 100644
--- a/pandas/tests/test_series.py
+++ b/pandas/tests/test_series.py
@@ -4782,29 +4782,63 @@ def test_axis_alias(self):
self.assertEqual(s._get_axis_name('rows'), 'index')
def test_drop_duplicates(self):
- s = Series([1, 2, 3, 3])
+ # check both int and object
+ for s in [Series([1, 2, 3, 3]), Series(['1', '2', '3', '3'])]:
+ expected = Series([False, False, False, True])
+ assert_series_equal(s.duplicated(), expected)
+ assert_series_equal(s.drop_duplicates(), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(inplace=True)
+ assert_series_equal(sc, s[~expected])
- result = s.duplicated()
- expected = Series([False, False, False, True])
- assert_series_equal(result, expected)
+ expected = Series([False, False, True, False])
+ assert_series_equal(s.duplicated(keep='last'), expected)
+ assert_series_equal(s.drop_duplicates(keep='last'), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(keep='last', inplace=True)
+ assert_series_equal(sc, s[~expected])
+ # deprecate take_last
+ assert_series_equal(s.duplicated(take_last=True), expected)
+ assert_series_equal(s.drop_duplicates(take_last=True), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(take_last=True, inplace=True)
+ assert_series_equal(sc, s[~expected])
- result = s.duplicated(take_last=True)
- expected = Series([False, False, True, False])
- assert_series_equal(result, expected)
+ expected = Series([False, False, True, True])
+ assert_series_equal(s.duplicated(keep=False), expected)
+ assert_series_equal(s.drop_duplicates(keep=False), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(keep=False, inplace=True)
+ assert_series_equal(sc, s[~expected])
+
+ for s in [Series([1, 2, 3, 5, 3, 2, 4]),
+ Series(['1', '2', '3', '5', '3', '2', '4'])]:
+ expected = Series([False, False, False, False, True, True, False])
+ assert_series_equal(s.duplicated(), expected)
+ assert_series_equal(s.drop_duplicates(), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(inplace=True)
+ assert_series_equal(sc, s[~expected])
- result = s.drop_duplicates()
- expected = s[[True, True, True, False]]
- assert_series_equal(result, expected)
- sc = s.copy()
- sc.drop_duplicates(inplace=True)
- assert_series_equal(sc, expected)
+ expected = Series([False, True, True, False, False, False, False])
+ assert_series_equal(s.duplicated(keep='last'), expected)
+ assert_series_equal(s.drop_duplicates(keep='last'), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(keep='last', inplace=True)
+ assert_series_equal(sc, s[~expected])
+ # deprecate take_last
+ assert_series_equal(s.duplicated(take_last=True), expected)
+ assert_series_equal(s.drop_duplicates(take_last=True), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(take_last=True, inplace=True)
+ assert_series_equal(sc, s[~expected])
- result = s.drop_duplicates(take_last=True)
- expected = s[[True, True, False, True]]
- assert_series_equal(result, expected)
- sc = s.copy()
- sc.drop_duplicates(take_last=True, inplace=True)
- assert_series_equal(sc, expected)
+ expected = Series([False, True, True, False, True, True, False])
+ assert_series_equal(s.duplicated(keep=False), expected)
+ assert_series_equal(s.drop_duplicates(keep=False), s[~expected])
+ sc = s.copy()
+ sc.drop_duplicates(keep=False, inplace=True)
+ assert_series_equal(sc, s[~expected])
def test_sort(self):
ts = self.ts.copy()
diff --git a/pandas/tests/test_tseries.py b/pandas/tests/test_tseries.py
index 035b3ac07342d..f10d541a7e23b 100644
--- a/pandas/tests/test_tseries.py
+++ b/pandas/tests/test_tseries.py
@@ -275,10 +275,18 @@ def test_duplicated_with_nas():
expected = [False, False, False, True, False, True]
assert(np.array_equal(result, expected))
- result = lib.duplicated(keys, take_last=True)
+ result = lib.duplicated(keys, keep='first')
+ expected = [False, False, False, True, False, True]
+ assert(np.array_equal(result, expected))
+
+ result = lib.duplicated(keys, keep='last')
expected = [True, False, True, False, False, False]
assert(np.array_equal(result, expected))
+ result = lib.duplicated(keys, keep=False)
+ expected = [True, False, True, True, False, True]
+ assert(np.array_equal(result, expected))
+
keys = np.empty(8, dtype=object)
for i, t in enumerate(zip([0, 0, nan, nan] * 2, [0, nan, 0, nan] * 2)):
keys[i] = t
@@ -289,10 +297,14 @@ def test_duplicated_with_nas():
expected = falses + trues
assert(np.array_equal(result, expected))
- result = lib.duplicated(keys, take_last=True)
+ result = lib.duplicated(keys, keep='last')
expected = trues + falses
assert(np.array_equal(result, expected))
+ result = lib.duplicated(keys, keep=False)
+ expected = trues + trues
+ assert(np.array_equal(result, expected))
+
def test_maybe_booleans_to_slice():
arr = np.array([0, 0, 1, 1, 1, 0, 1], dtype=np.uint8)
| Closes #6511, Closes #8505.
Introduce `keep` kw to handle first / last / all duplicates based on the discussion above. If there is better API/kw, please lmk.
### `duplicated`
- `keep='first'` (default): mark duplicates as `True` except for the first occurrence.
- `keep='last'`: mark duplicates as `True` except for the last occurrence.
- `keep=False`: mark all duplicates as `True`.
### `drop_duplicates`
- `keep='first'` (default): drop duplicates except for the first occurrence.
- `keep='last'`: drop duplicates except for the last occurrence.
- `keep=False`: drop all duplicates.
``` python
import pandas as pd
s = pd.Series(['A', 'B', 'C', 'A', 'B', 'D'])
# mark duplicates except for the first occurrence
s.duplicated()
#0 False
#1 False
#2 False
#3 True
#4 True
#5 False
# dtype: bool
# mark duplicates except for the last occurrence
s.duplicated(keep='last')
#0 True
#1 True
#2 False
#3 False
#4 False
#5 False
# dtype: bool
# mark all duplicates
s.duplicated(keep=False)
#0 True
#1 True
#2 False
#3 True
#4 True
#5 False
# dtype: bool
# drop duplicates except for the first occurrence
s.drop_duplicates()
#0 A
#1 B
#2 C
#5 D
# dtype: object
# drop duplicates except for the last occurrence
s.drop_duplicates(keep='last')
#2 C
#3 A
#4 B
#5 D
# dtype: object
# drop all duplicates
s.drop_duplicates(keep=False)
#2 C
#5 D
# dtype: object
```
CC @shoyer, @wikiped, @socheon
| https://api.github.com/repos/pandas-dev/pandas/pulls/10236 | 2015-05-30T13:00:05Z | 2015-08-08T19:46:01Z | 2015-08-08T19:46:01Z | 2024-03-23T16:32:22Z |
Close mysql connection in TestXMySQL to prevent tests freezing | diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index fa7debeb228ce..9576f80696350 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -2195,6 +2195,13 @@ def setUp(self):
"[pandas] in your system's mysql default file, "
"typically located at ~/.my.cnf or /etc/.my.cnf. ")
+ def tearDown(self):
+ from pymysql.err import Error
+ try:
+ self.db.close()
+ except Error:
+ pass
+
def test_basic(self):
_skip_if_no_pymysql()
frame = tm.makeTimeDataFrame()
| The unit tests freeze on my machine without this change because you can't drop a table until other connections that are using that table disconnect.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10230 | 2015-05-29T09:14:01Z | 2015-05-29T12:21:07Z | 2015-05-29T12:21:07Z | 2015-06-02T19:26:12Z |
BUG: plot doesnt default to matplotlib axes.grid setting (#9792) | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index d7955d7210ade..a7917e81f7057 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -76,5 +76,6 @@ Bug Fixes
- Bug in ``DatetimeIndex`` and ``TimedeltaIndex`` names are lost after timedelta arithmetics ( :issue:`9926`)
- Bug in `Series.plot(label="LABEL")` not correctly setting the label (:issue:`10119`)
+- Bug in `plot` not defaulting to matplotlib `axes.grid` setting (:issue:`9792`)
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py
index 4c9d5a9207dd7..82f4b8c05ca06 100644
--- a/pandas/tests/test_graphics.py
+++ b/pandas/tests/test_graphics.py
@@ -439,6 +439,38 @@ def _check_box_return_type(self, returned, return_type, expected_keys=None,
else:
raise AssertionError
+ def _check_grid_settings(self, obj, kinds, kws={}):
+ # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
+
+ import matplotlib as mpl
+
+ def is_grid_on():
+ xoff = all(not g.gridOn for g in self.plt.gca().xaxis.get_major_ticks())
+ yoff = all(not g.gridOn for g in self.plt.gca().yaxis.get_major_ticks())
+ return not(xoff and yoff)
+
+ spndx=1
+ for kind in kinds:
+ self.plt.subplot(1,4*len(kinds),spndx); spndx+=1
+ mpl.rc('axes',grid=False)
+ obj.plot(kind=kind, **kws)
+ self.assertFalse(is_grid_on())
+
+ self.plt.subplot(1,4*len(kinds),spndx); spndx+=1
+ mpl.rc('axes',grid=True)
+ obj.plot(kind=kind, grid=False, **kws)
+ self.assertFalse(is_grid_on())
+
+ if kind != 'pie':
+ self.plt.subplot(1,4*len(kinds),spndx); spndx+=1
+ mpl.rc('axes',grid=True)
+ obj.plot(kind=kind, **kws)
+ self.assertTrue(is_grid_on())
+
+ self.plt.subplot(1,4*len(kinds),spndx); spndx+=1
+ mpl.rc('axes',grid=False)
+ obj.plot(kind=kind, grid=True, **kws)
+ self.assertTrue(is_grid_on())
@tm.mplskip
class TestSeriesPlots(TestPlotBase):
@@ -1108,6 +1140,12 @@ def test_table(self):
_check_plot_works(self.series.plot, table=True)
_check_plot_works(self.series.plot, table=self.series)
+ @slow
+ def test_series_grid_settings(self):
+ # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
+ self._check_grid_settings(Series([1,2,3]),
+ plotting._series_kinds + plotting._common_kinds)
+
@tm.mplskip
class TestDataFramePlots(TestPlotBase):
@@ -3426,6 +3464,12 @@ def test_sharey_and_ax(self):
"y label is invisible but shouldn't")
+ @slow
+ def test_df_grid_settings(self):
+ # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
+ self._check_grid_settings(DataFrame({'a':[1,2,3],'b':[2,3,4]}),
+ plotting._dataframe_kinds, kws={'x':'a','y':'b'})
+
@tm.mplskip
class TestDataFrameGroupByPlots(TestPlotBase):
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 04dd4d3395684..76685e2589012 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -810,7 +810,7 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=None,
self.rot = self._default_rot
if grid is None:
- grid = False if secondary_y else True
+ grid = False if secondary_y else self.plt.rcParams['axes.grid']
self.grid = grid
self.legend = legend
| Closes #9792 .
| https://api.github.com/repos/pandas-dev/pandas/pulls/10212 | 2015-05-26T23:08:37Z | 2015-05-27T02:48:47Z | 2015-05-27T02:48:47Z | 2015-06-02T19:26:12Z |
Update bq link | diff --git a/README.md b/README.md
index c76fbe7df9e6b..8623ee170d154 100644
--- a/README.md
+++ b/README.md
@@ -123,7 +123,7 @@ conda install pandas
- xlrd >= 0.9.0
- [XlsxWriter](https://pypi.python.org/pypi/XlsxWriter)
- Alternative Excel writer.
-- [Google bq Command Line Tool](https://developers.google.com/bigquery/bq-command-line-tool/)
+- [Google bq Command Line Tool](https://cloud.google.com/bigquery/bq-command-line-tool)
- Needed for `pandas.io.gbq`
- [boto](https://pypi.python.org/pypi/boto): necessary for Amazon S3 access.
- One of the following combinations of libraries is needed to use the
| https://api.github.com/repos/pandas-dev/pandas/pulls/10210 | 2015-05-26T16:32:54Z | 2015-05-26T17:28:24Z | 2015-05-26T17:28:24Z | 2015-05-26T17:28:26Z | |
DOC: Added nlargest/nsmallest to API docs | diff --git a/doc/source/api.rst b/doc/source/api.rst
index 3b2e8b65768bb..f5ba03afc9f19 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -358,6 +358,8 @@ Computations / Descriptive Stats
Series.median
Series.min
Series.mode
+ Series.nlargest
+ Series.nsmallest
Series.pct_change
Series.prod
Series.quantile
| Fixes #10145
| https://api.github.com/repos/pandas-dev/pandas/pulls/10206 | 2015-05-25T18:00:24Z | 2015-05-26T10:32:56Z | 2015-05-26T10:32:56Z | 2015-06-02T19:26:12Z |
DOC: Updated to mention axis='index' and axis='columns | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index ed01323eb9a27..f36108262432d 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2745,7 +2745,7 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None,
Parameters
----------
- axis : {0, 1}, or tuple/list thereof
+ axis : {0 or 'index', 1 or 'columns'}, or tuple/list thereof
Pass tuple or list to drop on multiple axes
how : {'any', 'all'}
* any : if any NA values are present, drop that label
@@ -2890,7 +2890,7 @@ def sort(self, columns=None, axis=0, ascending=True,
ascending : boolean or list, default True
Sort ascending vs. descending. Specify list for multiple sort
orders
- axis : {0, 1}
+ axis : {0 or 'index', 1 or 'columns'}, default 0
Sort index/rows versus columns
inplace : boolean, default False
Sort the DataFrame without creating a new instance
@@ -2919,7 +2919,7 @@ def sort_index(self, axis=0, by=None, ascending=True, inplace=False,
Parameters
----------
- axis : {0, 1}
+ axis : {0 or 'index', 1 or 'columns'}, default 0
Sort index/rows versus columns
by : object
Column name(s) in frame. Accepts a column name or a list
@@ -3027,7 +3027,7 @@ def sortlevel(self, level=0, axis=0, ascending=True,
Parameters
----------
level : int
- axis : {0, 1}
+ axis : {0 or 'index', 1 or 'columns'}, default 0
ascending : boolean, default True
inplace : boolean, default False
Sort the DataFrame without creating a new instance
@@ -3639,9 +3639,9 @@ def apply(self, func, axis=0, broadcast=False, raw=False, reduce=None,
----------
func : function
Function to apply to each column/row
- axis : {0, 1}
- * 0 : apply function to each column
- * 1 : apply function to each row
+ axis : {0 or 'index', 1 or 'columns'}, default 0
+ * 0 or 'index': apply function to each column
+ * 1 or 'columns': apply function to each row
broadcast : boolean, default False
For aggregation functions, return object of same size with values
propagated
@@ -4162,8 +4162,8 @@ def corrwith(self, other, axis=0, drop=False):
Parameters
----------
other : DataFrame
- axis : {0, 1}
- 0 to compute column-wise, 1 for row-wise
+ axis : {0 or 'index', 1 or 'columns'}, default 0
+ 0 or 'index' to compute column-wise, 1 or 'columns' for row-wise
drop : boolean, default False
Drop missing indices from result, default returns union of all
@@ -4214,8 +4214,8 @@ def count(self, axis=0, level=None, numeric_only=False):
Parameters
----------
- axis : {0, 1}
- 0 for row-wise, 1 for column-wise
+ axis : {0 or 'index', 1 or 'columns'}, default 0
+ 0 or 'index' for row-wise, 1 or 'columns' for column-wise
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a DataFrame
@@ -4368,8 +4368,8 @@ def idxmin(self, axis=0, skipna=True):
Parameters
----------
- axis : {0, 1}
- 0 for row-wise, 1 for column-wise
+ axis : {0 or 'index', 1 or 'columns'}, default 0
+ 0 or 'index' for row-wise, 1 or 'columns' for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
@@ -4399,8 +4399,8 @@ def idxmax(self, axis=0, skipna=True):
Parameters
----------
- axis : {0, 1}
- 0 for row-wise, 1 for column-wise
+ axis : {0 or 'index', 1 or 'columns'}, default 0
+ 0 or 'index' for row-wise, 1 or 'columns' for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be first index.
@@ -4446,9 +4446,9 @@ def mode(self, axis=0, numeric_only=False):
Parameters
----------
- axis : {0, 1, 'index', 'columns'} (default 0)
- * 0/'index' : get mode of each column
- * 1/'columns' : get mode of each row
+ axis : {0 or 'index', 1 or 'columns'}, default 0
+ * 0 or 'index' : get mode of each column
+ * 1 or 'columns' : get mode of each row
numeric_only : boolean, default False
if True, only apply to numeric columns
@@ -4553,7 +4553,7 @@ def rank(self, axis=0, numeric_only=None, method='average',
Parameters
----------
- axis : {0, 1}, default 0
+ axis : {0 or 'index', 1 or 'columns'}, default 0
Ranks over columns (0) or rows (1)
numeric_only : boolean, default None
Include only float, int, boolean data
@@ -4605,7 +4605,7 @@ def to_timestamp(self, freq=None, how='start', axis=0, copy=True):
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
vs. end
- axis : {0, 1} default 0
+ axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default)
copy : boolean, default True
If false then underlying input data is not copied
@@ -4636,7 +4636,7 @@ def to_period(self, freq=None, axis=0, copy=True):
Parameters
----------
freq : string, default
- axis : {0, 1}, default 0
+ axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default)
copy : boolean, default True
If False then underlying input data is not copied
| Updated docs throughout DataFrame methods to mention
that axis can be set to 'index' or 'column' instead of 0 or 1
which improves readability significantly.
Fixes #9658
| https://api.github.com/repos/pandas-dev/pandas/pulls/10202 | 2015-05-23T13:20:21Z | 2015-05-25T14:17:18Z | 2015-05-25T14:17:18Z | 2015-06-02T19:26:12Z |
DOC: minor doc fix for Series.append; indices can overlap | diff --git a/pandas/core/series.py b/pandas/core/series.py
index 8ef9adb1d24a4..6367fb4fe0396 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1442,7 +1442,7 @@ def searchsorted(self, v, side='left', sorter=None):
def append(self, to_append, verify_integrity=False):
"""
- Concatenate two or more Series. The indexes must not overlap
+ Concatenate two or more Series.
Parameters
----------
| Old docstring seemed to be incorrect; overlapping indices are supported e.g.
``` python
>>> import pandas as pd
>>> x = pd.Series(['A', 'B', 'C'])
>>> y = pd.Series(['D', 'E', 'F'])
>>> x.append(y)
0 A
1 B
2 C
0 D
1 E
2 F
dtype: object
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10200 | 2015-05-22T20:57:41Z | 2015-05-25T11:29:35Z | 2015-05-25T11:29:35Z | 2015-06-02T19:26:12Z |
PERF: releasing the GIL, #8882 | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 2b02c4ed93a0d..9f7779c6fee8c 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -13,6 +13,7 @@ users upgrade to this version.
Highlights include:
+ - Release the Global Interpreter Lock (GIL) on some cython operations, see :ref:`here <whatsnew_0170.gil>`
Check the :ref:`API Changes <whatsnew_0170.api>` and :ref:`deprecations <whatsnew_0170.deprecations>` before updating.
@@ -56,8 +57,32 @@ Deprecations
Removal of prior version deprecations/changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. _dask: https://dask.readthedocs.org/en/latest/
+
+.. _whatsnew_0170.gil:
+
+Releasing the GIL
+~~~~~~~~~~~~~~~~~
+
+We are releasing the global-interpreter-lock (GIL) on some cython operations.
+This will allow other threads to run simultaneously during computation, potentially allowing performance improvements
+from multi-threading. Notably ``groupby`` and some indexing operations are a benefit from this. (:issue:`8882`)
+
+For example the groupby expression in the following code will have the GIL released during the factorization step, e.g. ``df.groupby('key')``
+as well as the ``.sum()`` operation.
+
+.. code-block:: python
+
+ N = 1e6
+ df = DataFrame({'key' : np.random.randint(0,ngroups,size=N),
+ 'data' : np.random.randn(N) })
+ df.groupby('key')['data'].sum()
+
+Releasing of the GIL could benefit an application that uses threads for user interactions (e.g. ``QT``), or performaning multi-threaded computations. A nice example of a library that can handle these types of computation-in-parallel is the dask_ library.
+
.. _whatsnew_0170.performance:
+
Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Added vbench benchmarks for alternative ExcelWriter engines and reading Excel files (:issue:`7171`)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index b9866a414f058..62721587e0828 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -839,7 +839,6 @@ def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan,
func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype,
axis=axis, mask_info=mask_info)
-
indexer = _ensure_int64(indexer)
func(arr, indexer, out, fill_value)
diff --git a/pandas/hashtable.pyx b/pandas/hashtable.pyx
index c4cd788216018..3b3ea9fa032f8 100644
--- a/pandas/hashtable.pyx
+++ b/pandas/hashtable.pyx
@@ -1,14 +1,19 @@
+# cython: profile=False
+
from cpython cimport PyObject, Py_INCREF, PyList_Check, PyTuple_Check
from khash cimport *
from numpy cimport *
+from cpython cimport PyMem_Malloc, PyMem_Realloc, PyMem_Free
from util cimport _checknan
cimport util
import numpy as np
+nan = np.nan
-ONAN = np.nan
+cdef extern from "numpy/npy_math.h":
+ double NAN "NPY_NAN"
cimport cython
cimport numpy as cnp
@@ -28,33 +33,14 @@ PyDateTime_IMPORT
cdef extern from "Python.h":
int PySlice_Check(object)
-
-def list_to_object_array(list obj):
- '''
- Convert list to object ndarray. Seriously can't believe I had to write this
- function
- '''
- cdef:
- Py_ssize_t i, n
- ndarray[object] arr
-
- n = len(obj)
- arr = np.empty(n, dtype=object)
-
- for i from 0 <= i < n:
- arr[i] = obj[i]
-
- return arr
-
-
cdef size_t _INIT_VEC_CAP = 32
cdef class ObjectVector:
cdef:
+ PyObject **data
size_t n, m
ndarray ao
- PyObject **data
def __cinit__(self):
self.n = 0
@@ -65,11 +51,6 @@ cdef class ObjectVector:
def __len__(self):
return self.n
- def to_array(self):
- self.ao.resize(self.n)
- self.m = self.n
- return self.ao
-
cdef inline append(self, object o):
if self.n == self.m:
self.m = max(self.m * 2, _INIT_VEC_CAP)
@@ -80,72 +61,120 @@ cdef class ObjectVector:
self.data[self.n] = <PyObject*> o
self.n += 1
+ def to_array(self):
+ self.ao.resize(self.n)
+ self.m = self.n
+ return self.ao
+
+ctypedef struct Int64VectorData:
+ int64_t *data
+ size_t n, m
+
+ctypedef struct Float64VectorData:
+ float64_t *data
+ size_t n, m
+
+ctypedef fused vector_data:
+ Int64VectorData
+ Float64VectorData
+
+ctypedef fused sixty_four_bit_scalar:
+ int64_t
+ float64_t
+
+cdef bint needs_resize(vector_data *data) nogil:
+ return data.n == data.m
+
+cdef void append_data(vector_data *data, sixty_four_bit_scalar x) nogil:
+
+ # compile time specilization of the fused types
+ # as the cross-product is generated, but we cannot assign float->int
+ # the types that don't pass are pruned
+ if (vector_data is Int64VectorData and sixty_four_bit_scalar is int64_t) or (
+ vector_data is Float64VectorData and sixty_four_bit_scalar is float64_t):
+
+ data.data[data.n] = x
+ data.n += 1
cdef class Int64Vector:
cdef:
- size_t n, m
+ Int64VectorData *data
ndarray ao
- int64_t *data
def __cinit__(self):
- self.n = 0
- self.m = _INIT_VEC_CAP
- self.ao = np.empty(_INIT_VEC_CAP, dtype=np.int64)
- self.data = <int64_t*> self.ao.data
+ self.data = <Int64VectorData *>PyMem_Malloc(sizeof(Int64VectorData))
+ if not self.data:
+ raise MemoryError()
+ self.data.n = 0
+ self.data.m = _INIT_VEC_CAP
+ self.ao = np.empty(self.data.m, dtype=np.int64)
+ self.data.data = <int64_t*> self.ao.data
+
+ cdef resize(self):
+ self.data.m = max(self.data.m * 4, _INIT_VEC_CAP)
+ self.ao.resize(self.data.m)
+ self.data.data = <int64_t*> self.ao.data
+
+ def __dealloc__(self):
+ PyMem_Free(self.data)
def __len__(self):
- return self.n
+ return self.data.n
def to_array(self):
- self.ao.resize(self.n)
- self.m = self.n
+ self.ao.resize(self.data.n)
+ self.data.m = self.data.n
return self.ao
- cdef inline append(self, int64_t x):
- if self.n == self.m:
- self.m = max(self.m * 2, _INIT_VEC_CAP)
- self.ao.resize(self.m)
- self.data = <int64_t*> self.ao.data
+ cdef inline void append(self, int64_t x):
- self.data[self.n] = x
- self.n += 1
+ if needs_resize(self.data):
+ self.resize()
+
+ append_data(self.data, x)
cdef class Float64Vector:
cdef:
- size_t n, m
+ Float64VectorData *data
ndarray ao
- float64_t *data
def __cinit__(self):
- self.n = 0
- self.m = _INIT_VEC_CAP
- self.ao = np.empty(_INIT_VEC_CAP, dtype=np.float64)
- self.data = <float64_t*> self.ao.data
+ self.data = <Float64VectorData *>PyMem_Malloc(sizeof(Float64VectorData))
+ if not self.data:
+ raise MemoryError()
+ self.data.n = 0
+ self.data.m = _INIT_VEC_CAP
+ self.ao = np.empty(self.data.m, dtype=np.float64)
+ self.data.data = <float64_t*> self.ao.data
+
+ cdef resize(self):
+ self.data.m = max(self.data.m * 4, _INIT_VEC_CAP)
+ self.ao.resize(self.data.m)
+ self.data.data = <float64_t*> self.ao.data
+
+ def __dealloc__(self):
+ PyMem_Free(self.data)
def __len__(self):
- return self.n
+ return self.data.n
def to_array(self):
- self.ao.resize(self.n)
- self.m = self.n
+ self.ao.resize(self.data.n)
+ self.data.m = self.data.n
return self.ao
- cdef inline append(self, float64_t x):
- if self.n == self.m:
- self.m = max(self.m * 2, _INIT_VEC_CAP)
- self.ao.resize(self.m)
- self.data = <float64_t*> self.ao.data
+ cdef inline void append(self, float64_t x):
- self.data[self.n] = x
- self.n += 1
+ if needs_resize(self.data):
+ self.resize()
+ append_data(self.data, x)
cdef class HashTable:
pass
-
cdef class StringHashTable(HashTable):
cdef kh_str_t *table
@@ -157,9 +186,6 @@ cdef class StringHashTable(HashTable):
def __dealloc__(self):
kh_destroy_str(self.table)
- cdef inline int check_type(self, object val):
- return util.is_string_object(val)
-
cpdef get_item(self, object val):
cdef khiter_t k
k = kh_get_str(self.table, util.get_c_string(val))
@@ -256,111 +282,16 @@ cdef class StringHashTable(HashTable):
return reverse, labels
-cdef class Int32HashTable(HashTable):
- cdef kh_int32_t *table
-
- def __init__(self, size_hint=1):
- if size_hint is not None:
- kh_resize_int32(self.table, size_hint)
-
- def __cinit__(self):
- self.table = kh_init_int32()
-
- def __dealloc__(self):
- kh_destroy_int32(self.table)
-
- cdef inline int check_type(self, object val):
- return util.is_string_object(val)
-
- cpdef get_item(self, int32_t val):
- cdef khiter_t k
- k = kh_get_int32(self.table, val)
- if k != self.table.n_buckets:
- return self.table.vals[k]
- else:
- raise KeyError(val)
-
- def get_iter_test(self, int32_t key, Py_ssize_t iterations):
- cdef Py_ssize_t i, val=0
- for i in range(iterations):
- k = kh_get_int32(self.table, val)
- if k != self.table.n_buckets:
- val = self.table.vals[k]
-
- cpdef set_item(self, int32_t key, Py_ssize_t val):
- cdef:
- khiter_t k
- int ret = 0
-
- k = kh_put_int32(self.table, key, &ret)
- self.table.keys[k] = key
- if kh_exist_int32(self.table, k):
- self.table.vals[k] = val
- else:
- raise KeyError(key)
-
- def map_locations(self, ndarray[int32_t] values):
- cdef:
- Py_ssize_t i, n = len(values)
- int ret = 0
- int32_t val
- khiter_t k
-
- for i in range(n):
- val = values[i]
- k = kh_put_int32(self.table, val, &ret)
- self.table.vals[k] = i
-
- def lookup(self, ndarray[int32_t] values):
- cdef:
- Py_ssize_t i, n = len(values)
- int32_t val
- khiter_t k
- ndarray[int32_t] locs = np.empty(n, dtype=np.int64)
-
- for i in range(n):
- val = values[i]
- k = kh_get_int32(self.table, val)
- if k != self.table.n_buckets:
- locs[i] = self.table.vals[k]
- else:
- locs[i] = -1
-
- return locs
-
- def factorize(self, ndarray[int32_t] values):
- cdef:
- Py_ssize_t i, n = len(values)
- ndarray[int64_t] labels = np.empty(n, dtype=np.int64)
- dict reverse = {}
- Py_ssize_t idx, count = 0
- int ret = 0
- int32_t val
- khiter_t k
-
- for i in range(n):
- val = values[i]
- k = kh_get_int32(self.table, val)
- if k != self.table.n_buckets:
- idx = self.table.vals[k]
- labels[i] = idx
- else:
- k = kh_put_int32(self.table, val, &ret)
- self.table.vals[k] = count
- reverse[count] = val
- labels[i] = count
- count += 1
-
- return reverse, labels
-
-cdef class Int64HashTable: #(HashTable):
- # cdef kh_int64_t *table
+cdef class Int64HashTable(HashTable):
def __cinit__(self, size_hint=1):
self.table = kh_init_int64()
if size_hint is not None:
kh_resize_int64(self.table, size_hint)
+ def __len__(self):
+ return self.table.size
+
def __dealloc__(self):
kh_destroy_int64(self.table)
@@ -369,9 +300,6 @@ cdef class Int64HashTable: #(HashTable):
k = kh_get_int64(self.table, key)
return k != self.table.n_buckets
- def __len__(self):
- return self.table.size
-
cpdef get_item(self, int64_t val):
cdef khiter_t k
k = kh_get_int64(self.table, val)
@@ -399,137 +327,166 @@ cdef class Int64HashTable: #(HashTable):
else:
raise KeyError(key)
- def map(self, ndarray[int64_t] keys, ndarray[int64_t] values):
+ @cython.boundscheck(False)
+ def map(self, int64_t[:] keys, int64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
int64_t key
khiter_t k
- for i in range(n):
- key = keys[i]
- k = kh_put_int64(self.table, key, &ret)
- self.table.vals[k] = <Py_ssize_t> values[i]
+ with nogil:
+ for i in range(n):
+ key = keys[i]
+ k = kh_put_int64(self.table, key, &ret)
+ self.table.vals[k] = <Py_ssize_t> values[i]
- def map_locations(self, ndarray[int64_t] values):
+ @cython.boundscheck(False)
+ def map_locations(self, int64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
int64_t val
khiter_t k
- for i in range(n):
- val = values[i]
- k = kh_put_int64(self.table, val, &ret)
- self.table.vals[k] = i
+ with nogil:
+ for i in range(n):
+ val = values[i]
+ k = kh_put_int64(self.table, val, &ret)
+ self.table.vals[k] = i
- def lookup(self, ndarray[int64_t] values):
+ @cython.boundscheck(False)
+ def lookup(self, int64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
int64_t val
khiter_t k
- ndarray[int64_t] locs = np.empty(n, dtype=np.int64)
+ int64_t[:] locs = np.empty(n, dtype=np.int64)
- for i in range(n):
- val = values[i]
- k = kh_get_int64(self.table, val)
- if k != self.table.n_buckets:
- locs[i] = self.table.vals[k]
- else:
- locs[i] = -1
+ with nogil:
+ for i in range(n):
+ val = values[i]
+ k = kh_get_int64(self.table, val)
+ if k != self.table.n_buckets:
+ locs[i] = self.table.vals[k]
+ else:
+ locs[i] = -1
- return locs
+ return np.asarray(locs)
def factorize(self, ndarray[object] values):
reverse = {}
labels = self.get_labels(values, reverse, 0)
return reverse, labels
- def get_labels(self, ndarray[int64_t] values, Int64Vector uniques,
+ @cython.boundscheck(False)
+ def get_labels(self, int64_t[:] values, Int64Vector uniques,
Py_ssize_t count_prior, Py_ssize_t na_sentinel):
cdef:
Py_ssize_t i, n = len(values)
- ndarray[int64_t] labels
+ int64_t[:] labels
Py_ssize_t idx, count = count_prior
int ret = 0
int64_t val
khiter_t k
+ Int64VectorData *ud
labels = np.empty(n, dtype=np.int64)
-
- for i in range(n):
- val = values[i]
- k = kh_get_int64(self.table, val)
- if k != self.table.n_buckets:
- idx = self.table.vals[k]
- labels[i] = idx
- else:
- k = kh_put_int64(self.table, val, &ret)
- self.table.vals[k] = count
- uniques.append(val)
- labels[i] = count
- count += 1
-
- return labels
-
- def get_labels_groupby(self, ndarray[int64_t] values):
+ ud = uniques.data
+
+ with nogil:
+ for i in range(n):
+ val = values[i]
+ k = kh_get_int64(self.table, val)
+ if k != self.table.n_buckets:
+ idx = self.table.vals[k]
+ labels[i] = idx
+ else:
+ k = kh_put_int64(self.table, val, &ret)
+ self.table.vals[k] = count
+
+ if needs_resize(ud):
+ with gil:
+ uniques.resize()
+ append_data(ud, val)
+ labels[i] = count
+ count += 1
+
+ return np.asarray(labels)
+
+ @cython.boundscheck(False)
+ def get_labels_groupby(self, int64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
- ndarray[int64_t] labels
+ int64_t[:] labels
Py_ssize_t idx, count = 0
int ret = 0
int64_t val
khiter_t k
Int64Vector uniques = Int64Vector()
+ Int64VectorData *ud
labels = np.empty(n, dtype=np.int64)
-
- for i in range(n):
- val = values[i]
-
- # specific for groupby
- if val < 0:
- labels[i] = -1
- continue
-
- k = kh_get_int64(self.table, val)
- if k != self.table.n_buckets:
- idx = self.table.vals[k]
- labels[i] = idx
- else:
- k = kh_put_int64(self.table, val, &ret)
- self.table.vals[k] = count
- uniques.append(val)
- labels[i] = count
- count += 1
+ ud = uniques.data
+
+ with nogil:
+ for i in range(n):
+ val = values[i]
+
+ # specific for groupby
+ if val < 0:
+ labels[i] = -1
+ continue
+
+ k = kh_get_int64(self.table, val)
+ if k != self.table.n_buckets:
+ idx = self.table.vals[k]
+ labels[i] = idx
+ else:
+ k = kh_put_int64(self.table, val, &ret)
+ self.table.vals[k] = count
+
+ if needs_resize(ud):
+ with gil:
+ uniques.resize()
+ append_data(ud, val)
+ labels[i] = count
+ count += 1
arr_uniques = uniques.to_array()
- return labels, arr_uniques
+ return np.asarray(labels), arr_uniques
- def unique(self, ndarray[int64_t] values):
+ @cython.boundscheck(False)
+ def unique(self, int64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
- ndarray result
int64_t val
khiter_t k
Int64Vector uniques = Int64Vector()
+ Int64VectorData *ud
- for i in range(n):
- val = values[i]
- k = kh_get_int64(self.table, val)
- if k == self.table.n_buckets:
- kh_put_int64(self.table, val, &ret)
- uniques.append(val)
+ ud = uniques.data
- result = uniques.to_array()
+ with nogil:
+ for i in range(n):
+ val = values[i]
+ k = kh_get_int64(self.table, val)
+ if k == self.table.n_buckets:
+ kh_put_int64(self.table, val, &ret)
+
+ if needs_resize(ud):
+ with gil:
+ uniques.resize()
+ append_data(ud, val)
- return result
+ return uniques.to_array()
cdef class Float64HashTable(HashTable):
+
def __cinit__(self, size_hint=1):
self.table = kh_init_float64()
if size_hint is not None:
@@ -566,99 +523,124 @@ cdef class Float64HashTable(HashTable):
k = kh_get_float64(self.table, key)
return k != self.table.n_buckets
- def factorize(self, ndarray[float64_t] values):
+ def factorize(self, float64_t[:] values):
uniques = Float64Vector()
labels = self.get_labels(values, uniques, 0, -1)
return uniques.to_array(), labels
- def get_labels(self, ndarray[float64_t] values,
+ @cython.boundscheck(False)
+ def get_labels(self, float64_t[:] values,
Float64Vector uniques,
Py_ssize_t count_prior, int64_t na_sentinel):
cdef:
Py_ssize_t i, n = len(values)
- ndarray[int64_t] labels
+ int64_t[:] labels
Py_ssize_t idx, count = count_prior
int ret = 0
float64_t val
khiter_t k
+ Float64VectorData *ud
labels = np.empty(n, dtype=np.int64)
+ ud = uniques.data
- for i in range(n):
- val = values[i]
-
- if val != val:
- labels[i] = na_sentinel
- continue
-
- k = kh_get_float64(self.table, val)
- if k != self.table.n_buckets:
- idx = self.table.vals[k]
- labels[i] = idx
- else:
- k = kh_put_float64(self.table, val, &ret)
- self.table.vals[k] = count
- uniques.append(val)
- labels[i] = count
- count += 1
+ with nogil:
+ for i in range(n):
+ val = values[i]
- return labels
+ if val != val:
+ labels[i] = na_sentinel
+ continue
- def map_locations(self, ndarray[float64_t] values):
+ k = kh_get_float64(self.table, val)
+ if k != self.table.n_buckets:
+ idx = self.table.vals[k]
+ labels[i] = idx
+ else:
+ k = kh_put_float64(self.table, val, &ret)
+ self.table.vals[k] = count
+
+ if needs_resize(ud):
+ with gil:
+ uniques.resize()
+ append_data(ud, val)
+ labels[i] = count
+ count += 1
+
+ return np.asarray(labels)
+
+ @cython.boundscheck(False)
+ def map_locations(self, float64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
khiter_t k
- for i in range(n):
- k = kh_put_float64(self.table, values[i], &ret)
- self.table.vals[k] = i
+ with nogil:
+ for i in range(n):
+ k = kh_put_float64(self.table, values[i], &ret)
+ self.table.vals[k] = i
- def lookup(self, ndarray[float64_t] values):
+ @cython.boundscheck(False)
+ def lookup(self, float64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
float64_t val
khiter_t k
- ndarray[int64_t] locs = np.empty(n, dtype=np.int64)
+ int64_t[:] locs = np.empty(n, dtype=np.int64)
- for i in range(n):
- val = values[i]
- k = kh_get_float64(self.table, val)
- if k != self.table.n_buckets:
- locs[i] = self.table.vals[k]
- else:
- locs[i] = -1
+ with nogil:
+ for i in range(n):
+ val = values[i]
+ k = kh_get_float64(self.table, val)
+ if k != self.table.n_buckets:
+ locs[i] = self.table.vals[k]
+ else:
+ locs[i] = -1
- return locs
+ return np.asarray(locs)
- def unique(self, ndarray[float64_t] values):
+ @cython.boundscheck(False)
+ def unique(self, float64_t[:] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
float64_t val
khiter_t k
- Float64Vector uniques = Float64Vector()
bint seen_na = 0
+ Float64Vector uniques = Float64Vector()
+ Float64VectorData *ud
- for i in range(n):
- val = values[i]
+ ud = uniques.data
- if val == val:
- k = kh_get_float64(self.table, val)
- if k == self.table.n_buckets:
- kh_put_float64(self.table, val, &ret)
- uniques.append(val)
- elif not seen_na:
- seen_na = 1
- uniques.append(ONAN)
+ with nogil:
+ for i in range(n):
+ val = values[i]
+
+ if val == val:
+ k = kh_get_float64(self.table, val)
+ if k == self.table.n_buckets:
+ kh_put_float64(self.table, val, &ret)
+
+ if needs_resize(ud):
+ with gil:
+ uniques.resize()
+ append_data(ud, val)
+
+ elif not seen_na:
+ seen_na = 1
+
+ if needs_resize(ud):
+ with gil:
+ uniques.resize()
+ append_data(ud, NAN)
return uniques.to_array()
na_sentinel = object
cdef class PyObjectHashTable(HashTable):
- # cdef kh_pymap_t *table
def __init__(self, size_hint=1):
self.table = kh_init_pymap()
@@ -740,7 +722,7 @@ cdef class PyObjectHashTable(HashTable):
int ret = 0
object val
khiter_t k
- ndarray[int64_t] locs = np.empty(n, dtype=np.int64)
+ int64_t[:] locs = np.empty(n, dtype=np.int64)
for i in range(n):
val = values[i]
@@ -754,30 +736,13 @@ cdef class PyObjectHashTable(HashTable):
else:
locs[i] = -1
- return locs
-
- def lookup2(self, ndarray[object] values):
- cdef:
- Py_ssize_t i, n = len(values)
- int ret = 0
- object val
- khiter_t k
- long hval
- ndarray[int64_t] locs = np.empty(n, dtype=np.int64)
-
- # for i in range(n):
- # val = values[i]
- # hval = PyObject_Hash(val)
- # k = kh_get_pymap(self.table, <PyObject*>val)
-
- return locs
+ return np.asarray(locs)
def unique(self, ndarray[object] values):
cdef:
Py_ssize_t i, n = len(values)
int ret = 0
object val
- ndarray result
khiter_t k
ObjectVector uniques = ObjectVector()
bint seen_na = 0
@@ -792,17 +757,15 @@ cdef class PyObjectHashTable(HashTable):
uniques.append(val)
elif not seen_na:
seen_na = 1
- uniques.append(ONAN)
-
- result = uniques.to_array()
+ uniques.append(nan)
- return result
+ return uniques.to_array()
def get_labels(self, ndarray[object] values, ObjectVector uniques,
Py_ssize_t count_prior, int64_t na_sentinel):
cdef:
Py_ssize_t i, n = len(values)
- ndarray[int64_t] labels
+ int64_t[:] labels
Py_ssize_t idx, count = count_prior
int ret = 0
object val
@@ -829,7 +792,7 @@ cdef class PyObjectHashTable(HashTable):
labels[i] = count
count += 1
- return labels
+ return np.asarray(labels)
cdef class Factorizer:
@@ -884,7 +847,7 @@ cdef class Int64Factorizer:
def get_count(self):
return self.count
- def factorize(self, ndarray[int64_t] values, sort=False,
+ def factorize(self, int64_t[:] values, sort=False,
na_sentinel=-1):
labels = self.table.get_labels(values, self.uniques,
self.count, na_sentinel)
@@ -904,28 +867,34 @@ cdef class Int64Factorizer:
return labels
-cdef build_count_table_int64(ndarray[int64_t] values, kh_int64_t *table):
+
+@cython.boundscheck(False)
+cdef build_count_table_int64(int64_t[:] values, kh_int64_t *table):
cdef:
khiter_t k
Py_ssize_t i, n = len(values)
+ int64_t val
int ret = 0
- kh_resize_int64(table, n)
+ with nogil:
+ kh_resize_int64(table, n)
- for i in range(n):
- val = values[i]
- k = kh_get_int64(table, val)
- if k != table.n_buckets:
- table.vals[k] += 1
- else:
- k = kh_put_int64(table, val, &ret)
- table.vals[k] = 1
+ for i in range(n):
+ val = values[i]
+ k = kh_get_int64(table, val)
+ if k != table.n_buckets:
+ table.vals[k] += 1
+ else:
+ k = kh_put_int64(table, val, &ret)
+ table.vals[k] = 1
-cpdef value_count_int64(ndarray[int64_t] values):
+@cython.boundscheck(False)
+cpdef value_count_int64(int64_t[:] values):
cdef:
Py_ssize_t i
kh_int64_t *table
+ int64_t[:] result_keys, result_counts
int k
table = kh_init_int64()
@@ -934,14 +903,16 @@ cpdef value_count_int64(ndarray[int64_t] values):
i = 0
result_keys = np.empty(table.n_occupied, dtype=np.int64)
result_counts = np.zeros(table.n_occupied, dtype=np.int64)
- for k in range(table.n_buckets):
- if kh_exist_int64(table, k):
- result_keys[i] = table.keys[k]
- result_counts[i] = table.vals[k]
- i += 1
+
+ with nogil:
+ for k in range(table.n_buckets):
+ if kh_exist_int64(table, k):
+ result_keys[i] = table.keys[k]
+ result_counts[i] = table.vals[k]
+ i += 1
kh_destroy_int64(table)
- return result_keys, result_counts
+ return np.asarray(result_keys), np.asarray(result_counts)
cdef build_count_table_object(ndarray[object] values,
@@ -968,7 +939,7 @@ cdef build_count_table_object(ndarray[object] values,
cpdef value_count_object(ndarray[object] values,
- ndarray[uint8_t, cast=True] mask):
+ ndarray[uint8_t, cast=True] mask):
cdef:
Py_ssize_t i
kh_pymap_t *table
@@ -995,6 +966,7 @@ def mode_object(ndarray[object] values, ndarray[uint8_t, cast=True] mask):
int count, max_count = 2
int j = -1 # so you can do +=
int k
+ ndarray[object] modes
kh_pymap_t *table
table = kh_init_pymap()
@@ -1019,36 +991,39 @@ def mode_object(ndarray[object] values, ndarray[uint8_t, cast=True] mask):
return modes[:j+1]
-def mode_int64(ndarray[int64_t] values):
+@cython.boundscheck(False)
+def mode_int64(int64_t[:] values):
cdef:
int count, max_count = 2
int j = -1 # so you can do +=
int k
kh_int64_t *table
+ ndarray[int64_t] modes
table = kh_init_int64()
build_count_table_int64(values, table)
modes = np.empty(table.n_buckets, dtype=np.int64)
- for k in range(table.n_buckets):
- if kh_exist_int64(table, k):
- count = table.vals[k]
- if count == max_count:
- j += 1
- elif count > max_count:
- max_count = count
- j = 0
- else:
- continue
- modes[j] = table.keys[k]
+ with nogil:
+ for k in range(table.n_buckets):
+ if kh_exist_int64(table, k):
+ count = table.vals[k]
+
+ if count == max_count:
+ j += 1
+ elif count > max_count:
+ max_count = count
+ j = 0
+ else:
+ continue
+ modes[j] = table.keys[k]
kh_destroy_int64(table)
return modes[:j+1]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def duplicated_int64(ndarray[int64_t, ndim=1] values, int take_last):
@@ -1060,14 +1035,15 @@ def duplicated_int64(ndarray[int64_t, ndim=1] values, int take_last):
kh_resize_int64(table, min(n, _SIZE_HINT_LIMIT))
- if take_last:
- for i from n > i >=0:
- kh_put_int64(table, values[i], &ret)
- out[i] = ret == 0
- else:
- for i from 0 <= i < n:
- kh_put_int64(table, values[i], &ret)
- out[i] = ret == 0
+ with nogil:
+ if take_last:
+ for i from n > i >=0:
+ kh_put_int64(table, values[i], &ret)
+ out[i] = ret == 0
+ else:
+ for i from 0 <= i < n:
+ kh_put_int64(table, values[i], &ret)
+ out[i] = ret == 0
kh_destroy_int64(table)
return out
@@ -1087,13 +1063,18 @@ def unique_label_indices(ndarray[int64_t, ndim=1] labels):
kh_int64_t * table = kh_init_int64()
Int64Vector idx = Int64Vector()
ndarray[int64_t, ndim=1] arr
+ Int64VectorData *ud = idx.data
kh_resize_int64(table, min(n, _SIZE_HINT_LIMIT))
- for i in range(n):
- kh_put_int64(table, labels[i], &ret)
- if ret != 0:
- idx.append(i)
+ with nogil:
+ for i in range(n):
+ kh_put_int64(table, labels[i], &ret)
+ if ret != 0:
+ if needs_resize(ud):
+ with gil:
+ idx.resize()
+ append_data(ud, i)
kh_destroy_int64(table)
diff --git a/pandas/index.pyx b/pandas/index.pyx
index 9be7e7404f3fe..1678e3b280ee5 100644
--- a/pandas/index.pyx
+++ b/pandas/index.pyx
@@ -1,3 +1,5 @@
+# cython: profile=False
+
from numpy cimport ndarray
from numpy cimport (float64_t, int32_t, int64_t, uint8_t,
@@ -89,6 +91,7 @@ cdef class IndexEngine:
self.monotonic_check = 0
self.unique = 0
+ self.unique_check = 0
self.monotonic_inc = 0
self.monotonic_dec = 0
@@ -230,16 +233,12 @@ cdef class IndexEngine:
cdef inline _do_monotonic_check(self):
try:
values = self._get_index_values()
- self.monotonic_inc, self.monotonic_dec, unique = \
+ self.monotonic_inc, self.monotonic_dec = \
self._call_monotonic(values)
-
- if unique is not None:
- self.unique = unique
- self.unique_check = 1
-
except TypeError:
self.monotonic_inc = 0
self.monotonic_dec = 0
+
self.monotonic_check = 1
cdef _get_index_values(self):
diff --git a/pandas/src/generate_code.py b/pandas/src/generate_code.py
index 5d4b18b36050f..9016f232afa9a 100644
--- a/pandas/src/generate_code.py
+++ b/pandas/src/generate_code.py
@@ -23,6 +23,9 @@
from cpython cimport PyFloat_Check
cimport cpython
+cdef extern from "numpy/npy_math.h":
+ double NAN "NPY_NAN"
+
import numpy as np
isnan = np.isnan
@@ -70,29 +73,31 @@
return arr.asobject
else:
return np.array(arr, dtype=np.object_)
-
"""
-take_1d_template = """@cython.wraparound(False)
-def take_1d_%(name)s_%(dest)s(ndarray[%(c_type_in)s] values,
- ndarray[int64_t] indexer,
- ndarray[%(c_type_out)s] out,
+take_1d_template = """
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def take_1d_%(name)s_%(dest)s(%(c_type_in)s[:] values,
+ int64_t[:] indexer,
+ %(c_type_out)s[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
%(c_type_out)s fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = %(preval)svalues[idx]%(postval)s
+ %(nogil)s
+ %(tab)sfor i from 0 <= i < n:
+ %(tab)s idx = indexer[i]
+ %(tab)s if idx == -1:
+ %(tab)s out[i] = fv
+ %(tab)s else:
+ %(tab)s out[i] = %(preval)svalues[idx]%(postval)s
"""
inner_take_2d_axis0_template = """\
@@ -134,7 +139,6 @@ def take_1d_%(name)s_%(dest)s(ndarray[%(c_type_in)s] values,
else:
for j from 0 <= j < k:
out[i, j] = %(preval)svalues[idx, j]%(postval)s
-
"""
take_2d_axis0_template = """\
@@ -241,7 +245,6 @@ def take_2d_multi_%(name)s_%(dest)s(ndarray[%(c_type_in)s, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = %(preval)svalues[idx, idx1[j]]%(postval)s
-
"""
@@ -332,7 +335,6 @@ def backfill_%(name)s(ndarray[%(c_type)s] old, ndarray[%(c_type)s] new,
cur = prev
return indexer
-
"""
@@ -396,7 +398,6 @@ def pad_%(name)s(ndarray[%(c_type)s] old, ndarray[%(c_type)s] new,
cur = next
return indexer
-
"""
pad_1d_template = """@cython.boundscheck(False)
@@ -431,7 +432,6 @@ def pad_inplace_%(name)s(ndarray[%(c_type)s] values,
else:
fill_count = 0
val = values[i]
-
"""
pad_2d_template = """@cython.boundscheck(False)
@@ -592,12 +592,11 @@ def is_monotonic_%(name)s(ndarray[%(c_type)s] arr, bint timelike):
'''
Returns
-------
- is_monotonic_inc, is_monotonic_dec, is_unique
+ is_monotonic_inc, is_monotonic_dec
'''
cdef:
Py_ssize_t i, n
%(c_type)s prev, cur
- bint is_unique = 1
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
@@ -606,33 +605,40 @@ def is_monotonic_%(name)s(ndarray[%(c_type)s] arr, bint timelike):
if n == 1:
if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
# single value is NaN
- return False, False, True
+ return False, False
else:
- return True, True, True
+ return True, True
elif n < 2:
- return True, True, True
+ return True, True
if timelike and arr[0] == iNaT:
- return False, False, None
-
- prev = arr[0]
- for i in range(1, n):
- cur = arr[i]
- if timelike and cur == iNaT:
- return False, False, None
- if cur < prev:
- is_monotonic_inc = 0
- elif cur > prev:
- is_monotonic_dec = 0
- elif cur == prev:
- is_unique = 0
- else:
- # cur or prev is NaN
- return False, False, None
- if not is_monotonic_inc and not is_monotonic_dec:
- return False, False, None
- prev = cur
- return is_monotonic_inc, is_monotonic_dec, is_unique
+ return False, False
+
+ %(nogil)s
+ %(tab)sprev = arr[0]
+ %(tab)sfor i in range(1, n):
+ %(tab)s cur = arr[i]
+ %(tab)s if timelike and cur == iNaT:
+ %(tab)s is_monotonic_inc = 0
+ %(tab)s is_monotonic_dec = 0
+ %(tab)s break
+ %(tab)s if cur < prev:
+ %(tab)s is_monotonic_inc = 0
+ %(tab)s elif cur > prev:
+ %(tab)s is_monotonic_dec = 0
+ %(tab)s elif cur == prev:
+ %(tab)s pass # is_unique = 0
+ %(tab)s else:
+ %(tab)s # cur or prev is NaN
+ %(tab)s is_monotonic_inc = 0
+ %(tab)s is_monotonic_dec = 0
+ %(tab)s break
+ %(tab)s if not is_monotonic_inc and not is_monotonic_dec:
+ %(tab)s is_monotonic_inc = 0
+ %(tab)s is_monotonic_dec = 0
+ %(tab)s break
+ %(tab)s prev = cur
+ return is_monotonic_inc, is_monotonic_dec
"""
map_indices_template = """@cython.wraparound(False)
@@ -656,7 +662,6 @@ def is_monotonic_%(name)s(ndarray[%(c_type)s] arr, bint timelike):
result[index[i]] = i
return result
-
"""
groupby_template = """@cython.wraparound(False)
@@ -686,11 +691,10 @@ def groupby_%(name)s(ndarray[%(c_type)s] index, ndarray labels):
result[key] = [idx]
return result
-
"""
group_last_template = """@cython.wraparound(False)
-@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(c_type)s, ndim=2] values,
@@ -699,7 +703,7 @@ def group_last_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, count
ndarray[%(dest_type2)s, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -712,30 +716,31 @@ def group_last_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ resx[lab, j] = val
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = resx[i, j]
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = resx[i, j]
"""
group_last_bin_template = """@cython.wraparound(False)
-@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(c_type)s, ndim=2] values,
@@ -760,30 +765,31 @@ def group_last_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
-
- # not nan
- if val == val:
- nobs[b, j] += 1
- resx[b, j] = val
-
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = resx[i, j]
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ resx[b, j] = val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = resx[i, j]
"""
-group_nth_bin_template = """@cython.boundscheck(False)
-@cython.wraparound(False)
+group_nth_bin_template = """@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(c_type)s, ndim=2] values,
@@ -808,31 +814,32 @@ def group_nth_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, j] += 1
- if nobs[b, j] == rank:
- resx[b, j] = val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if nobs[b, j] == rank:
+ resx[b, j] = val
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = resx[i, j]
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = resx[i, j]
"""
-group_nth_template = """@cython.boundscheck(False)
-@cython.wraparound(False)
+group_nth_template = """@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(c_type)s, ndim=2] values,
@@ -841,7 +848,7 @@ def group_nth_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, count
ndarray[%(dest_type2)s, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -854,31 +861,32 @@ def group_nth_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- if nobs[lab, j] == rank:
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if nobs[lab, j] == rank:
+ resx[lab, j] = val
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = resx[i, j]
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = resx[i, j]
"""
-group_add_template = """@cython.boundscheck(False)
-@cython.wraparound(False)
+group_add_template = """@cython.wraparound(False)
+@cython.boundscheck(False)
def group_add_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(c_type)s, ndim=2] values,
@@ -887,7 +895,7 @@ def group_add_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, count
ndarray[%(dest_type2)s, ndim=2] sumx, nobs
@@ -899,44 +907,50 @@ def group_add_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
- # not nan
- if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ if K > 1:
- counts[lab] += 1
- val = values[i, 0]
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j]
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+
+ else:
+
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
+
+ # not nan
+ if val == val:
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
+
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j]
"""
-group_add_bin_template = """@cython.boundscheck(False)
-@cython.wraparound(False)
+group_add_bin_template = """@cython.wraparound(False)
+@cython.boundscheck(False)
def group_add_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(dest_type2)s, ndim=2] values,
@@ -960,43 +974,46 @@ def group_add_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ngroups = len(bins) + 1
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ b = 0
+ if K > 1:
- # not nan
- if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- val = values[i, 0]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j]
+ counts[b] += 1
+ val = values[i, 0]
+
+ # not nan
+ if val == val:
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j]
"""
-group_prod_template = """@cython.boundscheck(False)
-@cython.wraparound(False)
+group_prod_template = """@cython.wraparound(False)
+@cython.boundscheck(False)
def group_prod_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(c_type)s, ndim=2] values,
@@ -1005,7 +1022,7 @@ def group_prod_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, count
ndarray[%(dest_type2)s, ndim=2] prodx, nobs
@@ -1017,44 +1034,45 @@ def group_prod_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- prodx[lab, j] *= val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ prodx[lab, j] *= val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- val = values[i, 0]
+ counts[lab] += 1
+ val = values[i, 0]
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- prodx[lab, 0] *= val
+ # not nan
+ if val == val:
+ nobs[lab, 0] += 1
+ prodx[lab, 0] *= val
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = prodx[i, j]
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = prodx[i, j]
"""
-group_prod_bin_template = """@cython.boundscheck(False)
-@cython.wraparound(False)
+group_prod_bin_template = """@cython.wraparound(False)
+@cython.boundscheck(False)
def group_prod_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(dest_type2)s, ndim=2] values,
@@ -1078,39 +1096,41 @@ def group_prod_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ngroups = len(bins) + 1
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- # not nan
- if val == val:
- nobs[b, j] += 1
- prodx[b, j] *= val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- counts[b] += 1
- val = values[i, 0]
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ prodx[b, j] *= val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- # not nan
- if val == val:
- nobs[b, 0] += 1
- prodx[b, 0] *= val
+ counts[b] += 1
+ val = values[i, 0]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = prodx[i, j]
+ # not nan
+ if val == val:
+ nobs[b, 0] += 1
+ prodx[b, 0] *= val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = prodx[i, j]
"""
group_var_template = """@cython.wraparound(False)
@@ -1120,7 +1140,7 @@ def group_var_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[%(dest_type2)s, ndim=2] values,
ndarray[int64_t] labels):
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, ct
ndarray[%(dest_type2)s, ndim=2] nobs, sumx, sumxx
@@ -1133,47 +1153,49 @@ def group_var_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
+ with nogil:
+ if K > 1:
+ for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
+ counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+ sumxx[lab, j] += val * val
+ else:
+ for i in range(N):
+
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- sumxx[lab, j] += val * val
- else:
- for i in range(N):
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
+ sumxx[lab, 0] += val * val
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- val = values[i, 0]
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
- sumxx[lab, 0] += val * val
-
-
- for i in range(len(counts)):
- for j in range(K):
- ct = nobs[i, j]
- if ct < 2:
- out[i, j] = nan
- else:
- out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
- (ct * ct - ct))
+ for i in range(ncounts):
+ for j in range(K):
+ ct = nobs[i, j]
+ if ct < 2:
+ out[i, j] = NAN
+ else:
+ out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
+ (ct * ct - ct))
"""
group_var_bin_template = """@cython.wraparound(False)
@@ -1201,44 +1223,45 @@ def group_var_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
+ counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ sumxx[b, j] += val * val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- sumxx[b, j] += val * val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
+ sumxx[b, 0] += val * val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
- sumxx[b, 0] += val * val
-
- for i in range(ngroups):
- for j in range(K):
- ct = nobs[i, j]
- if ct < 2:
- out[i, j] = nan
- else:
- out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
- (ct * ct - ct))
+ for i in range(ngroups):
+ for j in range(K):
+ ct = nobs[i, j]
+ if ct < 2:
+ out[i, j] = NAN
+ else:
+ out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
+ (ct * ct - ct))
"""
group_count_template = """@cython.boundscheck(False)
@@ -1251,36 +1274,36 @@ def group_count_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, lab
+ Py_ssize_t i, j, lab, ncounts = len(counts)
Py_ssize_t N = values.shape[0], K = values.shape[1]
%(c_type)s val
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)
if len(values) != len(labels):
- raise AssertionError("len(index) != len(labels)")
-
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ raise AssertionError("len(index) != len(labels)")
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
- # not nan
- nobs[lab, j] += val == val and val != iNaT
+ %(nogil)s
+ %(tab)sfor i in range(N):
+ %(tab)s lab = labels[i]
+ %(tab)s if lab < 0:
+ %(tab)s continue
- for i in range(len(counts)):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ %(tab)s counts[lab] += 1
+ %(tab)s for j in range(K):
+ %(tab)s val = values[i, j]
+ %(tab)s # not nan
+ %(tab)s nobs[lab, j] += val == val and val != iNaT
+ %(tab)sfor i in range(ncounts):
+ %(tab)s for j in range(K):
+ %(tab)s out[i, j] = nobs[i, j]
"""
-group_count_bin_template = """@cython.boundscheck(False)
-@cython.wraparound(False)
+group_count_bin_template = """@cython.wraparound(False)
+@cython.boundscheck(False)
def group_count_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(c_type)s, ndim=2] values,
@@ -1299,23 +1322,23 @@ def group_count_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ %(nogil)s
+ %(tab)sfor i in range(N):
+ %(tab)s while b < ngroups - 1 and i >= bins[b]:
+ %(tab)s b += 1
- # not nan
- nobs[b, j] += val == val and val != iNaT
-
- for i in range(ngroups):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ %(tab)s counts[b] += 1
+ %(tab)s for j in range(K):
+ %(tab)s val = values[i, j]
+ %(tab)s # not nan
+ %(tab)s nobs[b, j] += val == val and val != iNaT
+ %(tab)sfor i in range(ngroups):
+ %(tab)s for j in range(K):
+ %(tab)s out[i, j] = nobs[i, j]
"""
+
# add passing bin edges, instead of labels
@@ -1350,41 +1373,42 @@ def group_min_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val < minx[b, j]:
+ minx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val < minx[b, j]:
- minx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ if val < minx[b, 0]:
+ minx[b, 0] = val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val < minx[b, 0]:
- minx[b, 0] = val
-
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = minx[i, j]
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = minx[i, j]
"""
group_max_template = """@cython.wraparound(False)
@@ -1397,7 +1421,7 @@ def group_max_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, count
ndarray[%(dest_type2)s, ndim=2] maxx, nobs
@@ -1411,42 +1435,43 @@ def group_max_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val > maxx[lab, j]:
+ maxx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- if val > maxx[lab, j]:
- maxx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ if val > maxx[lab, 0]:
+ maxx[lab, 0] = val
- counts[lab] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val > maxx[lab, 0]:
- maxx[lab, 0] = val
-
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = maxx[i, j]
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = maxx[i, j]
"""
group_max_bin_template = """@cython.wraparound(False)
@@ -1476,41 +1501,42 @@ def group_max_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val > maxx[b, j]:
+ maxx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val > maxx[b, j]:
- maxx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ if val > maxx[b, 0]:
+ maxx[b, 0] = val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val > maxx[b, 0]:
- maxx[b, 0] = val
-
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = maxx[i, j]
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = maxx[i, j]
"""
@@ -1524,7 +1550,7 @@ def group_min_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, count
ndarray[%(dest_type2)s, ndim=2] minx, nobs
@@ -1538,42 +1564,43 @@ def group_min_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val < minx[lab, j]:
+ minx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- if val < minx[lab, j]:
- minx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ if val < minx[lab, 0]:
+ minx[lab, 0] = val
- counts[lab] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val < minx[lab, 0]:
- minx[lab, 0] = val
-
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = %(nan_val)s
- else:
- out[i, j] = minx[i, j]
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = %(nan_val)s
+ else:
+ out[i, j] = minx[i, j]
"""
@@ -1584,7 +1611,7 @@ def group_mean_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[%(dest_type2)s, ndim=2] values,
ndarray[int64_t] labels):
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
%(dest_type2)s val, count
ndarray[%(dest_type2)s, ndim=2] sumx, nobs
@@ -1596,42 +1623,44 @@ def group_mean_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
- counts[lab] += 1
- val = values[i, 0]
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
-
- for i in range(len(counts)):
- for j in range(K):
- count = nobs[i, j]
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j] / count
+ for i in range(ncounts):
+ for j in range(K):
+ count = nobs[i, j]
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j] / count
"""
group_mean_bin_template = """
+@cython.boundscheck(False)
def group_mean_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
ndarray[int64_t] counts,
ndarray[%(dest_type2)s, ndim=2] values,
@@ -1652,40 +1681,41 @@ def group_mean_bin_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
else:
ngroups = len(bins) + 1
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
-
- for i in range(ngroups):
- for j in range(K):
- count = nobs[i, j]
- if count == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j] / count
+ for i in range(ngroups):
+ for j in range(K):
+ count = nobs[i, j]
+ if count == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j] / count
"""
group_ohlc_template = """@cython.wraparound(False)
@@ -1700,7 +1730,7 @@ def group_ohlc_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
cdef:
Py_ssize_t i, j, N, K, ngroups, b
%(dest_type2)s val, count
- %(dest_type2)s vopen, vhigh, vlow, vclose, NA
+ %(dest_type2)s vopen, vhigh, vlow, vclose
bint got_first = 0
if len(bins) == 0:
@@ -1715,55 +1745,55 @@ def group_ohlc_%(name)s(ndarray[%(dest_type2)s, ndim=2] out,
if out.shape[1] != 4:
raise ValueError('Output array must have 4 columns')
- NA = np.nan
-
b = 0
if K > 1:
raise NotImplementedError("Argument 'values' must have only "
"one dimension")
else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- if not got_first:
- out[b, 0] = NA
- out[b, 1] = NA
- out[b, 2] = NA
- out[b, 3] = NA
- else:
- out[b, 0] = vopen
- out[b, 1] = vhigh
- out[b, 2] = vlow
- out[b, 3] = vclose
- b += 1
- got_first = 0
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- if not got_first:
- got_first = 1
- vopen = val
- vlow = val
- vhigh = val
- else:
- if val < vlow:
+ with nogil:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ if not got_first:
+ out[b, 0] = NAN
+ out[b, 1] = NAN
+ out[b, 2] = NAN
+ out[b, 3] = NAN
+ else:
+ out[b, 0] = vopen
+ out[b, 1] = vhigh
+ out[b, 2] = vlow
+ out[b, 3] = vclose
+ b += 1
+ got_first = 0
+
+ counts[b] += 1
+ val = values[i, 0]
+
+ # not nan
+ if val == val:
+ if not got_first:
+ got_first = 1
+ vopen = val
vlow = val
- if val > vhigh:
vhigh = val
- vclose = val
-
- if not got_first:
- out[b, 0] = NA
- out[b, 1] = NA
- out[b, 2] = NA
- out[b, 3] = NA
- else:
- out[b, 0] = vopen
- out[b, 1] = vhigh
- out[b, 2] = vlow
- out[b, 3] = vclose
+ else:
+ if val < vlow:
+ vlow = val
+ if val > vhigh:
+ vhigh = val
+ vclose = val
+
+ if not got_first:
+ out[b, 0] = NAN
+ out[b, 1] = NAN
+ out[b, 2] = NAN
+ out[b, 3] = NAN
+ else:
+ out[b, 0] = vopen
+ out[b, 1] = vhigh
+ out[b, 2] = vlow
+ out[b, 3] = vclose
"""
arrmap_template = """@cython.wraparound(False)
@@ -1780,7 +1810,6 @@ def arrmap_%(name)s(ndarray[%(c_type)s] index, object func):
result[i] = func(index[i])
return maybe_convert_objects(result)
-
"""
#----------------------------------------------------------------------
@@ -1832,7 +1861,6 @@ def left_join_indexer_unique_%(name)s(ndarray[%(c_type)s] left,
indexer[i] = -1
i += 1
return indexer
-
"""
# @cython.wraparound(False)
@@ -1939,7 +1967,6 @@ def left_join_indexer_%(name)s(ndarray[%(c_type)s] left,
j += 1
return result, lindexer, rindexer
-
"""
@@ -2035,7 +2062,6 @@ def inner_join_indexer_%(name)s(ndarray[%(c_type)s] left,
j += 1
return result, lindexer, rindexer
-
"""
@@ -2167,7 +2193,6 @@ def outer_join_indexer_%(name)s(ndarray[%(c_type)s] left,
j += 1
return result, lindexer, rindexer
-
"""
outer_join_template = """@cython.wraparound(False)
@@ -2265,7 +2290,6 @@ def outer_join_indexer_%(name)s(ndarray[%(c_type)s] left,
count += 1
return result, lindexer, rindexer
-
"""
# ensure_dtype functions
@@ -2279,7 +2303,6 @@ def outer_join_indexer_%(name)s(ndarray[%(c_type)s] left,
return arr.astype(np.%(dtype)s)
else:
return np.array(arr, dtype=np.%(dtype)s)
-
"""
ensure_functions = [
@@ -2323,19 +2346,19 @@ def put2d_%(name)s_%(dest_type)s(ndarray[%(c_type)s, ndim=2, cast=True] values,
def generate_put_template(template, use_ints=True, use_floats=True,
use_objects=False, use_datelikes=False):
floats_list = [
- ('float64', 'float64_t', 'float64_t', 'np.float64'),
- ('float32', 'float32_t', 'float32_t', 'np.float32'),
+ ('float64', 'float64_t', 'float64_t', 'np.float64', True),
+ ('float32', 'float32_t', 'float32_t', 'np.float32', True),
]
ints_list = [
- ('int8', 'int8_t', 'float32_t', 'np.float32'),
- ('int16', 'int16_t', 'float32_t', 'np.float32'),
- ('int32', 'int32_t', 'float64_t', 'np.float64'),
- ('int64', 'int64_t', 'float64_t', 'np.float64'),
+ ('int8', 'int8_t', 'float32_t', 'np.float32', True),
+ ('int16', 'int16_t', 'float32_t', 'np.float32', True),
+ ('int32', 'int32_t', 'float64_t', 'np.float64', True),
+ ('int64', 'int64_t', 'float64_t', 'np.float64', True),
]
date_like_list = [
- ('int64', 'int64_t', 'float64_t', 'np.float64'),
+ ('int64', 'int64_t', 'float64_t', 'np.float64', True),
]
- object_list = [('object', 'object', 'object', 'np.object_')]
+ object_list = [('object', 'object', 'object', 'np.object_', False)]
function_list = []
if use_floats:
function_list.extend(floats_list)
@@ -2347,28 +2370,31 @@ def generate_put_template(template, use_ints=True, use_floats=True,
function_list.extend(date_like_list)
output = StringIO()
- for name, c_type, dest_type, dest_dtype in function_list:
+ for name, c_type, dest_type, dest_dtype, nogil in function_list:
func = template % {'name': name,
'c_type': c_type,
'dest_type': dest_type.replace('_t', ''),
'dest_type2': dest_type,
- 'dest_dtype': dest_dtype}
+ 'dest_dtype': dest_dtype,
+ 'nogil' : 'with nogil:' if nogil else '',
+ 'tab' : ' ' if nogil else '' }
output.write(func)
+ output.write("\n")
return output.getvalue()
def generate_put_min_max_template(template, use_ints=True, use_floats=True,
use_objects=False, use_datelikes=False):
floats_list = [
- ('float64', 'float64_t', 'nan', 'np.inf'),
- ('float32', 'float32_t', 'nan', 'np.inf'),
+ ('float64', 'float64_t', 'NAN', 'np.inf', True),
+ ('float32', 'float32_t', 'NAN', 'np.inf', True),
]
ints_list = [
- ('int64', 'int64_t', 'iNaT', _int64_max),
+ ('int64', 'int64_t', 'iNaT', _int64_max, True),
]
date_like_list = [
- ('int64', 'int64_t', 'iNaT', _int64_max),
+ ('int64', 'int64_t', 'iNaT', _int64_max, True),
]
- object_list = [('object', 'object', 'nan', 'np.inf')]
+ object_list = [('object', 'object', 'np.nan', 'np.inf', False)]
function_list = []
if use_floats:
function_list.extend(floats_list)
@@ -2380,27 +2406,30 @@ def generate_put_min_max_template(template, use_ints=True, use_floats=True,
function_list.extend(date_like_list)
output = StringIO()
- for name, dest_type, nan_val, inf_val in function_list:
+ for name, dest_type, nan_val, inf_val, nogil in function_list:
func = template % {'name': name,
'dest_type2': dest_type,
'nan_val': nan_val,
- 'inf_val': inf_val}
+ 'inf_val': inf_val,
+ 'nogil' : "with nogil:" if nogil else '',
+ 'tab' : ' ' if nogil else '' }
output.write(func)
+ output.write("\n")
return output.getvalue()
def generate_put_selection_template(template, use_ints=True, use_floats=True,
use_objects=False, use_datelikes=False):
floats_list = [
- ('float64', 'float64_t', 'float64_t', 'nan'),
- ('float32', 'float32_t', 'float32_t', 'nan'),
+ ('float64', 'float64_t', 'float64_t', 'NAN', True),
+ ('float32', 'float32_t', 'float32_t', 'NAN', True),
]
ints_list = [
- ('int64', 'int64_t', 'int64_t', 'iNaT'),
+ ('int64', 'int64_t', 'int64_t', 'iNaT', True),
]
date_like_list = [
- ('int64', 'int64_t', 'int64_t', 'iNaT'),
+ ('int64', 'int64_t', 'int64_t', 'iNaT', True),
]
- object_list = [('object', 'object', 'object', 'nan')]
+ object_list = [('object', 'object', 'object', 'np.nan', False)]
function_list = []
if use_floats:
function_list.extend(floats_list)
@@ -2412,72 +2441,97 @@ def generate_put_selection_template(template, use_ints=True, use_floats=True,
function_list.extend(date_like_list)
output = StringIO()
- for name, c_type, dest_type, nan_val in function_list:
+ for name, c_type, dest_type, nan_val, nogil in function_list:
+
+ if nogil:
+ nogil = "with nogil:"
+ tab = ' '
+ else:
+ nogil = ''
+ tab = ''
+
func = template % {'name': name,
'c_type': c_type,
'dest_type2': dest_type,
- 'nan_val': nan_val}
+ 'nan_val': nan_val,
+ 'nogil' : nogil,
+ 'tab' : tab }
output.write(func)
+ output.write("\n")
return output.getvalue()
def generate_take_template(template, exclude=None):
- # name, dest, ctypein, ctypeout, preval, postval, cancopy
+ # name, dest, ctypein, ctypeout, preval, postval, cancopy, nogil
function_list = [
- ('bool', 'bool', 'uint8_t', 'uint8_t', '', '', True),
+ ('bool', 'bool', 'uint8_t', 'uint8_t', '', '', True, True),
('bool', 'object', 'uint8_t', 'object',
- 'True if ', ' > 0 else False', False),
- ('int8', 'int8', 'int8_t', 'int8_t', '', '', True),
- ('int8', 'int32', 'int8_t', 'int32_t', '', '', False),
- ('int8', 'int64', 'int8_t', 'int64_t', '', '', False),
- ('int8', 'float64', 'int8_t', 'float64_t', '', '', False),
- ('int16', 'int16', 'int16_t', 'int16_t', '', '', True),
- ('int16', 'int32', 'int16_t', 'int32_t', '', '', False),
- ('int16', 'int64', 'int16_t', 'int64_t', '', '', False),
- ('int16', 'float64', 'int16_t', 'float64_t', '', '', False),
- ('int32', 'int32', 'int32_t', 'int32_t', '', '', True),
- ('int32', 'int64', 'int32_t', 'int64_t', '', '', False),
- ('int32', 'float64', 'int32_t', 'float64_t', '', '', False),
- ('int64', 'int64', 'int64_t', 'int64_t', '', '', True),
- ('int64', 'float64', 'int64_t', 'float64_t', '', '', False),
- ('float32', 'float32', 'float32_t', 'float32_t', '', '', True),
- ('float32', 'float64', 'float32_t', 'float64_t', '', '', False),
- ('float64', 'float64', 'float64_t', 'float64_t', '', '', True),
- ('object', 'object', 'object', 'object', '', '', False)
+ 'True if ', ' > 0 else False', False, False),
+ ('int8', 'int8', 'int8_t', 'int8_t', '', '', True, False),
+ ('int8', 'int32', 'int8_t', 'int32_t', '', '', False, True),
+ ('int8', 'int64', 'int8_t', 'int64_t', '', '', False, True),
+ ('int8', 'float64', 'int8_t', 'float64_t', '', '', False, True),
+ ('int16', 'int16', 'int16_t', 'int16_t', '', '', True, True),
+ ('int16', 'int32', 'int16_t', 'int32_t', '', '', False, True),
+ ('int16', 'int64', 'int16_t', 'int64_t', '', '', False, True),
+ ('int16', 'float64', 'int16_t', 'float64_t', '', '', False, True),
+ ('int32', 'int32', 'int32_t', 'int32_t', '', '', True, True),
+ ('int32', 'int64', 'int32_t', 'int64_t', '', '', False, True),
+ ('int32', 'float64', 'int32_t', 'float64_t', '', '', False, True),
+ ('int64', 'int64', 'int64_t', 'int64_t', '', '', True, True),
+ ('int64', 'float64', 'int64_t', 'float64_t', '', '', False, True),
+ ('float32', 'float32', 'float32_t', 'float32_t', '', '', True, True),
+ ('float32', 'float64', 'float32_t', 'float64_t', '', '', False, True),
+ ('float64', 'float64', 'float64_t', 'float64_t', '', '', True, True),
+ ('object', 'object', 'object', 'object', '', '', False, False),
]
output = StringIO()
for (name, dest, c_type_in, c_type_out,
- preval, postval, can_copy) in function_list:
+ preval, postval, can_copy, nogil) in function_list:
+
if exclude is not None and name in exclude:
continue
+ if nogil:
+ nogil = "with nogil:"
+ tab = ' '
+ else:
+ nogil = ''
+ tab = ''
+
func = template % {'name': name, 'dest': dest,
'c_type_in': c_type_in, 'c_type_out': c_type_out,
'preval': preval, 'postval': postval,
- 'can_copy': 'True' if can_copy else 'False'}
+ 'can_copy': 'True' if can_copy else 'False',
+ 'nogil' : nogil,
+ 'tab' : tab }
output.write(func)
+ output.write("\n")
return output.getvalue()
def generate_from_template(template, exclude=None):
# name, ctype, capable of holding NA
function_list = [
- ('float64', 'float64_t', 'np.float64', True),
- ('float32', 'float32_t', 'np.float32', True),
- ('object', 'object', 'object', True),
- ('int32', 'int32_t', 'np.int32', False),
- ('int64', 'int64_t', 'np.int64', False),
- ('bool', 'uint8_t', 'np.bool', False)
+ ('float64', 'float64_t', 'np.float64', True, True),
+ ('float32', 'float32_t', 'np.float32', True, True),
+ ('object', 'object', 'object', True, False),
+ ('int32', 'int32_t', 'np.int32', False, True),
+ ('int64', 'int64_t', 'np.int64', False, True),
+ ('bool', 'uint8_t', 'np.bool', False, True)
]
output = StringIO()
- for name, c_type, dtype, can_hold_na in function_list:
+ for name, c_type, dtype, can_hold_na, nogil in function_list:
if exclude is not None and name in exclude:
continue
func = template % {'name': name, 'c_type': c_type,
'dtype': dtype,
- 'raise_on_na': 'False' if can_hold_na else 'True'}
+ 'raise_on_na': 'False' if can_hold_na else 'True',
+ 'nogil' : 'with nogil:' if nogil else '',
+ 'tab' : ' ' if nogil else '' }
output.write(func)
+ output.write("\n")
return output.getvalue()
put_2d = [diff_2d_template]
diff --git a/pandas/src/generated.pyx b/pandas/src/generated.pyx
index 83dfacba45211..11334516ea555 100644
--- a/pandas/src/generated.pyx
+++ b/pandas/src/generated.pyx
@@ -14,6 +14,9 @@ from cpython cimport (PyDict_New, PyDict_GetItem, PyDict_SetItem,
from cpython cimport PyFloat_Check
cimport cpython
+cdef extern from "numpy/npy_math.h":
+ double NAN "NPY_NAN"
+
import numpy as np
isnan = np.isnan
@@ -63,7 +66,6 @@ cpdef ensure_object(object arr):
return np.array(arr, dtype=np.object_)
-
cpdef ensure_float64(object arr):
if util.is_array(arr):
if (<ndarray> arr).descr.type_num == NPY_FLOAT64:
@@ -73,7 +75,6 @@ cpdef ensure_float64(object arr):
else:
return np.array(arr, dtype=np.float64)
-
cpdef ensure_float32(object arr):
if util.is_array(arr):
if (<ndarray> arr).descr.type_num == NPY_FLOAT32:
@@ -83,7 +84,6 @@ cpdef ensure_float32(object arr):
else:
return np.array(arr, dtype=np.float32)
-
cpdef ensure_int8(object arr):
if util.is_array(arr):
if (<ndarray> arr).descr.type_num == NPY_INT8:
@@ -93,7 +93,6 @@ cpdef ensure_int8(object arr):
else:
return np.array(arr, dtype=np.int8)
-
cpdef ensure_int16(object arr):
if util.is_array(arr):
if (<ndarray> arr).descr.type_num == NPY_INT16:
@@ -103,7 +102,6 @@ cpdef ensure_int16(object arr):
else:
return np.array(arr, dtype=np.int16)
-
cpdef ensure_int32(object arr):
if util.is_array(arr):
if (<ndarray> arr).descr.type_num == NPY_INT32:
@@ -113,7 +111,6 @@ cpdef ensure_int32(object arr):
else:
return np.array(arr, dtype=np.int32)
-
cpdef ensure_int64(object arr):
if util.is_array(arr):
if (<ndarray> arr).descr.type_num == NPY_INT64:
@@ -123,7 +120,6 @@ cpdef ensure_int64(object arr):
else:
return np.array(arr, dtype=np.int64)
-
@cython.wraparound(False)
@cython.boundscheck(False)
cpdef map_indices_float64(ndarray[float64_t] index):
@@ -1228,6 +1224,7 @@ def backfill_inplace_float64(ndarray[float64_t] values,
else:
fill_count = 0
val = values[i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_inplace_float32(ndarray[float32_t] values,
@@ -1260,6 +1257,7 @@ def backfill_inplace_float32(ndarray[float32_t] values,
else:
fill_count = 0
val = values[i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_inplace_object(ndarray[object] values,
@@ -1292,6 +1290,7 @@ def backfill_inplace_object(ndarray[object] values,
else:
fill_count = 0
val = values[i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_inplace_int32(ndarray[int32_t] values,
@@ -1324,6 +1323,7 @@ def backfill_inplace_int32(ndarray[int32_t] values,
else:
fill_count = 0
val = values[i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_inplace_int64(ndarray[int64_t] values,
@@ -1356,6 +1356,7 @@ def backfill_inplace_int64(ndarray[int64_t] values,
else:
fill_count = 0
val = values[i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_inplace_bool(ndarray[uint8_t] values,
@@ -1389,6 +1390,7 @@ def backfill_inplace_bool(ndarray[uint8_t] values,
fill_count = 0
val = values[i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_2d_inplace_float64(ndarray[float64_t, ndim=2] values,
@@ -1423,6 +1425,7 @@ def pad_2d_inplace_float64(ndarray[float64_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_2d_inplace_float32(ndarray[float32_t, ndim=2] values,
@@ -1457,6 +1460,7 @@ def pad_2d_inplace_float32(ndarray[float32_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_2d_inplace_object(ndarray[object, ndim=2] values,
@@ -1491,6 +1495,7 @@ def pad_2d_inplace_object(ndarray[object, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_2d_inplace_int32(ndarray[int32_t, ndim=2] values,
@@ -1525,6 +1530,7 @@ def pad_2d_inplace_int32(ndarray[int32_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_2d_inplace_int64(ndarray[int64_t, ndim=2] values,
@@ -1559,6 +1565,7 @@ def pad_2d_inplace_int64(ndarray[int64_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_2d_inplace_bool(ndarray[uint8_t, ndim=2] values,
@@ -1594,6 +1601,7 @@ def pad_2d_inplace_bool(ndarray[uint8_t, ndim=2] values,
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_2d_inplace_float64(ndarray[float64_t, ndim=2] values,
@@ -1628,6 +1636,7 @@ def backfill_2d_inplace_float64(ndarray[float64_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_2d_inplace_float32(ndarray[float32_t, ndim=2] values,
@@ -1662,6 +1671,7 @@ def backfill_2d_inplace_float32(ndarray[float32_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_2d_inplace_object(ndarray[object, ndim=2] values,
@@ -1696,6 +1706,7 @@ def backfill_2d_inplace_object(ndarray[object, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_2d_inplace_int32(ndarray[int32_t, ndim=2] values,
@@ -1730,6 +1741,7 @@ def backfill_2d_inplace_int32(ndarray[int32_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_2d_inplace_int64(ndarray[int64_t, ndim=2] values,
@@ -1764,6 +1776,7 @@ def backfill_2d_inplace_int64(ndarray[int64_t, ndim=2] values,
else:
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill_2d_inplace_bool(ndarray[uint8_t, ndim=2] values,
@@ -1799,18 +1812,18 @@ def backfill_2d_inplace_bool(ndarray[uint8_t, ndim=2] values,
fill_count = 0
val = values[j, i]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def is_monotonic_float64(ndarray[float64_t] arr, bint timelike):
'''
Returns
-------
- is_monotonic_inc, is_monotonic_dec, is_unique
+ is_monotonic_inc, is_monotonic_dec
'''
cdef:
Py_ssize_t i, n
float64_t prev, cur
- bint is_unique = 1
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
@@ -1819,45 +1832,52 @@ def is_monotonic_float64(ndarray[float64_t] arr, bint timelike):
if n == 1:
if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
# single value is NaN
- return False, False, True
+ return False, False
else:
- return True, True, True
+ return True, True
elif n < 2:
- return True, True, True
+ return True, True
if timelike and arr[0] == iNaT:
- return False, False, None
+ return False, False
+
+ with nogil:
+ prev = arr[0]
+ for i in range(1, n):
+ cur = arr[i]
+ if timelike and cur == iNaT:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if cur < prev:
+ is_monotonic_inc = 0
+ elif cur > prev:
+ is_monotonic_dec = 0
+ elif cur == prev:
+ pass # is_unique = 0
+ else:
+ # cur or prev is NaN
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if not is_monotonic_inc and not is_monotonic_dec:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ prev = cur
+ return is_monotonic_inc, is_monotonic_dec
- prev = arr[0]
- for i in range(1, n):
- cur = arr[i]
- if timelike and cur == iNaT:
- return False, False, None
- if cur < prev:
- is_monotonic_inc = 0
- elif cur > prev:
- is_monotonic_dec = 0
- elif cur == prev:
- is_unique = 0
- else:
- # cur or prev is NaN
- return False, False, None
- if not is_monotonic_inc and not is_monotonic_dec:
- return False, False, None
- prev = cur
- return is_monotonic_inc, is_monotonic_dec, is_unique
@cython.boundscheck(False)
@cython.wraparound(False)
def is_monotonic_float32(ndarray[float32_t] arr, bint timelike):
'''
Returns
-------
- is_monotonic_inc, is_monotonic_dec, is_unique
+ is_monotonic_inc, is_monotonic_dec
'''
cdef:
Py_ssize_t i, n
float32_t prev, cur
- bint is_unique = 1
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
@@ -1866,45 +1886,52 @@ def is_monotonic_float32(ndarray[float32_t] arr, bint timelike):
if n == 1:
if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
# single value is NaN
- return False, False, True
+ return False, False
else:
- return True, True, True
+ return True, True
elif n < 2:
- return True, True, True
+ return True, True
if timelike and arr[0] == iNaT:
- return False, False, None
+ return False, False
+
+ with nogil:
+ prev = arr[0]
+ for i in range(1, n):
+ cur = arr[i]
+ if timelike and cur == iNaT:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if cur < prev:
+ is_monotonic_inc = 0
+ elif cur > prev:
+ is_monotonic_dec = 0
+ elif cur == prev:
+ pass # is_unique = 0
+ else:
+ # cur or prev is NaN
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if not is_monotonic_inc and not is_monotonic_dec:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ prev = cur
+ return is_monotonic_inc, is_monotonic_dec
- prev = arr[0]
- for i in range(1, n):
- cur = arr[i]
- if timelike and cur == iNaT:
- return False, False, None
- if cur < prev:
- is_monotonic_inc = 0
- elif cur > prev:
- is_monotonic_dec = 0
- elif cur == prev:
- is_unique = 0
- else:
- # cur or prev is NaN
- return False, False, None
- if not is_monotonic_inc and not is_monotonic_dec:
- return False, False, None
- prev = cur
- return is_monotonic_inc, is_monotonic_dec, is_unique
@cython.boundscheck(False)
@cython.wraparound(False)
def is_monotonic_object(ndarray[object] arr, bint timelike):
'''
Returns
-------
- is_monotonic_inc, is_monotonic_dec, is_unique
+ is_monotonic_inc, is_monotonic_dec
'''
cdef:
Py_ssize_t i, n
object prev, cur
- bint is_unique = 1
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
@@ -1913,45 +1940,52 @@ def is_monotonic_object(ndarray[object] arr, bint timelike):
if n == 1:
if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
# single value is NaN
- return False, False, True
+ return False, False
else:
- return True, True, True
+ return True, True
elif n < 2:
- return True, True, True
+ return True, True
if timelike and arr[0] == iNaT:
- return False, False, None
+ return False, False
+
prev = arr[0]
for i in range(1, n):
cur = arr[i]
if timelike and cur == iNaT:
- return False, False, None
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
if cur < prev:
is_monotonic_inc = 0
elif cur > prev:
is_monotonic_dec = 0
elif cur == prev:
- is_unique = 0
+ pass # is_unique = 0
else:
# cur or prev is NaN
- return False, False, None
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
if not is_monotonic_inc and not is_monotonic_dec:
- return False, False, None
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
prev = cur
- return is_monotonic_inc, is_monotonic_dec, is_unique
+ return is_monotonic_inc, is_monotonic_dec
+
@cython.boundscheck(False)
@cython.wraparound(False)
def is_monotonic_int32(ndarray[int32_t] arr, bint timelike):
'''
Returns
-------
- is_monotonic_inc, is_monotonic_dec, is_unique
+ is_monotonic_inc, is_monotonic_dec
'''
cdef:
Py_ssize_t i, n
int32_t prev, cur
- bint is_unique = 1
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
@@ -1960,45 +1994,52 @@ def is_monotonic_int32(ndarray[int32_t] arr, bint timelike):
if n == 1:
if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
# single value is NaN
- return False, False, True
+ return False, False
else:
- return True, True, True
+ return True, True
elif n < 2:
- return True, True, True
+ return True, True
if timelike and arr[0] == iNaT:
- return False, False, None
+ return False, False
+
+ with nogil:
+ prev = arr[0]
+ for i in range(1, n):
+ cur = arr[i]
+ if timelike and cur == iNaT:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if cur < prev:
+ is_monotonic_inc = 0
+ elif cur > prev:
+ is_monotonic_dec = 0
+ elif cur == prev:
+ pass # is_unique = 0
+ else:
+ # cur or prev is NaN
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if not is_monotonic_inc and not is_monotonic_dec:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ prev = cur
+ return is_monotonic_inc, is_monotonic_dec
- prev = arr[0]
- for i in range(1, n):
- cur = arr[i]
- if timelike and cur == iNaT:
- return False, False, None
- if cur < prev:
- is_monotonic_inc = 0
- elif cur > prev:
- is_monotonic_dec = 0
- elif cur == prev:
- is_unique = 0
- else:
- # cur or prev is NaN
- return False, False, None
- if not is_monotonic_inc and not is_monotonic_dec:
- return False, False, None
- prev = cur
- return is_monotonic_inc, is_monotonic_dec, is_unique
@cython.boundscheck(False)
@cython.wraparound(False)
def is_monotonic_int64(ndarray[int64_t] arr, bint timelike):
'''
Returns
-------
- is_monotonic_inc, is_monotonic_dec, is_unique
+ is_monotonic_inc, is_monotonic_dec
'''
cdef:
Py_ssize_t i, n
int64_t prev, cur
- bint is_unique = 1
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
@@ -2007,45 +2048,52 @@ def is_monotonic_int64(ndarray[int64_t] arr, bint timelike):
if n == 1:
if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
# single value is NaN
- return False, False, True
+ return False, False
else:
- return True, True, True
+ return True, True
elif n < 2:
- return True, True, True
+ return True, True
if timelike and arr[0] == iNaT:
- return False, False, None
+ return False, False
+
+ with nogil:
+ prev = arr[0]
+ for i in range(1, n):
+ cur = arr[i]
+ if timelike and cur == iNaT:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if cur < prev:
+ is_monotonic_inc = 0
+ elif cur > prev:
+ is_monotonic_dec = 0
+ elif cur == prev:
+ pass # is_unique = 0
+ else:
+ # cur or prev is NaN
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if not is_monotonic_inc and not is_monotonic_dec:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ prev = cur
+ return is_monotonic_inc, is_monotonic_dec
- prev = arr[0]
- for i in range(1, n):
- cur = arr[i]
- if timelike and cur == iNaT:
- return False, False, None
- if cur < prev:
- is_monotonic_inc = 0
- elif cur > prev:
- is_monotonic_dec = 0
- elif cur == prev:
- is_unique = 0
- else:
- # cur or prev is NaN
- return False, False, None
- if not is_monotonic_inc and not is_monotonic_dec:
- return False, False, None
- prev = cur
- return is_monotonic_inc, is_monotonic_dec, is_unique
@cython.boundscheck(False)
@cython.wraparound(False)
def is_monotonic_bool(ndarray[uint8_t] arr, bint timelike):
'''
Returns
-------
- is_monotonic_inc, is_monotonic_dec, is_unique
+ is_monotonic_inc, is_monotonic_dec
'''
cdef:
Py_ssize_t i, n
uint8_t prev, cur
- bint is_unique = 1
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
@@ -2054,33 +2102,41 @@ def is_monotonic_bool(ndarray[uint8_t] arr, bint timelike):
if n == 1:
if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
# single value is NaN
- return False, False, True
+ return False, False
else:
- return True, True, True
+ return True, True
elif n < 2:
- return True, True, True
+ return True, True
if timelike and arr[0] == iNaT:
- return False, False, None
+ return False, False
+
+ with nogil:
+ prev = arr[0]
+ for i in range(1, n):
+ cur = arr[i]
+ if timelike and cur == iNaT:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if cur < prev:
+ is_monotonic_inc = 0
+ elif cur > prev:
+ is_monotonic_dec = 0
+ elif cur == prev:
+ pass # is_unique = 0
+ else:
+ # cur or prev is NaN
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ if not is_monotonic_inc and not is_monotonic_dec:
+ is_monotonic_inc = 0
+ is_monotonic_dec = 0
+ break
+ prev = cur
+ return is_monotonic_inc, is_monotonic_dec
- prev = arr[0]
- for i in range(1, n):
- cur = arr[i]
- if timelike and cur == iNaT:
- return False, False, None
- if cur < prev:
- is_monotonic_inc = 0
- elif cur > prev:
- is_monotonic_dec = 0
- elif cur == prev:
- is_unique = 0
- else:
- # cur or prev is NaN
- return False, False, None
- if not is_monotonic_inc and not is_monotonic_dec:
- return False, False, None
- prev = cur
- return is_monotonic_inc, is_monotonic_dec, is_unique
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -2342,37 +2398,45 @@ def arrmap_bool(ndarray[uint8_t] index, object func):
return maybe_convert_objects(result)
+
@cython.wraparound(False)
-def take_1d_bool_bool(ndarray[uint8_t] values,
- ndarray[int64_t] indexer,
- ndarray[uint8_t] out,
+@cython.boundscheck(False)
+def take_1d_bool_bool(uint8_t[:] values,
+ int64_t[:] indexer,
+ uint8_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
uint8_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_bool_object(ndarray[uint8_t] values,
- ndarray[int64_t] indexer,
- ndarray[object] out,
+@cython.boundscheck(False)
+def take_1d_bool_object(uint8_t[:] values,
+ int64_t[:] indexer,
+ object[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
object fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
+
+
for i from 0 <= i < n:
idx = indexer[i]
if idx == -1:
@@ -2380,18 +2444,22 @@ def take_1d_bool_object(ndarray[uint8_t] values,
else:
out[i] = True if values[idx] > 0 else False
+
@cython.wraparound(False)
-def take_1d_int8_int8(ndarray[int8_t] values,
- ndarray[int64_t] indexer,
- ndarray[int8_t] out,
+@cython.boundscheck(False)
+def take_1d_int8_int8(int8_t[:] values,
+ int64_t[:] indexer,
+ int8_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int8_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
+
+
for i from 0 <= i < n:
idx = indexer[i]
if idx == -1:
@@ -2399,303 +2467,367 @@ def take_1d_int8_int8(ndarray[int8_t] values,
else:
out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int8_int32(ndarray[int8_t] values,
- ndarray[int64_t] indexer,
- ndarray[int32_t] out,
+@cython.boundscheck(False)
+def take_1d_int8_int32(int8_t[:] values,
+ int64_t[:] indexer,
+ int32_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int32_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int8_int64(ndarray[int8_t] values,
- ndarray[int64_t] indexer,
- ndarray[int64_t] out,
+@cython.boundscheck(False)
+def take_1d_int8_int64(int8_t[:] values,
+ int64_t[:] indexer,
+ int64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int8_float64(ndarray[int8_t] values,
- ndarray[int64_t] indexer,
- ndarray[float64_t] out,
+@cython.boundscheck(False)
+def take_1d_int8_float64(int8_t[:] values,
+ int64_t[:] indexer,
+ float64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
float64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int16_int16(ndarray[int16_t] values,
- ndarray[int64_t] indexer,
- ndarray[int16_t] out,
+@cython.boundscheck(False)
+def take_1d_int16_int16(int16_t[:] values,
+ int64_t[:] indexer,
+ int16_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int16_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int16_int32(ndarray[int16_t] values,
- ndarray[int64_t] indexer,
- ndarray[int32_t] out,
+@cython.boundscheck(False)
+def take_1d_int16_int32(int16_t[:] values,
+ int64_t[:] indexer,
+ int32_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int32_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int16_int64(ndarray[int16_t] values,
- ndarray[int64_t] indexer,
- ndarray[int64_t] out,
+@cython.boundscheck(False)
+def take_1d_int16_int64(int16_t[:] values,
+ int64_t[:] indexer,
+ int64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int16_float64(ndarray[int16_t] values,
- ndarray[int64_t] indexer,
- ndarray[float64_t] out,
+@cython.boundscheck(False)
+def take_1d_int16_float64(int16_t[:] values,
+ int64_t[:] indexer,
+ float64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
float64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int32_int32(ndarray[int32_t] values,
- ndarray[int64_t] indexer,
- ndarray[int32_t] out,
+@cython.boundscheck(False)
+def take_1d_int32_int32(int32_t[:] values,
+ int64_t[:] indexer,
+ int32_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int32_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int32_int64(ndarray[int32_t] values,
- ndarray[int64_t] indexer,
- ndarray[int64_t] out,
+@cython.boundscheck(False)
+def take_1d_int32_int64(int32_t[:] values,
+ int64_t[:] indexer,
+ int64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
int64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int32_float64(ndarray[int32_t] values,
- ndarray[int64_t] indexer,
- ndarray[float64_t] out,
+@cython.boundscheck(False)
+def take_1d_int32_float64(int32_t[:] values,
+ int64_t[:] indexer,
+ float64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
float64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
-@cython.wraparound(False)
-def take_1d_int64_int64(ndarray[int64_t] values,
- ndarray[int64_t] indexer,
- ndarray[int64_t] out,
- fill_value=np.nan):
- cdef:
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
+
+@cython.wraparound(False)
+@cython.boundscheck(False)
+def take_1d_int64_int64(int64_t[:] values,
+ int64_t[:] indexer,
+ int64_t[:] out,
+ fill_value=np.nan):
+ cdef:
Py_ssize_t i, n, idx
int64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_int64_float64(ndarray[int64_t] values,
- ndarray[int64_t] indexer,
- ndarray[float64_t] out,
+@cython.boundscheck(False)
+def take_1d_int64_float64(int64_t[:] values,
+ int64_t[:] indexer,
+ float64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
float64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_float32_float32(ndarray[float32_t] values,
- ndarray[int64_t] indexer,
- ndarray[float32_t] out,
+@cython.boundscheck(False)
+def take_1d_float32_float32(float32_t[:] values,
+ int64_t[:] indexer,
+ float32_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
float32_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_float32_float64(ndarray[float32_t] values,
- ndarray[int64_t] indexer,
- ndarray[float64_t] out,
+@cython.boundscheck(False)
+def take_1d_float32_float64(float32_t[:] values,
+ int64_t[:] indexer,
+ float64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
float64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_float64_float64(ndarray[float64_t] values,
- ndarray[int64_t] indexer,
- ndarray[float64_t] out,
+@cython.boundscheck(False)
+def take_1d_float64_float64(float64_t[:] values,
+ int64_t[:] indexer,
+ float64_t[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
float64_t fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
- for i from 0 <= i < n:
- idx = indexer[i]
- if idx == -1:
- out[i] = fv
- else:
- out[i] = values[idx]
+
+ with nogil:
+ for i from 0 <= i < n:
+ idx = indexer[i]
+ if idx == -1:
+ out[i] = fv
+ else:
+ out[i] = values[idx]
+
@cython.wraparound(False)
-def take_1d_object_object(ndarray[object] values,
- ndarray[int64_t] indexer,
- ndarray[object] out,
+@cython.boundscheck(False)
+def take_1d_object_object(object[:] values,
+ int64_t[:] indexer,
+ object[:] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, n, idx
object fv
- n = len(indexer)
+ n = indexer.shape[0]
fv = fill_value
+
+
for i from 0 <= i < n:
idx = indexer[i]
if idx == -1:
@@ -2750,7 +2882,6 @@ cdef inline take_2d_axis0_bool_bool_memview(uint8_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_bool_bool(ndarray[uint8_t, ndim=2] values,
@@ -2851,7 +2982,6 @@ cdef inline take_2d_axis0_bool_object_memview(uint8_t[:, :] values,
out[i, j] = True if values[idx, j] > 0 else False
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_bool_object(ndarray[uint8_t, ndim=2] values,
@@ -2952,7 +3082,6 @@ cdef inline take_2d_axis0_int8_int8_memview(int8_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int8_int8(ndarray[int8_t, ndim=2] values,
@@ -3053,7 +3182,6 @@ cdef inline take_2d_axis0_int8_int32_memview(int8_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int8_int32(ndarray[int8_t, ndim=2] values,
@@ -3154,7 +3282,6 @@ cdef inline take_2d_axis0_int8_int64_memview(int8_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int8_int64(ndarray[int8_t, ndim=2] values,
@@ -3255,7 +3382,6 @@ cdef inline take_2d_axis0_int8_float64_memview(int8_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int8_float64(ndarray[int8_t, ndim=2] values,
@@ -3356,7 +3482,6 @@ cdef inline take_2d_axis0_int16_int16_memview(int16_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int16_int16(ndarray[int16_t, ndim=2] values,
@@ -3457,7 +3582,6 @@ cdef inline take_2d_axis0_int16_int32_memview(int16_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int16_int32(ndarray[int16_t, ndim=2] values,
@@ -3558,7 +3682,6 @@ cdef inline take_2d_axis0_int16_int64_memview(int16_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int16_int64(ndarray[int16_t, ndim=2] values,
@@ -3659,7 +3782,6 @@ cdef inline take_2d_axis0_int16_float64_memview(int16_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int16_float64(ndarray[int16_t, ndim=2] values,
@@ -3760,7 +3882,6 @@ cdef inline take_2d_axis0_int32_int32_memview(int32_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int32_int32(ndarray[int32_t, ndim=2] values,
@@ -3861,7 +3982,6 @@ cdef inline take_2d_axis0_int32_int64_memview(int32_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int32_int64(ndarray[int32_t, ndim=2] values,
@@ -3962,7 +4082,6 @@ cdef inline take_2d_axis0_int32_float64_memview(int32_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int32_float64(ndarray[int32_t, ndim=2] values,
@@ -4063,7 +4182,6 @@ cdef inline take_2d_axis0_int64_int64_memview(int64_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int64_int64(ndarray[int64_t, ndim=2] values,
@@ -4164,7 +4282,6 @@ cdef inline take_2d_axis0_int64_float64_memview(int64_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int64_float64(ndarray[int64_t, ndim=2] values,
@@ -4265,7 +4382,6 @@ cdef inline take_2d_axis0_float32_float32_memview(float32_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_float32_float32(ndarray[float32_t, ndim=2] values,
@@ -4366,7 +4482,6 @@ cdef inline take_2d_axis0_float32_float64_memview(float32_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_float32_float64(ndarray[float32_t, ndim=2] values,
@@ -4467,7 +4582,6 @@ cdef inline take_2d_axis0_float64_float64_memview(float64_t[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_float64_float64(ndarray[float64_t, ndim=2] values,
@@ -4568,7 +4682,6 @@ cdef inline take_2d_axis0_object_object_memview(object[:, :] values,
out[i, j] = values[idx, j]
-
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_object_object(ndarray[object, ndim=2] values,
@@ -4686,6 +4799,7 @@ def take_2d_axis1_bool_bool(ndarray[uint8_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_bool_object_memview(uint8_t[:, :] values,
@@ -4748,6 +4862,7 @@ def take_2d_axis1_bool_object(ndarray[uint8_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = True if values[i, idx] > 0 else False
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int8_int8_memview(int8_t[:, :] values,
@@ -4810,6 +4925,7 @@ def take_2d_axis1_int8_int8(ndarray[int8_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int8_int32_memview(int8_t[:, :] values,
@@ -4872,6 +4988,7 @@ def take_2d_axis1_int8_int32(ndarray[int8_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int8_int64_memview(int8_t[:, :] values,
@@ -4934,6 +5051,7 @@ def take_2d_axis1_int8_int64(ndarray[int8_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int8_float64_memview(int8_t[:, :] values,
@@ -4996,6 +5114,7 @@ def take_2d_axis1_int8_float64(ndarray[int8_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int16_int16_memview(int16_t[:, :] values,
@@ -5058,6 +5177,7 @@ def take_2d_axis1_int16_int16(ndarray[int16_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int16_int32_memview(int16_t[:, :] values,
@@ -5120,6 +5240,7 @@ def take_2d_axis1_int16_int32(ndarray[int16_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int16_int64_memview(int16_t[:, :] values,
@@ -5182,6 +5303,7 @@ def take_2d_axis1_int16_int64(ndarray[int16_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int16_float64_memview(int16_t[:, :] values,
@@ -5244,6 +5366,7 @@ def take_2d_axis1_int16_float64(ndarray[int16_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int32_int32_memview(int32_t[:, :] values,
@@ -5306,6 +5429,7 @@ def take_2d_axis1_int32_int32(ndarray[int32_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int32_int64_memview(int32_t[:, :] values,
@@ -5368,6 +5492,7 @@ def take_2d_axis1_int32_int64(ndarray[int32_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int32_float64_memview(int32_t[:, :] values,
@@ -5430,6 +5555,7 @@ def take_2d_axis1_int32_float64(ndarray[int32_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int64_int64_memview(int64_t[:, :] values,
@@ -5492,6 +5618,7 @@ def take_2d_axis1_int64_int64(ndarray[int64_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_int64_float64_memview(int64_t[:, :] values,
@@ -5554,6 +5681,7 @@ def take_2d_axis1_int64_float64(ndarray[int64_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_float32_float32_memview(float32_t[:, :] values,
@@ -5616,6 +5744,7 @@ def take_2d_axis1_float32_float32(ndarray[float32_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_float32_float64_memview(float32_t[:, :] values,
@@ -5678,6 +5807,7 @@ def take_2d_axis1_float32_float64(ndarray[float32_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_float64_float64_memview(float64_t[:, :] values,
@@ -5740,6 +5870,7 @@ def take_2d_axis1_float64_float64(ndarray[float64_t, ndim=2] values,
out[i, j] = fv
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline take_2d_axis1_object_object_memview(object[:, :] values,
@@ -5803,6 +5934,7 @@ def take_2d_axis1_object_object(ndarray[object, ndim=2] values,
else:
out[i, j] = values[i, idx]
+
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_multi_bool_bool(ndarray[uint8_t, ndim=2] values,
@@ -6379,6 +6511,7 @@ def diff_2d_float64(ndarray[float64_t, ndim=2] arr,
for i in range(sx):
for j in range(start, stop):
out[i, j] = arr[i, j] - arr[i, j - periods]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def diff_2d_float32(ndarray[float32_t, ndim=2] arr,
@@ -6422,6 +6555,7 @@ def diff_2d_float32(ndarray[float32_t, ndim=2] arr,
for i in range(sx):
for j in range(start, stop):
out[i, j] = arr[i, j] - arr[i, j - periods]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def diff_2d_int8(ndarray[int8_t, ndim=2] arr,
@@ -6465,6 +6599,7 @@ def diff_2d_int8(ndarray[int8_t, ndim=2] arr,
for i in range(sx):
for j in range(start, stop):
out[i, j] = arr[i, j] - arr[i, j - periods]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def diff_2d_int16(ndarray[int16_t, ndim=2] arr,
@@ -6508,6 +6643,7 @@ def diff_2d_int16(ndarray[int16_t, ndim=2] arr,
for i in range(sx):
for j in range(start, stop):
out[i, j] = arr[i, j] - arr[i, j - periods]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def diff_2d_int32(ndarray[int32_t, ndim=2] arr,
@@ -6551,6 +6687,7 @@ def diff_2d_int32(ndarray[int32_t, ndim=2] arr,
for i in range(sx):
for j in range(start, stop):
out[i, j] = arr[i, j] - arr[i, j - periods]
+
@cython.boundscheck(False)
@cython.wraparound(False)
def diff_2d_int64(ndarray[int64_t, ndim=2] arr,
@@ -6595,8 +6732,9 @@ def diff_2d_int64(ndarray[int64_t, ndim=2] arr,
for j in range(start, stop):
out[i, j] = arr[i, j] - arr[i, j - periods]
-@cython.boundscheck(False)
+
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_add_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -6605,7 +6743,7 @@ def group_add_float64(ndarray[float64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, count
ndarray[float64_t, ndim=2] sumx, nobs
@@ -6617,42 +6755,49 @@ def group_add_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+
+ if K > 1:
+
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+
+ else:
+
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
- counts[lab] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_add_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -6661,7 +6806,7 @@ def group_add_float32(ndarray[float32_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, count
ndarray[float32_t, ndim=2] sumx, nobs
@@ -6673,43 +6818,50 @@ def group_add_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+
+ if K > 1:
+
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+
+ else:
+
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_add_bin_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -6733,41 +6885,45 @@ def group_add_bin_float64(ndarray[float64_t, ndim=2] out,
ngroups = len(bins) + 1
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ b = 0
+ if K > 1:
+
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_add_bin_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -6791,42 +6947,46 @@ def group_add_bin_float32(ndarray[float32_t, ndim=2] out,
ngroups = len(bins) + 1
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ b = 0
+ if K > 1:
+
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_prod_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -6835,7 +6995,7 @@ def group_prod_float64(ndarray[float64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, count
ndarray[float64_t, ndim=2] prodx, nobs
@@ -6847,42 +7007,44 @@ def group_prod_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ prodx[lab, j] *= val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- prodx[lab, j] *= val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ prodx[lab, 0] *= val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- prodx[lab, 0] *= val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = prodx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = prodx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_prod_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -6891,7 +7053,7 @@ def group_prod_float32(ndarray[float32_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, count
ndarray[float32_t, ndim=2] prodx, nobs
@@ -6903,43 +7065,45 @@ def group_prod_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ prodx[lab, j] *= val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- prodx[lab, j] *= val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ prodx[lab, 0] *= val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- prodx[lab, 0] *= val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = prodx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = prodx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_prod_bin_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -6963,41 +7127,44 @@ def group_prod_bin_float64(ndarray[float64_t, ndim=2] out,
ngroups = len(bins) + 1
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ prodx[b, j] *= val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- prodx[b, j] *= val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ prodx[b, 0] *= val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- prodx[b, 0] *= val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = prodx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = prodx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_prod_bin_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -7021,39 +7188,42 @@ def group_prod_bin_float32(ndarray[float32_t, ndim=2] out,
ngroups = len(bins) + 1
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ prodx[b, j] *= val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- prodx[b, j] *= val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ prodx[b, 0] *= val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- prodx[b, 0] *= val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = prodx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = prodx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -7062,7 +7232,7 @@ def group_var_float64(ndarray[float64_t, ndim=2] out,
ndarray[float64_t, ndim=2] values,
ndarray[int64_t] labels):
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, ct
ndarray[float64_t, ndim=2] nobs, sumx, sumxx
@@ -7075,47 +7245,50 @@ def group_var_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
+ with nogil:
+ if K > 1:
+ for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
+ counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+ sumxx[lab, j] += val * val
+ else:
+ for i in range(N):
+
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- sumxx[lab, j] += val * val
- else:
- for i in range(N):
-
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
+ sumxx[lab, 0] += val * val
- counts[lab] += 1
- val = values[i, 0]
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
- sumxx[lab, 0] += val * val
+ for i in range(ncounts):
+ for j in range(K):
+ ct = nobs[i, j]
+ if ct < 2:
+ out[i, j] = NAN
+ else:
+ out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
+ (ct * ct - ct))
- for i in range(len(counts)):
- for j in range(K):
- ct = nobs[i, j]
- if ct < 2:
- out[i, j] = nan
- else:
- out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
- (ct * ct - ct))
@cython.wraparound(False)
@cython.boundscheck(False)
def group_var_float32(ndarray[float32_t, ndim=2] out,
@@ -7123,7 +7296,7 @@ def group_var_float32(ndarray[float32_t, ndim=2] out,
ndarray[float32_t, ndim=2] values,
ndarray[int64_t] labels):
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, ct
ndarray[float32_t, ndim=2] nobs, sumx, sumxx
@@ -7136,47 +7309,50 @@ def group_var_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
+ with nogil:
+ if K > 1:
+ for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
+ counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+ sumxx[lab, j] += val * val
+ else:
+ for i in range(N):
+
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- sumxx[lab, j] += val * val
- else:
- for i in range(N):
-
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
+ sumxx[lab, 0] += val * val
- counts[lab] += 1
- val = values[i, 0]
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
- sumxx[lab, 0] += val * val
+ for i in range(ncounts):
+ for j in range(K):
+ ct = nobs[i, j]
+ if ct < 2:
+ out[i, j] = NAN
+ else:
+ out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
+ (ct * ct - ct))
- for i in range(len(counts)):
- for j in range(K):
- ct = nobs[i, j]
- if ct < 2:
- out[i, j] = nan
- else:
- out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
- (ct * ct - ct))
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -7203,44 +7379,46 @@ def group_var_bin_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
+ counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ sumxx[b, j] += val * val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- sumxx[b, j] += val * val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
+ sumxx[b, 0] += val * val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
- sumxx[b, 0] += val * val
+ for i in range(ngroups):
+ for j in range(K):
+ ct = nobs[i, j]
+ if ct < 2:
+ out[i, j] = NAN
+ else:
+ out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
+ (ct * ct - ct))
- for i in range(ngroups):
- for j in range(K):
- ct = nobs[i, j]
- if ct < 2:
- out[i, j] = nan
- else:
- out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
- (ct * ct - ct))
@cython.wraparound(False)
@cython.boundscheck(False)
def group_var_bin_float32(ndarray[float32_t, ndim=2] out,
@@ -7266,44 +7444,46 @@ def group_var_bin_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
+ counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ sumxx[b, j] += val * val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- sumxx[b, j] += val * val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
+ sumxx[b, 0] += val * val
- counts[b] += 1
- val = values[i, 0]
-
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
- sumxx[b, 0] += val * val
+ for i in range(ngroups):
+ for j in range(K):
+ ct = nobs[i, j]
+ if ct < 2:
+ out[i, j] = NAN
+ else:
+ out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
+ (ct * ct - ct))
- for i in range(ngroups):
- for j in range(K):
- ct = nobs[i, j]
- if ct < 2:
- out[i, j] = nan
- else:
- out[i, j] = ((ct * sumxx[i, j] - sumx[i, j] * sumx[i, j]) /
- (ct * ct - ct))
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -7312,7 +7492,7 @@ def group_mean_float64(ndarray[float64_t, ndim=2] out,
ndarray[float64_t, ndim=2] values,
ndarray[int64_t] labels):
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, count
ndarray[float64_t, ndim=2] sumx, nobs
@@ -7324,39 +7504,41 @@ def group_mean_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
- counts[lab] += 1
- val = values[i, 0]
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
+ for i in range(ncounts):
+ for j in range(K):
+ count = nobs[i, j]
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j] / count
- for i in range(len(counts)):
- for j in range(K):
- count = nobs[i, j]
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j] / count
@cython.wraparound(False)
@cython.boundscheck(False)
def group_mean_float32(ndarray[float32_t, ndim=2] out,
@@ -7364,7 +7546,7 @@ def group_mean_float32(ndarray[float32_t, ndim=2] out,
ndarray[float32_t, ndim=2] values,
ndarray[int64_t] labels):
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, count
ndarray[float32_t, ndim=2] sumx, nobs
@@ -7376,41 +7558,44 @@ def group_mean_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ sumx[lab, j] += val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- sumx[lab, j] += val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ nobs[lab, 0] += 1
+ sumx[lab, 0] += val
- counts[lab] += 1
- val = values[i, 0]
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- sumx[lab, 0] += val
+ for i in range(ncounts):
+ for j in range(K):
+ count = nobs[i, j]
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j] / count
- for i in range(len(counts)):
- for j in range(K):
- count = nobs[i, j]
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j] / count
+@cython.boundscheck(False)
def group_mean_bin_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -7431,41 +7616,44 @@ def group_mean_bin_float64(ndarray[float64_t, ndim=2] out,
else:
ngroups = len(bins) + 1
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
- counts[b] += 1
- val = values[i, 0]
+ for i in range(ngroups):
+ for j in range(K):
+ count = nobs[i, j]
+ if count == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j] / count
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
-
- for i in range(ngroups):
- for j in range(K):
- count = nobs[i, j]
- if count == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j] / count
+@cython.boundscheck(False)
def group_mean_bin_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -7486,40 +7674,42 @@ def group_mean_bin_float32(ndarray[float32_t, ndim=2] out,
else:
ngroups = len(bins) + 1
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ sumx[b, j] += val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- sumx[b, j] += val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ sumx[b, 0] += val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- sumx[b, 0] += val
+ for i in range(ngroups):
+ for j in range(K):
+ count = nobs[i, j]
+ if count == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = sumx[i, j] / count
- for i in range(ngroups):
- for j in range(K):
- count = nobs[i, j]
- if count == 0:
- out[i, j] = nan
- else:
- out[i, j] = sumx[i, j] / count
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -7533,7 +7723,7 @@ def group_ohlc_float64(ndarray[float64_t, ndim=2] out,
cdef:
Py_ssize_t i, j, N, K, ngroups, b
float64_t val, count
- float64_t vopen, vhigh, vlow, vclose, NA
+ float64_t vopen, vhigh, vlow, vclose
bint got_first = 0
if len(bins) == 0:
@@ -7548,55 +7738,56 @@ def group_ohlc_float64(ndarray[float64_t, ndim=2] out,
if out.shape[1] != 4:
raise ValueError('Output array must have 4 columns')
- NA = np.nan
-
b = 0
if K > 1:
raise NotImplementedError("Argument 'values' must have only "
"one dimension")
else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- if not got_first:
- out[b, 0] = NA
- out[b, 1] = NA
- out[b, 2] = NA
- out[b, 3] = NA
- else:
- out[b, 0] = vopen
- out[b, 1] = vhigh
- out[b, 2] = vlow
- out[b, 3] = vclose
- b += 1
- got_first = 0
- counts[b] += 1
- val = values[i, 0]
+ with nogil:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ if not got_first:
+ out[b, 0] = NAN
+ out[b, 1] = NAN
+ out[b, 2] = NAN
+ out[b, 3] = NAN
+ else:
+ out[b, 0] = vopen
+ out[b, 1] = vhigh
+ out[b, 2] = vlow
+ out[b, 3] = vclose
+ b += 1
+ got_first = 0
- # not nan
- if val == val:
- if not got_first:
- got_first = 1
- vopen = val
- vlow = val
- vhigh = val
- else:
- if val < vlow:
+ counts[b] += 1
+ val = values[i, 0]
+
+ # not nan
+ if val == val:
+ if not got_first:
+ got_first = 1
+ vopen = val
vlow = val
- if val > vhigh:
vhigh = val
- vclose = val
+ else:
+ if val < vlow:
+ vlow = val
+ if val > vhigh:
+ vhigh = val
+ vclose = val
+
+ if not got_first:
+ out[b, 0] = NAN
+ out[b, 1] = NAN
+ out[b, 2] = NAN
+ out[b, 3] = NAN
+ else:
+ out[b, 0] = vopen
+ out[b, 1] = vhigh
+ out[b, 2] = vlow
+ out[b, 3] = vclose
- if not got_first:
- out[b, 0] = NA
- out[b, 1] = NA
- out[b, 2] = NA
- out[b, 3] = NA
- else:
- out[b, 0] = vopen
- out[b, 1] = vhigh
- out[b, 2] = vlow
- out[b, 3] = vclose
@cython.wraparound(False)
@cython.boundscheck(False)
def group_ohlc_float32(ndarray[float32_t, ndim=2] out,
@@ -7609,7 +7800,7 @@ def group_ohlc_float32(ndarray[float32_t, ndim=2] out,
cdef:
Py_ssize_t i, j, N, K, ngroups, b
float32_t val, count
- float32_t vopen, vhigh, vlow, vclose, NA
+ float32_t vopen, vhigh, vlow, vclose
bint got_first = 0
if len(bins) == 0:
@@ -7624,58 +7815,59 @@ def group_ohlc_float32(ndarray[float32_t, ndim=2] out,
if out.shape[1] != 4:
raise ValueError('Output array must have 4 columns')
- NA = np.nan
-
b = 0
if K > 1:
raise NotImplementedError("Argument 'values' must have only "
"one dimension")
else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- if not got_first:
- out[b, 0] = NA
- out[b, 1] = NA
- out[b, 2] = NA
- out[b, 3] = NA
- else:
- out[b, 0] = vopen
- out[b, 1] = vhigh
- out[b, 2] = vlow
- out[b, 3] = vclose
- b += 1
- got_first = 0
- counts[b] += 1
- val = values[i, 0]
+ with nogil:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ if not got_first:
+ out[b, 0] = NAN
+ out[b, 1] = NAN
+ out[b, 2] = NAN
+ out[b, 3] = NAN
+ else:
+ out[b, 0] = vopen
+ out[b, 1] = vhigh
+ out[b, 2] = vlow
+ out[b, 3] = vclose
+ b += 1
+ got_first = 0
- # not nan
- if val == val:
- if not got_first:
- got_first = 1
- vopen = val
- vlow = val
- vhigh = val
- else:
- if val < vlow:
+ counts[b] += 1
+ val = values[i, 0]
+
+ # not nan
+ if val == val:
+ if not got_first:
+ got_first = 1
+ vopen = val
vlow = val
- if val > vhigh:
vhigh = val
- vclose = val
+ else:
+ if val < vlow:
+ vlow = val
+ if val > vhigh:
+ vhigh = val
+ vclose = val
+
+ if not got_first:
+ out[b, 0] = NAN
+ out[b, 1] = NAN
+ out[b, 2] = NAN
+ out[b, 3] = NAN
+ else:
+ out[b, 0] = vopen
+ out[b, 1] = vhigh
+ out[b, 2] = vlow
+ out[b, 3] = vclose
- if not got_first:
- out[b, 0] = NA
- out[b, 1] = NA
- out[b, 2] = NA
- out[b, 3] = NA
- else:
- out[b, 0] = vopen
- out[b, 1] = vhigh
- out[b, 2] = vlow
- out[b, 3] = vclose
@cython.wraparound(False)
-@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -7684,7 +7876,7 @@ def group_last_float64(ndarray[float64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, count
ndarray[float64_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -7697,28 +7889,30 @@ def group_last_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ resx[lab, j] = val
+
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.wraparound(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -7727,7 +7921,7 @@ def group_last_float32(ndarray[float32_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, count
ndarray[float32_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -7740,28 +7934,30 @@ def group_last_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ resx[lab, j] = val
+
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.wraparound(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[int64_t, ndim=2] values,
@@ -7770,7 +7966,7 @@ def group_last_int64(ndarray[int64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
int64_t val, count
ndarray[int64_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -7783,29 +7979,31 @@ def group_last_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ resx[lab, j] = val
+
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = resx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = resx[i, j]
@cython.wraparound(False)
-@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_bin_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -7830,28 +8028,30 @@ def group_last_bin_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, j] += 1
- resx[b, j] = val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ resx[b, j] = val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.wraparound(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_bin_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -7876,28 +8076,30 @@ def group_last_bin_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, j] += 1
- resx[b, j] = val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ resx[b, j] = val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.wraparound(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_last_bin_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[int64_t, ndim=2] values,
@@ -7922,29 +8124,31 @@ def group_last_bin_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, j] += 1
- resx[b, j] = val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ resx[b, j] = val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = resx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = resx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -7953,7 +8157,7 @@ def group_nth_float64(ndarray[float64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, count
ndarray[float64_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -7966,29 +8170,31 @@ def group_nth_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- if nobs[lab, j] == rank:
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if nobs[lab, j] == rank:
+ resx[lab, j] = val
+
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -7997,7 +8203,7 @@ def group_nth_float32(ndarray[float32_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, count
ndarray[float32_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -8010,29 +8216,31 @@ def group_nth_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- if nobs[lab, j] == rank:
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if nobs[lab, j] == rank:
+ resx[lab, j] = val
+
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[int64_t, ndim=2] values,
@@ -8041,7 +8249,7 @@ def group_nth_int64(ndarray[int64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
int64_t val, count
ndarray[int64_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
@@ -8054,30 +8262,32 @@ def group_nth_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- if nobs[lab, j] == rank:
- resx[lab, j] = val
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if nobs[lab, j] == rank:
+ resx[lab, j] = val
+
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = resx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = resx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_bin_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -8102,29 +8312,31 @@ def group_nth_bin_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, j] += 1
- if nobs[b, j] == rank:
- resx[b, j] = val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if nobs[b, j] == rank:
+ resx[b, j] = val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_bin_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -8149,29 +8361,31 @@ def group_nth_bin_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, j] += 1
- if nobs[b, j] == rank:
- resx[b, j] = val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if nobs[b, j] == rank:
+ resx[b, j] = val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = resx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = resx[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_nth_bin_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[int64_t, ndim=2] values,
@@ -8196,27 +8410,29 @@ def group_nth_bin_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[b, j] += 1
- if nobs[b, j] == rank:
- resx[b, j] = val
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if nobs[b, j] == rank:
+ resx[b, j] = val
+
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = resx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = resx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -8228,7 +8444,7 @@ def group_min_float64(ndarray[float64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, count
ndarray[float64_t, ndim=2] minx, nobs
@@ -8242,42 +8458,44 @@ def group_min_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val < minx[lab, j]:
+ minx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- if val < minx[lab, j]:
- minx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ if val < minx[lab, 0]:
+ minx[lab, 0] = val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val < minx[lab, 0]:
- minx[lab, 0] = val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = minx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = minx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_min_float32(ndarray[float32_t, ndim=2] out,
@@ -8288,7 +8506,7 @@ def group_min_float32(ndarray[float32_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, count
ndarray[float32_t, ndim=2] minx, nobs
@@ -8302,42 +8520,44 @@ def group_min_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val < minx[lab, j]:
+ minx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- if val < minx[lab, j]:
- minx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ if val < minx[lab, 0]:
+ minx[lab, 0] = val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val < minx[lab, 0]:
- minx[lab, 0] = val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = minx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = minx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_min_int64(ndarray[int64_t, ndim=2] out,
@@ -8348,7 +8568,7 @@ def group_min_int64(ndarray[int64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
int64_t val, count
ndarray[int64_t, ndim=2] minx, nobs
@@ -8362,42 +8582,44 @@ def group_min_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val < minx[lab, j]:
+ minx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- if val < minx[lab, j]:
- minx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ if val < minx[lab, 0]:
+ minx[lab, 0] = val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val < minx[lab, 0]:
- minx[lab, 0] = val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = minx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = minx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -8427,41 +8649,43 @@ def group_min_bin_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val < minx[b, j]:
+ minx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val < minx[b, j]:
- minx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ if val < minx[b, 0]:
+ minx[b, 0] = val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val < minx[b, 0]:
- minx[b, 0] = val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = minx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = minx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_min_bin_float32(ndarray[float32_t, ndim=2] out,
@@ -8490,41 +8714,43 @@ def group_min_bin_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val < minx[b, j]:
+ minx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val < minx[b, j]:
- minx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ if val < minx[b, 0]:
+ minx[b, 0] = val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val < minx[b, 0]:
- minx[b, 0] = val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = minx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = minx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_min_bin_int64(ndarray[int64_t, ndim=2] out,
@@ -8553,41 +8779,43 @@ def group_min_bin_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val < minx[b, j]:
+ minx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val < minx[b, j]:
- minx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ if val < minx[b, 0]:
+ minx[b, 0] = val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val < minx[b, 0]:
- minx[b, 0] = val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = minx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = minx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -8599,7 +8827,7 @@ def group_max_float64(ndarray[float64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float64_t val, count
ndarray[float64_t, ndim=2] maxx, nobs
@@ -8613,42 +8841,44 @@ def group_max_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val > maxx[lab, j]:
+ maxx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- if val > maxx[lab, j]:
- maxx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ if val > maxx[lab, 0]:
+ maxx[lab, 0] = val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val > maxx[lab, 0]:
- maxx[lab, 0] = val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = maxx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = maxx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_max_float32(ndarray[float32_t, ndim=2] out,
@@ -8659,7 +8889,7 @@ def group_max_float32(ndarray[float32_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
float32_t val, count
ndarray[float32_t, ndim=2] maxx, nobs
@@ -8673,42 +8903,44 @@ def group_max_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val > maxx[lab, j]:
+ maxx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
+
+ counts[lab] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[lab, j] += 1
- if val > maxx[lab, j]:
- maxx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
-
- counts[lab] += 1
- val = values[i, 0]
+ nobs[lab, 0] += 1
+ if val > maxx[lab, 0]:
+ maxx[lab, 0] = val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val > maxx[lab, 0]:
- maxx[lab, 0] = val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = maxx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = maxx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_max_int64(ndarray[int64_t, ndim=2] out,
@@ -8719,7 +8951,7 @@ def group_max_int64(ndarray[int64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, N, K, lab
+ Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
int64_t val, count
ndarray[int64_t, ndim=2] maxx, nobs
@@ -8733,42 +8965,44 @@ def group_max_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- if K > 1:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ with nogil:
+ if K > 1:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- # not nan
- if val == val:
- nobs[lab, j] += 1
- if val > maxx[lab, j]:
- maxx[lab, j] = val
- else:
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
+ # not nan
+ if val == val:
+ nobs[lab, j] += 1
+ if val > maxx[lab, j]:
+ maxx[lab, j] = val
+ else:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- counts[lab] += 1
- val = values[i, 0]
+ counts[lab] += 1
+ val = values[i, 0]
+
+ # not nan
+ if val == val:
+ nobs[lab, 0] += 1
+ if val > maxx[lab, 0]:
+ maxx[lab, 0] = val
- # not nan
- if val == val:
- nobs[lab, 0] += 1
- if val > maxx[lab, 0]:
- maxx[lab, 0] = val
+ for i in range(ncounts):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = maxx[i, j]
- for i in range(len(counts)):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = maxx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
@@ -8797,41 +9031,43 @@ def group_max_bin_float64(ndarray[float64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val > maxx[b, j]:
+ maxx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val > maxx[b, j]:
- maxx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ if val > maxx[b, 0]:
+ maxx[b, 0] = val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val > maxx[b, 0]:
- maxx[b, 0] = val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = maxx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = maxx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_max_bin_float32(ndarray[float32_t, ndim=2] out,
@@ -8859,41 +9095,43 @@ def group_max_bin_float32(ndarray[float32_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val > maxx[b, j]:
+ maxx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val > maxx[b, j]:
- maxx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ if val > maxx[b, 0]:
+ maxx[b, 0] = val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val > maxx[b, 0]:
- maxx[b, 0] = val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = NAN
+ else:
+ out[i, j] = maxx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = nan
- else:
- out[i, j] = maxx[i, j]
@cython.wraparound(False)
@cython.boundscheck(False)
def group_max_bin_int64(ndarray[int64_t, ndim=2] out,
@@ -8921,41 +9159,43 @@ def group_max_bin_int64(ndarray[int64_t, ndim=2] out,
N, K = (<object> values).shape
- b = 0
- if K > 1:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
+ with nogil:
+ b = 0
+ if K > 1:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
+
+ # not nan
+ if val == val:
+ nobs[b, j] += 1
+ if val > maxx[b, j]:
+ maxx[b, j] = val
+ else:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
+
+ counts[b] += 1
+ val = values[i, 0]
# not nan
if val == val:
- nobs[b, j] += 1
- if val > maxx[b, j]:
- maxx[b, j] = val
- else:
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- val = values[i, 0]
+ nobs[b, 0] += 1
+ if val > maxx[b, 0]:
+ maxx[b, 0] = val
- # not nan
- if val == val:
- nobs[b, 0] += 1
- if val > maxx[b, 0]:
- maxx[b, 0] = val
+ for i in range(ngroups):
+ for j in range(K):
+ if nobs[i, j] == 0:
+ out[i, j] = iNaT
+ else:
+ out[i, j] = maxx[i, j]
- for i in range(ngroups):
- for j in range(K):
- if nobs[i, j] == 0:
- out[i, j] = iNaT
- else:
- out[i, j] = maxx[i, j]
@cython.boundscheck(False)
@cython.wraparound(False)
@@ -8967,31 +9207,32 @@ def group_count_float64(ndarray[float64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, lab
+ Py_ssize_t i, j, lab, ncounts = len(counts)
Py_ssize_t N = values.shape[0], K = values.shape[1]
float64_t val
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)
if len(values) != len(labels):
- raise AssertionError("len(index) != len(labels)")
+ raise AssertionError("len(index) != len(labels)")
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- # not nan
- nobs[lab, j] += val == val and val != iNaT
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(len(counts)):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[lab, j] += val == val and val != iNaT
+ for i in range(ncounts):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
@cython.boundscheck(False)
@cython.wraparound(False)
@@ -9003,31 +9244,32 @@ def group_count_float32(ndarray[float32_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, lab
+ Py_ssize_t i, j, lab, ncounts = len(counts)
Py_ssize_t N = values.shape[0], K = values.shape[1]
float32_t val
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)
if len(values) != len(labels):
- raise AssertionError("len(index) != len(labels)")
+ raise AssertionError("len(index) != len(labels)")
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- # not nan
- nobs[lab, j] += val == val and val != iNaT
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(len(counts)):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[lab, j] += val == val and val != iNaT
+ for i in range(ncounts):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
@cython.boundscheck(False)
@cython.wraparound(False)
@@ -9039,31 +9281,32 @@ def group_count_int64(ndarray[int64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, lab
+ Py_ssize_t i, j, lab, ncounts = len(counts)
Py_ssize_t N = values.shape[0], K = values.shape[1]
int64_t val
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)
if len(values) != len(labels):
- raise AssertionError("len(index) != len(labels)")
+ raise AssertionError("len(index) != len(labels)")
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- # not nan
- nobs[lab, j] += val == val and val != iNaT
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(len(counts)):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[lab, j] += val == val and val != iNaT
+ for i in range(ncounts):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
@cython.boundscheck(False)
@cython.wraparound(False)
@@ -9075,15 +9318,17 @@ def group_count_object(ndarray[object, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, lab
+ Py_ssize_t i, j, lab, ncounts = len(counts)
Py_ssize_t N = values.shape[0], K = values.shape[1]
object val
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)
if len(values) != len(labels):
- raise AssertionError("len(index) != len(labels)")
+ raise AssertionError("len(index) != len(labels)")
+
+
for i in range(N):
lab = labels[i]
if lab < 0:
@@ -9096,11 +9341,10 @@ def group_count_object(ndarray[object, ndim=2] out,
# not nan
nobs[lab, j] += val == val and val != iNaT
- for i in range(len(counts)):
+ for i in range(ncounts):
for j in range(K):
out[i, j] = nobs[i, j]
-
@cython.boundscheck(False)
@cython.wraparound(False)
def group_count_int64(ndarray[int64_t, ndim=2] out,
@@ -9111,35 +9355,36 @@ def group_count_int64(ndarray[int64_t, ndim=2] out,
Only aggregates on axis=0
'''
cdef:
- Py_ssize_t i, j, lab
+ Py_ssize_t i, j, lab, ncounts = len(counts)
Py_ssize_t N = values.shape[0], K = values.shape[1]
int64_t val
ndarray[int64_t, ndim=2] nobs = np.zeros((out.shape[0], out.shape[1]),
dtype=np.int64)
if len(values) != len(labels):
- raise AssertionError("len(index) != len(labels)")
+ raise AssertionError("len(index) != len(labels)")
- for i in range(N):
- lab = labels[i]
- if lab < 0:
- continue
- counts[lab] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ lab = labels[i]
+ if lab < 0:
+ continue
- # not nan
- nobs[lab, j] += val == val and val != iNaT
+ counts[lab] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(len(counts)):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[lab, j] += val == val and val != iNaT
+ for i in range(ncounts):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_count_bin_float64(ndarray[float64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float64_t, ndim=2] values,
@@ -9158,24 +9403,24 @@ def group_count_bin_float64(ndarray[float64_t, ndim=2] out,
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- # not nan
- nobs[b, j] += val == val and val != iNaT
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(ngroups):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[b, j] += val == val and val != iNaT
+ for i in range(ngroups):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_count_bin_float32(ndarray[float32_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[float32_t, ndim=2] values,
@@ -9194,24 +9439,24 @@ def group_count_bin_float32(ndarray[float32_t, ndim=2] out,
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- # not nan
- nobs[b, j] += val == val and val != iNaT
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(ngroups):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[b, j] += val == val and val != iNaT
+ for i in range(ngroups):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_count_bin_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[int64_t, ndim=2] values,
@@ -9230,24 +9475,24 @@ def group_count_bin_int64(ndarray[int64_t, ndim=2] out,
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- # not nan
- nobs[b, j] += val == val and val != iNaT
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(ngroups):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[b, j] += val == val and val != iNaT
+ for i in range(ngroups):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_count_bin_object(ndarray[object, ndim=2] out,
ndarray[int64_t] counts,
ndarray[object, ndim=2] values,
@@ -9266,6 +9511,7 @@ def group_count_bin_object(ndarray[object, ndim=2] out,
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)
+
for i in range(N):
while b < ngroups - 1 and i >= bins[b]:
b += 1
@@ -9281,9 +9527,8 @@ def group_count_bin_object(ndarray[object, ndim=2] out,
for j in range(K):
out[i, j] = nobs[i, j]
-
-@cython.boundscheck(False)
@cython.wraparound(False)
+@cython.boundscheck(False)
def group_count_bin_int64(ndarray[int64_t, ndim=2] out,
ndarray[int64_t] counts,
ndarray[int64_t, ndim=2] values,
@@ -9302,21 +9547,21 @@ def group_count_bin_int64(ndarray[int64_t, ndim=2] out,
return
ngroups = len(bins) + (bins[len(bins) - 1] != N)
- for i in range(N):
- while b < ngroups - 1 and i >= bins[b]:
- b += 1
-
- counts[b] += 1
- for j in range(K):
- val = values[i, j]
+ with nogil:
+ for i in range(N):
+ while b < ngroups - 1 and i >= bins[b]:
+ b += 1
- # not nan
- nobs[b, j] += val == val and val != iNaT
+ counts[b] += 1
+ for j in range(K):
+ val = values[i, j]
- for i in range(ngroups):
- for j in range(K):
- out[i, j] = nobs[i, j]
+ # not nan
+ nobs[b, j] += val == val and val != iNaT
+ for i in range(ngroups):
+ for j in range(K):
+ out[i, j] = nobs[i, j]
@cython.wraparound(False)
diff --git a/pandas/src/khash.pxd b/pandas/src/khash.pxd
index a8fd51a62cfbe..b28f43eecfac7 100644
--- a/pandas/src/khash.pxd
+++ b/pandas/src/khash.pxd
@@ -45,15 +45,15 @@ cdef extern from "khash_python.h":
kh_cstr_t *keys
size_t *vals
- inline kh_str_t* kh_init_str()
- inline void kh_destroy_str(kh_str_t*)
- inline void kh_clear_str(kh_str_t*)
- inline khint_t kh_get_str(kh_str_t*, kh_cstr_t)
- inline void kh_resize_str(kh_str_t*, khint_t)
- inline khint_t kh_put_str(kh_str_t*, kh_cstr_t, int*)
- inline void kh_del_str(kh_str_t*, khint_t)
+ inline kh_str_t* kh_init_str() nogil
+ inline void kh_destroy_str(kh_str_t*) nogil
+ inline void kh_clear_str(kh_str_t*) nogil
+ inline khint_t kh_get_str(kh_str_t*, kh_cstr_t) nogil
+ inline void kh_resize_str(kh_str_t*, khint_t) nogil
+ inline khint_t kh_put_str(kh_str_t*, kh_cstr_t, int*) nogil
+ inline void kh_del_str(kh_str_t*, khint_t) nogil
- bint kh_exist_str(kh_str_t*, khiter_t)
+ bint kh_exist_str(kh_str_t*, khiter_t) nogil
ctypedef struct kh_int64_t:
@@ -62,15 +62,15 @@ cdef extern from "khash_python.h":
int64_t *keys
size_t *vals
- inline kh_int64_t* kh_init_int64()
- inline void kh_destroy_int64(kh_int64_t*)
- inline void kh_clear_int64(kh_int64_t*)
- inline khint_t kh_get_int64(kh_int64_t*, int64_t)
- inline void kh_resize_int64(kh_int64_t*, khint_t)
- inline khint_t kh_put_int64(kh_int64_t*, int64_t, int*)
- inline void kh_del_int64(kh_int64_t*, khint_t)
+ inline kh_int64_t* kh_init_int64() nogil
+ inline void kh_destroy_int64(kh_int64_t*) nogil
+ inline void kh_clear_int64(kh_int64_t*) nogil
+ inline khint_t kh_get_int64(kh_int64_t*, int64_t) nogil
+ inline void kh_resize_int64(kh_int64_t*, khint_t) nogil
+ inline khint_t kh_put_int64(kh_int64_t*, int64_t, int*) nogil
+ inline void kh_del_int64(kh_int64_t*, khint_t) nogil
- bint kh_exist_int64(kh_int64_t*, khiter_t)
+ bint kh_exist_int64(kh_int64_t*, khiter_t) nogil
ctypedef struct kh_float64_t:
khint_t n_buckets, size, n_occupied, upper_bound
@@ -78,15 +78,15 @@ cdef extern from "khash_python.h":
float64_t *keys
size_t *vals
- inline kh_float64_t* kh_init_float64()
- inline void kh_destroy_float64(kh_float64_t*)
- inline void kh_clear_float64(kh_float64_t*)
- inline khint_t kh_get_float64(kh_float64_t*, float64_t)
- inline void kh_resize_float64(kh_float64_t*, khint_t)
- inline khint_t kh_put_float64(kh_float64_t*, float64_t, int*)
- inline void kh_del_float64(kh_float64_t*, khint_t)
+ inline kh_float64_t* kh_init_float64() nogil
+ inline void kh_destroy_float64(kh_float64_t*) nogil
+ inline void kh_clear_float64(kh_float64_t*) nogil
+ inline khint_t kh_get_float64(kh_float64_t*, float64_t) nogil
+ inline void kh_resize_float64(kh_float64_t*, khint_t) nogil
+ inline khint_t kh_put_float64(kh_float64_t*, float64_t, int*) nogil
+ inline void kh_del_float64(kh_float64_t*, khint_t) nogil
- bint kh_exist_float64(kh_float64_t*, khiter_t)
+ bint kh_exist_float64(kh_float64_t*, khiter_t) nogil
ctypedef struct kh_int32_t:
khint_t n_buckets, size, n_occupied, upper_bound
@@ -94,15 +94,15 @@ cdef extern from "khash_python.h":
int32_t *keys
size_t *vals
- inline kh_int32_t* kh_init_int32()
- inline void kh_destroy_int32(kh_int32_t*)
- inline void kh_clear_int32(kh_int32_t*)
- inline khint_t kh_get_int32(kh_int32_t*, int32_t)
- inline void kh_resize_int32(kh_int32_t*, khint_t)
- inline khint_t kh_put_int32(kh_int32_t*, int32_t, int*)
- inline void kh_del_int32(kh_int32_t*, khint_t)
+ inline kh_int32_t* kh_init_int32() nogil
+ inline void kh_destroy_int32(kh_int32_t*) nogil
+ inline void kh_clear_int32(kh_int32_t*) nogil
+ inline khint_t kh_get_int32(kh_int32_t*, int32_t) nogil
+ inline void kh_resize_int32(kh_int32_t*, khint_t) nogil
+ inline khint_t kh_put_int32(kh_int32_t*, int32_t, int*) nogil
+ inline void kh_del_int32(kh_int32_t*, khint_t) nogil
- bint kh_exist_int32(kh_int32_t*, khiter_t)
+ bint kh_exist_int32(kh_int32_t*, khiter_t) nogil
# sweep factorize
@@ -112,13 +112,12 @@ cdef extern from "khash_python.h":
kh_cstr_t *keys
PyObject **vals
- inline kh_strbox_t* kh_init_strbox()
- inline void kh_destroy_strbox(kh_strbox_t*)
- inline void kh_clear_strbox(kh_strbox_t*)
- inline khint_t kh_get_strbox(kh_strbox_t*, kh_cstr_t)
- inline void kh_resize_strbox(kh_strbox_t*, khint_t)
- inline khint_t kh_put_strbox(kh_strbox_t*, kh_cstr_t, int*)
- inline void kh_del_strbox(kh_strbox_t*, khint_t)
-
- bint kh_exist_strbox(kh_strbox_t*, khiter_t)
+ inline kh_strbox_t* kh_init_strbox() nogil
+ inline void kh_destroy_strbox(kh_strbox_t*) nogil
+ inline void kh_clear_strbox(kh_strbox_t*) nogil
+ inline khint_t kh_get_strbox(kh_strbox_t*, kh_cstr_t) nogil
+ inline void kh_resize_strbox(kh_strbox_t*, khint_t) nogil
+ inline khint_t kh_put_strbox(kh_strbox_t*, kh_cstr_t, int*) nogil
+ inline void kh_del_strbox(kh_strbox_t*, khint_t) nogil
+ bint kh_exist_strbox(kh_strbox_t*, khiter_t) nogil
diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 83d6b97788e91..f4d27932f1f3f 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -1817,3 +1817,36 @@ def use_numexpr(use, min_elements=expr._MIN_ELEMENTS):
for name, obj in inspect.getmembers(sys.modules[__name__]):
if inspect.isfunction(obj) and name.startswith('assert'):
setattr(TestCase, name, staticmethod(obj))
+
+def test_parallel(num_threads=2):
+ """Decorator to run the same function multiple times in parallel.
+
+ Parameters
+ ----------
+ num_threads : int, optional
+ The number of times the function is run in parallel.
+
+ Notes
+ -----
+ This decorator does not pass the return value of the decorated function.
+
+ Original from scikit-image: https://github.com/scikit-image/scikit-image/pull/1519
+
+ """
+
+ assert num_threads > 0
+ import threading
+
+ def wrapper(func):
+ @wraps(func)
+ def inner(*args, **kwargs):
+ threads = []
+ for i in range(num_threads):
+ thread = threading.Thread(target=func, args=args, kwargs=kwargs)
+ threads.append(thread)
+ for thread in threads:
+ thread.start()
+ for thread in threads:
+ thread.join()
+ return inner
+ return wrapper
diff --git a/vb_suite/gil.py b/vb_suite/gil.py
new file mode 100644
index 0000000000000..30f41bb3c738d
--- /dev/null
+++ b/vb_suite/gil.py
@@ -0,0 +1,98 @@
+from vbench.api import Benchmark
+from datetime import datetime
+
+common_setup = """from pandas_vb_common import *
+"""
+
+basic = common_setup + """
+from pandas.util.testing import test_parallel
+
+N = 1000000
+ngroups = 1000
+np.random.seed(1234)
+
+df = DataFrame({'key' : np.random.randint(0,ngroups,size=N),
+ 'data' : np.random.randn(N) })
+"""
+
+setup = basic + """
+
+def f():
+ df.groupby('key')['data'].sum()
+
+# run consecutivily
+def g2():
+ for i in range(2):
+ f()
+def g4():
+ for i in range(4):
+ f()
+def g8():
+ for i in range(8):
+ f()
+
+# run in parallel
+@test_parallel(num_threads=2)
+def pg2():
+ f()
+
+@test_parallel(num_threads=4)
+def pg4():
+ f()
+
+@test_parallel(num_threads=8)
+def pg8():
+ f()
+
+"""
+
+nogil_groupby_sum_4 = Benchmark(
+ 'pg4()', setup,
+ start_date=datetime(2015, 1, 1))
+
+nogil_groupby_sum_8 = Benchmark(
+ 'pg8()', setup,
+ start_date=datetime(2015, 1, 1))
+
+
+#### test all groupby funcs ####
+
+setup = basic + """
+
+@test_parallel(num_threads=2)
+def pg2():
+ df.groupby('key')['data'].func()
+
+"""
+
+for f in ['sum','prod','var','count','min','max','mean','last']:
+
+ name = "nogil_groupby_{f}_2".format(f=f)
+ bmark = Benchmark('pg2()', setup.replace('func',f), start_date=datetime(2015, 1, 1))
+ bmark.name = name
+ globals()[name] = bmark
+
+del bmark
+
+
+#### test take_1d ####
+setup = basic + """
+from pandas.core import common as com
+
+N = 1e7
+df = DataFrame({'int64' : np.arange(N,dtype='int64'),
+ 'float64' : np.arange(N,dtype='float64')})
+indexer = np.arange(100,len(df)-100)
+
+@test_parallel(num_threads=2)
+def take_1d_pg2_int64():
+ com.take_1d(df.int64.values,indexer)
+
+@test_parallel(num_threads=2)
+def take_1d_pg2_float64():
+ com.take_1d(df.float64.values,indexer)
+
+"""
+
+nogil_take1d_float64 = Benchmark('take_1d_pg2()_int64', setup, start_date=datetime(2015, 1, 1))
+nogil_take1d_int64 = Benchmark('take_1d_pg2()_float64', setup, start_date=datetime(2015, 1, 1))
diff --git a/vb_suite/suite.py b/vb_suite/suite.py
index a16d183ae62e2..ca7a4a9b70836 100644
--- a/vb_suite/suite.py
+++ b/vb_suite/suite.py
@@ -16,6 +16,7 @@
'inference',
'hdfstore_bench',
'join_merge',
+ 'gil',
'miscellaneous',
'panel_ctor',
'packers',
| closes #8882
closes #10139
This is now implemented for all groupbys with the factorization part as well (which is actually a big part fo the time)
```
In [2]: N = 1000000
In [3]: ngroups = 1000
In [4]: np.random.seed(1234)
In [5]: df = DataFrame({'key' : np.random.randint(0,ngroups,size=N),
...: 'data' : np.random.randn(N) })
```
```
def f():
df.groupby('key')['data'].sum()
# run consecutivily
def g2():
for i in range(2):
f()
def g4():
for i in range(4):
f()
def g8():
for i in range(8):
f()
# run in parallel
@test_parallel(num_threads=2)
def pg2():
f()
@test_parallel(num_threads=4)
def pg4():
f()
@test_parallel(num_threads=8)
def pg8():
f()
```
So seeing a nice curve as far as addtl cores are used (as compared to some of my previous posts).
```
In [19]: df
Out[19]:
after before speedup
2 27.3 52.1 1.908425
4 44.8 105.0 2.343750
8 83.2 213.0 2.560096
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10199 | 2015-05-22T20:19:47Z | 2015-06-30T10:55:04Z | 2015-06-30T10:55:03Z | 2015-06-30T16:58:13Z |
BUG: Raise TypeError only if key DataFrame is not empty #10126 | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index b571aab0b19a5..b409ea89a8032 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -83,3 +83,5 @@ Bug Fixes
- Bug where infer_freq infers timerule (WOM-5XXX) unsupported by to_offset (:issue:`9425`)
+
+- Bug to handle masking empty ``DataFrame``(:issue:`10126`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index f36108262432d..ab6f11a4b8d5b 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2151,7 +2151,7 @@ def _setitem_array(self, key, value):
def _setitem_frame(self, key, value):
# support boolean setting with DataFrame input, e.g.
# df[df > df2] = 0
- if key.values.dtype != np.bool_:
+ if key.values.size and not com.is_bool_dtype(key.values):
raise TypeError('Must pass DataFrame with boolean values only')
self._check_inplace_setting(value)
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 4964d13f7ac28..f74cb07557342 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -794,6 +794,19 @@ def test_setitem_empty(self):
result.loc[result.b.isnull(), 'a'] = result.a
assert_frame_equal(result, df)
+ def test_setitem_empty_frame_with_boolean(self):
+ # Test for issue #10126
+
+ for dtype in ('float', 'int64'):
+ for df in [
+ pd.DataFrame(dtype=dtype),
+ pd.DataFrame(dtype=dtype, index=[1]),
+ pd.DataFrame(dtype=dtype, columns=['A']),
+ ]:
+ df2 = df.copy()
+ df[df > df2] = 47
+ assert_frame_equal(df, df2)
+
def test_delitem_corner(self):
f = self.frame.copy()
del f['D']
@@ -2821,7 +2834,7 @@ def custom_frame_function(self):
data = {'col1': range(10),
'col2': range(10)}
cdf = CustomDataFrame(data)
-
+
# Did we get back our own DF class?
self.assertTrue(isinstance(cdf, CustomDataFrame))
@@ -2833,7 +2846,7 @@ def custom_frame_function(self):
# Do we get back our own DF class after slicing row-wise?
cdf_rows = cdf[1:5]
self.assertTrue(isinstance(cdf_rows, CustomDataFrame))
- self.assertEqual(cdf_rows.custom_frame_function(), 'OK')
+ self.assertEqual(cdf_rows.custom_frame_function(), 'OK')
# Make sure sliced part of multi-index frame is custom class
mcol = pd.MultiIndex.from_tuples([('A', 'A'), ('A', 'B')])
| Proposed fix for #10126
| https://api.github.com/repos/pandas-dev/pandas/pulls/10196 | 2015-05-22T10:46:45Z | 2015-06-03T19:02:45Z | 2015-06-03T19:02:45Z | 2015-06-04T11:17:25Z |
DOC/CLN: period_range kwarg descriptions missing and other minor doc … | diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 4b7d8b9796f01..ffc3e6a08221c 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -187,7 +187,7 @@ class Grouper(object):
Examples
--------
- >>> df.groupby(Grouper(key='A')) : syntatic sugar for df.groupby('A')
+ >>> df.groupby(Grouper(key='A')) : syntactic sugar for df.groupby('A')
>>> df.groupby(Grouper(key='date',freq='60s')) : specify a resample on the column 'date'
>>> df.groupby(Grouper(level='date',freq='60s',axis=1)) :
specify a resample on the level 'date' on the columns axis with a frequency of 60s
diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py
index 98d9f9f14d3da..510887a185054 100644
--- a/pandas/tseries/period.py
+++ b/pandas/tseries/period.py
@@ -110,20 +110,20 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index):
Parameters
----------
- data : array-like (1-dimensional), optional
+ data : array-like (1-dimensional), optional
Optional period-like data to construct index with
dtype : NumPy dtype (default: i8)
- copy : bool
+ copy : bool
Make a copy of input ndarray
freq : string or period object, optional
One of pandas period strings or corresponding objects
start : starting value, period-like, optional
If data is None, used as the start point in generating regular
period data.
- periods : int, optional, > 0
+ periods : int, optional, > 0
Number of periods to generate, if generating index. Takes precedence
over end argument
- end : end value, period-like, optional
+ end : end value, period-like, optional
If periods is none, generated index will extend to first conforming
period on or just past end argument
year : int, array, or Series, default None
@@ -501,7 +501,6 @@ def shift(self, n):
----------
n : int
Periods to shift by
- freq : freq string
Returns
-------
@@ -970,8 +969,8 @@ def period_range(start=None, end=None, periods=None, freq='D', name=None):
Parameters
----------
- start :
- end :
+ start : starting value, period-like, optional
+ end : ending value, period-like, optional
periods : int, default None
Number of periods in the index
freq : str/DateOffset, default 'D'
| …fixes
| https://api.github.com/repos/pandas-dev/pandas/pulls/10189 | 2015-05-21T18:13:28Z | 2015-05-23T06:40:55Z | 2015-05-23T06:40:55Z | 2015-06-02T19:26:12Z |
BUG: Adding empty dataframes should result in empty blocks #10181 | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index 3665948d15271..48fce87f5088d 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -41,6 +41,8 @@ Other API Changes
- ``Holiday`` now raises ``NotImplementedError`` if both ``offset`` and ``observance`` are used in constructor instead of returning an incorrect result (:issue:`10217`).
+- Adding empty ``DataFrame``s results in a ``DataFrame`` that ``.equals`` an empty ``DataFrame`` (:issue:`10181`)
+
.. _whatsnew_0162.performance:
Performance Improvements
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 3395ea360165e..8ff39f4fb0e06 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -3530,11 +3530,15 @@ def construction_error(tot_items, block_shape, axes, e=None):
def create_block_manager_from_blocks(blocks, axes):
try:
if len(blocks) == 1 and not isinstance(blocks[0], Block):
- # It's OK if a single block is passed as values, its placement is
- # basically "all items", but if there're many, don't bother
- # converting, it's an error anyway.
- blocks = [make_block(values=blocks[0],
- placement=slice(0, len(axes[0])))]
+ # if blocks[0] is of length 0, return empty blocks
+ if not len(blocks[0]):
+ blocks = []
+ else:
+ # It's OK if a single block is passed as values, its placement is
+ # basically "all items", but if there're many, don't bother
+ # converting, it's an error anyway.
+ blocks = [make_block(values=blocks[0],
+ placement=slice(0, len(axes[0])))]
mgr = BlockManager(blocks, axes)
mgr._consolidate_inplace()
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index f74cb07557342..a6fafb445925b 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -5142,6 +5142,17 @@ def test_operators(self):
df = DataFrame({'a': ['a', None, 'b']})
assert_frame_equal(df + df, DataFrame({'a': ['aa', np.nan, 'bb']}))
+ # Test for issue #10181
+ for dtype in ('float', 'int64'):
+ frames = [
+ DataFrame(dtype=dtype),
+ DataFrame(columns=['A'], dtype=dtype),
+ DataFrame(index=[0], dtype=dtype),
+ ]
+ for df in frames:
+ self.assertTrue((df + df).equals(df))
+ assert_frame_equal(df + df, df)
+
def test_ops_np_scalar(self):
vals, xs = np.random.rand(5, 3), [nan, 7, -23, 2.718, -3.14, np.inf]
f = lambda x: DataFrame(x, index=list('ABCDE'),
| Fixes #10181
I added a couple of lines to the dataframe test `test_operators` to assert that adding two empty dataframes returns a dataframe that is equal (both using `assert_frame_equal` as well as `.equals`) to an empty dataframe.
I couldn't get vbench to install (I am running on windows) - the error was:
```
c:\dev\code\opensource\pandas-rekcahpassyla>pip install git+https://github.com/pydata/vbench
You are using pip version 6.0.8, however version 6.1.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Collecting git+https://github.com/pydata/vbench
Cloning https://github.com/pydata/vbench to c:\users\redacted\appdata\local\temp\pip-ruiahg-build
fatal: unable to connect to github.com:
github.com[0: 192.30.252.129]: errno=No error
Clone of 'git://github.com/yarikoptic/vbenchtest.git' into submodule path 'vbench/tests/vbenchtest/vbenchtest' failed
Complete output from command C:\dev\bin\git\cmd\git.exe submodule update --init --recursive -q:
----------------------------------------
Command "C:\dev\bin\git\cmd\git.exe submodule update --init --recursive -q" failed with error code 1 in c:\users\redacted\appdata\local\temp\pip-ruiahg-build
```
The test suite failed for me in master with 1 failure. The same failure, but no others, were observed when running in my branch.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10188 | 2015-05-21T16:08:46Z | 2015-06-07T22:22:40Z | 2015-06-07T22:22:40Z | 2015-06-16T15:40:21Z |
DOC: Add Index.difference to API doc | diff --git a/doc/source/api.rst b/doc/source/api.rst
index 3f47c0380116c..3b2e8b65768bb 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -1262,8 +1262,6 @@ Modifying and Computations
Index.argmax
Index.copy
Index.delete
- Index.diff
- Index.sym_diff
Index.drop
Index.drop_duplicates
Index.duplicated
@@ -1309,15 +1307,17 @@ Time-specific operations
Index.shift
-Combining / joining / merging
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Combining / joining / set operations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated/
Index.append
- Index.intersection
Index.join
+ Index.intersection
Index.union
+ Index.difference
+ Index.sym_diff
Selecting
~~~~~~~~~
| Add `Index.difference` to api doc rather than deprecated `Index.diff` (#8226)
And is it OK to remove `Index.diff` alias because #6581 lists #8227 (which closes #8226)?
| https://api.github.com/repos/pandas-dev/pandas/pulls/10185 | 2015-05-21T13:51:22Z | 2015-05-23T06:41:18Z | 2015-05-23T06:41:18Z | 2015-06-02T19:26:12Z |
FIX printing index with display.max_seq_items=None (GH10182) | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index c219818a62631..ba7d1b80c7f8a 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -137,6 +137,7 @@ Bug Fixes
- Bug in ``Timestamp``'s' ``microsecond``, ``quarter``, ``dayofyear``, ``week`` and ``daysinmonth`` properties return ``np.int`` type, not built-in ``int``. (:issue:`10050`)
- Bug in ``NaT`` raises ``AttributeError`` when accessing to ``daysinmonth``, ``dayofweek`` properties. (:issue:`10096`)
+- Bug in Index repr when using the ``max_seq_items=None`` setting (:issue:`10182`).
- Bug in getting timezone data with ``dateutil`` on various platforms ( :issue:`9059`, :issue:`8639`, :issue:`9663`, :issue:`10121`)
- Bug in display datetimes with mixed frequencies uniformly; display 'ms' datetimes to the proper precision. (:issue:`10170`)
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py
index c5cd8390359dc..05886fb5a54d4 100644
--- a/pandas/core/categorical.py
+++ b/pandas/core/categorical.py
@@ -1599,6 +1599,20 @@ def describe(self):
return result
+ def repeat(self, repeats):
+ """
+ Repeat elements of a Categorical.
+
+ See also
+ --------
+ numpy.ndarray.repeat
+
+ """
+ codes = self._codes.repeat(repeats)
+ return Categorical(values=codes, categories=self.categories,
+ ordered=self.ordered, name=self.name, fastpath=True)
+
+
##### The Series.cat accessor #####
class CategoricalAccessor(PandasDelegate):
diff --git a/pandas/core/index.py b/pandas/core/index.py
index 1483ca9a47b46..c558f101df0a2 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -443,7 +443,7 @@ def _format_data(self):
n = len(self)
sep = ','
- max_seq_items = get_option('display.max_seq_items')
+ max_seq_items = get_option('display.max_seq_items') or n
formatter = self._formatter_func
# do we want to justify (only do so for non-objects)
@@ -534,7 +534,7 @@ def _format_attrs(self):
attrs.append(('dtype',"'%s'" % self.dtype))
if self.name is not None:
attrs.append(('name',default_pprint(self.name)))
- max_seq_items = get_option('display.max_seq_items')
+ max_seq_items = get_option('display.max_seq_items') or len(self)
if len(self) > max_seq_items:
attrs.append(('length',len(self)))
return attrs
@@ -2950,7 +2950,7 @@ def _format_attrs(self):
if self.name is not None:
attrs.append(('name',default_pprint(self.name)))
attrs.append(('dtype',"'%s'" % self.dtype))
- max_seq_items = get_option('display.max_seq_items')
+ max_seq_items = get_option('display.max_seq_items') or len(self)
if len(self) > max_seq_items:
attrs.append(('length',len(self)))
return attrs
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index bec688db99114..49efdff139925 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -2714,7 +2714,6 @@ def f():
df.append(df_wrong_categories)
self.assertRaises(ValueError, f)
-
def test_merge(self):
# GH 9426
@@ -2747,6 +2746,13 @@ def test_merge(self):
result = pd.merge(cleft, cright, how='left', left_on='b', right_on='c')
tm.assert_frame_equal(result, expected)
+ def test_repeat(self):
+ #GH10183
+ cat = pd.Categorical(["a","b"], categories=["a","b"])
+ exp = pd.Categorical(["a", "a", "b", "b"], categories=["a","b"])
+ res = cat.repeat(2)
+ self.assert_categorical_equal(res, exp)
+
def test_na_actions(self):
cat = pd.Categorical([1,2,3,np.nan], categories=[1,2,3])
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index ac071d6d39e57..f422c3b49b691 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -133,6 +133,14 @@ def test_str(self):
self.assertTrue("'foo'" in str(idx))
self.assertTrue(idx.__class__.__name__ in str(idx))
+ def test_repr_max_seq_item_setting(self):
+ # GH10182
+ idx = self.create_index()
+ idx = idx.repeat(50)
+ with pd.option_context("display.max_seq_items", None):
+ repr(idx)
+ self.assertFalse('...' in str(idx))
+
def test_wrong_number_names(self):
def testit(ind):
ind.names = ["apple", "banana", "carrot"]
@@ -2857,6 +2865,14 @@ def test_get_indexer(self):
with self.assertRaisesRegexp(ValueError, 'different freq'):
idx.asfreq('D').get_indexer(idx)
+ def test_repeat(self):
+ # GH10183
+ idx = pd.period_range('2000-01-01', periods=3, freq='D')
+ res = idx.repeat(3)
+ exp = PeriodIndex(idx.values.repeat(3), freq='D')
+ self.assert_index_equal(res, exp)
+ self.assertEqual(res.freqstr, 'D')
+
class TestTimedeltaIndex(DatetimeLike, tm.TestCase):
_holder = TimedeltaIndex
diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py
index 6627047f0c335..95bbc5016237c 100644
--- a/pandas/tseries/period.py
+++ b/pandas/tseries/period.py
@@ -783,6 +783,17 @@ def append(self, other):
for x in to_concat]
return Index(com._concat_compat(to_concat), name=name)
+ def repeat(self, n):
+ """
+ Return a new Index of the values repeated n times.
+
+ See also
+ --------
+ numpy.ndarray.repeat
+ """
+ # overwrites method from DatetimeIndexOpsMixin
+ return self._shallow_copy(self.values.repeat(n))
+
def __setstate__(self, state):
"""Necessary for making this object picklable"""
| Fixes #10182
| https://api.github.com/repos/pandas-dev/pandas/pulls/10183 | 2015-05-21T11:20:38Z | 2015-06-10T06:47:12Z | 2015-06-10T06:47:12Z | 2015-06-10T06:47:12Z |
BUG: concat on axis=0 with categorical (GH10177) | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 02ef2bbed19b6..14e185b5b2a26 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -103,4 +103,4 @@ Bug Fixes
- Bug that caused segfault when resampling an empty Series (:issue:`10228`)
- Bug in ``DatetimeIndex`` and ``PeriodIndex.value_counts`` resets name from its result, but retains in result's ``Index``. (:issue:`10150`)
-
+- Bug in `pandas.concat` with ``axis=0`` when column is of dtype ``category`` (:issue:`10177`)
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 4c4d940f8077c..42d7163e7f741 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -4133,7 +4133,7 @@ def get_empty_dtype_and_na(join_units):
else:
return np.dtype(np.bool_), None
elif 'category' in upcast_classes:
- return com.CategoricalDtype(), np.nan
+ return np.dtype(np.object_), np.nan
elif 'float' in upcast_classes:
return np.dtype(np.float64), np.nan
elif 'datetime' in upcast_classes:
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index beff41fd9d109..63b913f59f18a 100755
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -2967,6 +2967,24 @@ def test_pickle_v0_15_2(self):
#
self.assert_categorical_equal(cat, pd.read_pickle(pickle_path))
+ def test_concat_categorical(self):
+ # See GH 10177
+ df1 = pd.DataFrame(np.arange(18).reshape(6, 3), columns=["a", "b", "c"])
+
+ df2 = pd.DataFrame(np.arange(14).reshape(7, 2), columns=["a", "c"])
+ df2['h'] = pd.Series(pd.Categorical(["one", "one", "two", "one", "two", "two", "one"]))
+
+ df_concat = pd.concat((df1, df2), axis=0).reset_index(drop=True)
+
+ df_expected = pd.DataFrame({'a': [0, 3, 6, 9, 12, 15, 0, 2, 4, 6, 8, 10, 12],
+ 'b': [1, 4, 7, 10, 13, 16, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+ 'c': [2, 5, 8, 11, 14, 17, 1, 3, 5, 7, 9, 11, 13]})
+ df_expected['h'] = pd.Series(pd.Categorical([None, None, None, None, None, None,
+ "one", "one", "two", "one", "two", "two", "one"]))
+
+ tm.assert_frame_equal(df_expected, df_concat)
+
+
if __name__ == '__main__':
import nose
| Contains a proposed fix for #10177
| https://api.github.com/repos/pandas-dev/pandas/pulls/10179 | 2015-05-20T15:34:57Z | 2015-06-27T22:04:22Z | 2015-06-27T22:04:22Z | 2015-06-27T22:04:27Z |
BUG: mean overflows for integer dtypes (fixes #10155) | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 1cff74d41f686..6f04b0358394f 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -63,6 +63,7 @@ Bug Fixes
- Bug in ``Categorical`` repr with ``display.width`` of ``None`` in Python 3 (:issue:`10087`)
+- Bug in ``mean()`` where integer dtypes can overflow (:issue:`10172`)
- Bug where Panel.from_dict does not set dtype when specified (:issue:`10058`)
- Bug in ``Timestamp``'s' ``microsecond``, ``quarter``, ``dayofyear``, ``week`` and ``daysinmonth`` properties return ``np.int`` type, not built-in ``int``. (:issue:`10050`)
- Bug in ``NaT`` raises ``AttributeError`` when accessing to ``daysinmonth``, ``dayofweek`` properties. (:issue:`10096`)
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index e921a9d562bc1..0df160618b7c3 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -20,7 +20,7 @@
is_complex_dtype, is_integer_dtype,
is_bool_dtype, is_object_dtype,
is_datetime64_dtype, is_timedelta64_dtype,
- is_datetime_or_timedelta_dtype,
+ is_datetime_or_timedelta_dtype, _get_dtype,
is_int_or_datetime_dtype, is_any_int_dtype)
@@ -254,8 +254,16 @@ def nansum(values, axis=None, skipna=True):
@bottleneck_switch()
def nanmean(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
- the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_max))
- count = _get_counts(mask, axis)
+
+ dtype_sum = dtype_max
+ dtype_count = np.float64
+ if is_integer_dtype(dtype):
+ dtype_sum = np.float64
+ elif is_float_dtype(dtype):
+ dtype_sum = dtype
+ dtype_count = dtype
+ count = _get_counts(mask, axis, dtype=dtype_count)
+ the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
if axis is not None and getattr(the_sum, 'ndim', False):
the_mean = the_sum / count
@@ -557,15 +565,16 @@ def _maybe_arg_null_out(result, axis, mask, skipna):
return result
-def _get_counts(mask, axis):
+def _get_counts(mask, axis, dtype=float):
+ dtype = _get_dtype(dtype)
if axis is None:
- return float(mask.size - mask.sum())
+ return dtype.type(mask.size - mask.sum())
count = mask.shape[axis] - mask.sum(axis)
try:
- return count.astype(float)
+ return count.astype(dtype)
except AttributeError:
- return np.array(count, dtype=float)
+ return np.array(count, dtype=dtype)
def _maybe_null_out(result, axis, mask):
diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index 2a605cba8a6c0..1adb8a5d9217c 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -5,7 +5,7 @@
import numpy as np
-from pandas.core.common import isnull
+from pandas.core.common import isnull, is_integer_dtype
import pandas.core.nanops as nanops
import pandas.util.testing as tm
@@ -323,6 +323,32 @@ def test_nanmean(self):
allow_complex=False, allow_obj=False,
allow_str=False, allow_date=False, allow_tdelta=True)
+ def test_nanmean_overflow(self):
+ # GH 10155
+ # In the previous implementation mean can overflow for int dtypes, it
+ # is now consistent with numpy
+ from pandas import Series
+
+ # numpy < 1.9.0 is not computing this correctly
+ from distutils.version import LooseVersion
+ if LooseVersion(np.__version__) >= '1.9.0':
+ for a in [2 ** 55, -2 ** 55, 20150515061816532]:
+ s = Series(a, index=range(500), dtype=np.int64)
+ result = s.mean()
+ np_result = s.values.mean()
+ self.assertEqual(result, a)
+ self.assertEqual(result, np_result)
+ self.assertTrue(result.dtype == np.float64)
+
+ # check returned dtype
+ for dtype in [np.int16, np.int32, np.int64, np.float16, np.float32, np.float64]:
+ s = Series(range(10), dtype=dtype)
+ result = s.mean()
+ if is_integer_dtype(dtype):
+ self.assertTrue(result.dtype == np.float64)
+ else:
+ self.assertTrue(result.dtype == dtype)
+
def test_nanmedian(self):
self.check_funs(nanops.nanmedian, np.median,
allow_complex=False, allow_str=False, allow_date=False,
| closes #10155
| https://api.github.com/repos/pandas-dev/pandas/pulls/10172 | 2015-05-19T16:43:57Z | 2015-05-30T20:41:19Z | 2015-05-30T20:41:19Z | 2015-06-02T19:26:12Z |
BUG: consistent datetime display format with < ms #10170 | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index b835733db6f00..82589a5500505 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -68,7 +68,7 @@ Bug Fixes
- Bug in ``NaT`` raises ``AttributeError`` when accessing to ``daysinmonth``, ``dayofweek`` properties. (:issue:`10096`)
- Bug in getting timezone data with ``dateutil`` on various platforms ( :issue:`9059`, :issue:`8639`, :issue:`9663`, :issue:`10121`)
-
+- Bug in display datetimes with mixed frequencies uniformly; display 'ms' datetimes to the proper precision. (:issue:`10170`)
- Bug in ``DatetimeIndex`` and ``TimedeltaIndex`` names are lost after timedelta arithmetics ( :issue:`9926`)
diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py
index fd9d9546ba235..a7129bca59a7f 100644
--- a/pandas/tests/test_format.py
+++ b/pandas/tests/test_format.py
@@ -14,7 +14,7 @@
from numpy.random import randn
import numpy as np
-from pandas import DataFrame, Series, Index, Timestamp, MultiIndex
+from pandas import DataFrame, Series, Index, Timestamp, MultiIndex, date_range, NaT
import pandas.core.format as fmt
import pandas.util.testing as tm
@@ -2495,7 +2495,7 @@ def test_to_string(self):
def test_freq_name_separation(self):
s = Series(np.random.randn(10),
- index=pd.date_range('1/1/2000', periods=10), name=0)
+ index=date_range('1/1/2000', periods=10), name=0)
result = repr(s)
self.assertTrue('Freq: D, Name: 0' in result)
@@ -2556,7 +2556,6 @@ def test_float_trim_zeros(self):
def test_datetimeindex(self):
- from pandas import date_range, NaT
index = date_range('20130102',periods=6)
s = Series(1,index=index)
result = s.to_string()
@@ -2574,7 +2573,6 @@ def test_datetimeindex(self):
def test_timedelta64(self):
- from pandas import date_range
from datetime import datetime, timedelta
Series(np.array([1100, 20], dtype='timedelta64[ns]')).to_string()
@@ -3179,6 +3177,44 @@ def test_date_nanos(self):
result = fmt.Datetime64Formatter(x).get_result()
self.assertEqual(result[0].strip(), "1970-01-01 00:00:00.000000200")
+ def test_dates_display(self):
+
+ # 10170
+ # make sure that we are consistently display date formatting
+ x = Series(date_range('20130101 09:00:00',periods=5,freq='D'))
+ x.iloc[1] = np.nan
+ result = fmt.Datetime64Formatter(x).get_result()
+ self.assertEqual(result[0].strip(), "2013-01-01 09:00:00")
+ self.assertEqual(result[1].strip(), "NaT")
+ self.assertEqual(result[4].strip(), "2013-01-05 09:00:00")
+
+ x = Series(date_range('20130101 09:00:00',periods=5,freq='s'))
+ x.iloc[1] = np.nan
+ result = fmt.Datetime64Formatter(x).get_result()
+ self.assertEqual(result[0].strip(), "2013-01-01 09:00:00")
+ self.assertEqual(result[1].strip(), "NaT")
+ self.assertEqual(result[4].strip(), "2013-01-01 09:00:04")
+
+ x = Series(date_range('20130101 09:00:00',periods=5,freq='ms'))
+ x.iloc[1] = np.nan
+ result = fmt.Datetime64Formatter(x).get_result()
+ self.assertEqual(result[0].strip(), "2013-01-01 09:00:00.000")
+ self.assertEqual(result[1].strip(), "NaT")
+ self.assertEqual(result[4].strip(), "2013-01-01 09:00:00.004")
+
+ x = Series(date_range('20130101 09:00:00',periods=5,freq='us'))
+ x.iloc[1] = np.nan
+ result = fmt.Datetime64Formatter(x).get_result()
+ self.assertEqual(result[0].strip(), "2013-01-01 09:00:00.000000")
+ self.assertEqual(result[1].strip(), "NaT")
+ self.assertEqual(result[4].strip(), "2013-01-01 09:00:00.000004")
+
+ x = Series(date_range('20130101 09:00:00',periods=5,freq='N'))
+ x.iloc[1] = np.nan
+ result = fmt.Datetime64Formatter(x).get_result()
+ self.assertEqual(result[0].strip(), "2013-01-01 09:00:00.000000000")
+ self.assertEqual(result[1].strip(), "NaT")
+ self.assertEqual(result[4].strip(), "2013-01-01 09:00:00.000000004")
class TestNaTFormatting(tm.TestCase):
def test_repr(self):
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index 6c20b02324688..8412ba8d4aad1 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -791,7 +791,7 @@ def test_series_repr_nat(self):
series = Series([0, 1000, 2000, iNaT], dtype='M8[ns]')
result = repr(series)
- expected = ('0 1970-01-01 00:00:00\n'
+ expected = ('0 1970-01-01 00:00:00.000000\n'
'1 1970-01-01 00:00:00.000001\n'
'2 1970-01-01 00:00:00.000002\n'
'3 NaT\n'
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 2b45718d1f9ea..59eb432844ee3 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -1418,6 +1418,8 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None, object f
"""
cdef:
int64_t val, ns, N = len(values)
+ ndarray[int64_t] consider_values
+ bint show_ms = 0, show_us = 0, show_ns = 0, basic_format = 0
ndarray[object] result = np.empty(N, dtype=object)
object ts, res
pandas_datetimestruct dts
@@ -1425,13 +1427,27 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None, object f
if na_rep is None:
na_rep = 'NaT'
+ # if we don't have a format nor tz, then choose
+ # a format based on precision
+ basic_format = format is None and tz is None
+ if basic_format:
+ consider_values = values[values != iNaT]
+ show_ns = (consider_values%1000).any()
+
+ if not show_ns:
+ consider_values //= 1000
+ show_us = (consider_values%1000).any()
+
+ if not show_ms:
+ consider_values //= 1000
+ show_ms = (consider_values%1000).any()
+
for i in range(N):
- val = values[i]
+ val = values[i]
- if val == iNaT:
- result[i] = na_rep
- else:
- if format is None and tz is None:
+ if val == iNaT:
+ result[i] = na_rep
+ elif basic_format:
pandas_datetime_to_datetimestruct(val, PANDAS_FR_ns, &dts)
res = '%d-%.2d-%.2d %.2d:%.2d:%.2d' % (dts.year,
@@ -1441,27 +1457,29 @@ def format_array_from_datetime(ndarray[int64_t] values, object tz=None, object f
dts.min,
dts.sec)
- ns = dts.ps / 1000
-
- if ns != 0:
- res += '.%.9d' % (ns + 1000 * dts.us)
- elif dts.us != 0:
- res += '.%.6d' % dts.us
+ if show_ns:
+ ns = dts.ps / 1000
+ res += '.%.9d' % (ns + 1000 * dts.us)
+ elif show_us:
+ res += '.%.6d' % dts.us
+ elif show_ms:
+ res += '.%.3d' % (dts.us/1000)
result[i] = res
- else:
- ts = Timestamp(val, tz=tz)
- if format is None:
- result[i] = str(ts)
- else:
-
- # invalid format string
- # requires dates > 1900
- try:
- result[i] = ts.strftime(format)
- except ValueError:
- result[i] = str(ts)
+ else:
+
+ ts = Timestamp(val, tz=tz)
+ if format is None:
+ result[i] = str(ts)
+ else:
+
+ # invalid format string
+ # requires dates > 1900
+ try:
+ result[i] = ts.strftime(format)
+ except ValueError:
+ result[i] = str(ts)
return result
| closes #10170
```
In [3]: Series(date_range('20130101 09:00:00',periods=5,freq='D'))
Out[3]:
0 2013-01-01 09:00:00
1 2013-01-02 09:00:00
2 2013-01-03 09:00:00
3 2013-01-04 09:00:00
4 2013-01-05 09:00:00
dtype: datetime64[ns]
In [4]: Series(date_range('20130101 09:00:00',periods=5,freq='s'))
Out[4]:
0 2013-01-01 09:00:00
1 2013-01-01 09:00:01
2 2013-01-01 09:00:02
3 2013-01-01 09:00:03
4 2013-01-01 09:00:04
dtype: datetime64[ns]
In [2]: s = Series(date_range('20130101 09:00:00',periods=5,freq='ms'))
In [3]: s.iloc[1] = np.nan
In [4]: s
Out[4]:
0 2013-01-01 09:00:00.000
1 NaT
2 2013-01-01 09:00:00.002
3 2013-01-01 09:00:00.003
4 2013-01-01 09:00:00.004
dtype: datetime64[ns]
In [6]: Series(date_range('20130101 09:00:00',periods=5,freq='us'))
Out[6]:
0 2013-01-01 09:00:00.000000
1 2013-01-01 09:00:00.000001
2 2013-01-01 09:00:00.000002
3 2013-01-01 09:00:00.000003
4 2013-01-01 09:00:00.000004
dtype: datetime64[ns]
In [7]: Series(date_range('20130101 09:00:00',periods=5,freq='N'))
Out[7]:
0 2013-01-01 09:00:00.000000000
1 2013-01-01 09:00:00.000000001
2 2013-01-01 09:00:00.000000002
3 2013-01-01 09:00:00.000000003
4 2013-01-01 09:00:00.000000004
dtype: datetime64[ns]
```
| https://api.github.com/repos/pandas-dev/pandas/pulls/10171 | 2015-05-19T15:46:04Z | 2015-05-20T22:02:19Z | 2015-05-20T22:02:19Z | 2015-06-02T19:26:12Z |
DOC: Reorder arguments in shared fillna docstring | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index a560dd4c00be7..b747f0a2ceacb 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -2398,15 +2398,15 @@ def convert_objects(self, convert_dates=True, convert_numeric=False,
Parameters
----------
- method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
- Method to use for filling holes in reindexed Series
- pad / ffill: propagate last valid observation forward to next valid
- backfill / bfill: use NEXT valid observation to fill gap
value : scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of
values specifying which value to use for each index (for a Series) or
column (for a DataFrame). (values not in the dict/Series/DataFrame will not be
filled). This value cannot be a list.
+ method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+ Method to use for filling holes in reindexed Series
+ pad / ffill: propagate last valid observation forward to next valid
+ backfill / bfill: use NEXT valid observation to fill gap
axis : %(axes_single_arg)s
inplace : boolean, default False
If True, fill in place. Note: this will modify any
| The docstring listed 'method' before 'value' which is not consistent
with the order of the arguments when calling the method.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10166 | 2015-05-18T19:43:07Z | 2015-05-18T20:30:57Z | 2015-05-18T20:30:57Z | 2015-05-18T20:31:00Z |
DOC: clarify purpose of DataFrame.from_csv (GH4191) | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index d8948bc82fe61..654d6c7fd3436 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -180,7 +180,6 @@ class DataFrame(NDFrame):
--------
DataFrame.from_records : constructor from tuples, also record arrays
DataFrame.from_dict : from dicts of Series, arrays, or dicts
- DataFrame.from_csv : from CSV files
DataFrame.from_items : from sequence of (key, value) pairs
pandas.read_csv, pandas.read_table, pandas.read_clipboard
"""
@@ -1052,13 +1051,29 @@ def from_csv(cls, path, header=0, sep=',', index_col=0,
parse_dates=True, encoding=None, tupleize_cols=False,
infer_datetime_format=False):
"""
- Read delimited file into DataFrame
+ Read CSV file (DISCOURAGED, please use :func:`pandas.read_csv` instead).
+
+ It is preferable to use the more powerful :func:`pandas.read_csv`
+ for most general purposes, but ``from_csv`` makes for an easy
+ roundtrip to and from a file (the exact counterpart of
+ ``to_csv``), especially with a DataFrame of time series data.
+
+ This method only differs from the preferred :func:`pandas.read_csv`
+ in some defaults:
+
+ - `index_col` is ``0`` instead of ``None`` (take first column as index
+ by default)
+ - `parse_dates` is ``True`` instead of ``False`` (try parsing the index
+ as datetime by default)
+
+ So a ``pd.DataFrame.from_csv(path)`` can be replaced by
+ ``pd.read_csv(path, index_col=0, parse_dates=True)``.
Parameters
----------
path : string file path or file handle / StringIO
header : int, default 0
- Row to use at header (skip prior rows)
+ Row to use as header (skip prior rows)
sep : string, default ','
Field delimiter
index_col : int or sequence, default 0
@@ -1074,15 +1089,14 @@ def from_csv(cls, path, header=0, sep=',', index_col=0,
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
- Notes
- -----
- Preferable to use read_table for most general purposes but from_csv
- makes for an easy roundtrip to and from file, especially with a
- DataFrame of time series data
+ See also
+ --------
+ pandas.read_csv
Returns
-------
y : DataFrame
+
"""
from pandas.io.parsers import read_table
return read_table(path, header=header, sep=sep,
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 6586fa10935e6..0aaa142533950 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2313,7 +2313,24 @@ def between(self, left, right, inclusive=True):
def from_csv(cls, path, sep=',', parse_dates=True, header=None,
index_col=0, encoding=None, infer_datetime_format=False):
"""
- Read delimited file into Series
+ Read CSV file (DISCOURAGED, please use :func:`pandas.read_csv` instead).
+
+ It is preferable to use the more powerful :func:`pandas.read_csv`
+ for most general purposes, but ``from_csv`` makes for an easy
+ roundtrip to and from a file (the exact counterpart of
+ ``to_csv``), especially with a time Series.
+
+ This method only differs from :func:`pandas.read_csv` in some defaults:
+
+ - `index_col` is ``0`` instead of ``None`` (take first column as index
+ by default)
+ - `header` is ``None`` instead of ``0`` (the first row is not used as
+ the column names)
+ - `parse_dates` is ``True`` instead of ``False`` (try parsing the index
+ as datetime by default)
+
+ With :func:`pandas.read_csv`, the option ``squeeze=True`` can be used
+ to return a Series like ``from_csv``.
Parameters
----------
@@ -2322,8 +2339,8 @@ def from_csv(cls, path, sep=',', parse_dates=True, header=None,
Field delimiter
parse_dates : boolean, default True
Parse dates. Different default from read_table
- header : int, default 0
- Row to use at header (skip prior rows)
+ header : int, default None
+ Row to use as header (skip prior rows)
index_col : int or sequence, default 0
Column to use for index. If a sequence is given, a MultiIndex
is used. Different default from read_table
@@ -2335,6 +2352,10 @@ def from_csv(cls, path, sep=',', parse_dates=True, header=None,
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
+ See also
+ --------
+ pandas.read_csv
+
Returns
-------
y : Series
| Closes #9556
xref #9568, #4916, #4191
However, while writing this up, I started to doubt a bit if this is necessary.
`DataFrame.from_csv` is implemented as a round-trip method together with `to_csv`. If you use a plain `df.to_csv(path)`, you cannnot read it in as `pd.read_csv(path)` to get exactly the same. You at least need `pd.read_csv(path, index_col=True)` and a `parse_dates` keyword if you have datetimes.
Secondly, there is also the question of `Series.from_csv`. It would be logical to deprecate this as well, but for this you don't directly have an alternative (but `pd.read_csv(path, index_col=0)[0]` will work). There is also for example this SO answer of Wes: http://stackoverflow.com/questions/13557559/how-to-write-read-pandas-series-to-from-csv (and it is used in the Python for Data Analysis book).
| https://api.github.com/repos/pandas-dev/pandas/pulls/10163 | 2015-05-18T13:29:40Z | 2015-08-21T07:33:31Z | 2015-08-21T07:33:31Z | 2015-08-21T07:33:31Z |
BUG: Index.name is lost during timedelta ops | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index 2a4a408643451..8f72a8f1240d6 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -67,3 +67,7 @@ Bug Fixes
- Bug in ``NaT`` raises ``AttributeError`` when accessing to ``daysinmonth``, ``dayofweek`` properties. (:issue:`10096`)
- Bug in getting timezone data with ``dateutil`` on various platforms ( :issue:`9059`, :issue:`8639`, :issue:`9663`, :issue:`10121`)
+
+
+
+- Bug in ``DatetimeIndex`` and ``TimedeltaIndex`` names are lost after timedelta arithmetics ( :issue:`9926`)
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 3c92300d1f9a5..1c9326c047a79 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -3322,10 +3322,17 @@ def save(obj, path): # TODO remove in 0.13
def _maybe_match_name(a, b):
- a_name = getattr(a, 'name', None)
- b_name = getattr(b, 'name', None)
- if a_name == b_name:
- return a_name
+ a_has = hasattr(a, 'name')
+ b_has = hasattr(b, 'name')
+ if a_has and b_has:
+ if a.name == b.name:
+ return a.name
+ else:
+ return None
+ elif a_has:
+ return a.name
+ elif b_has:
+ return b.name
return None
def _random_state(state=None):
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index 3282a36bda7b8..c3d39fcdf906f 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -545,6 +545,27 @@ def test_random_state():
com._random_state(5.5)
+def test_maybe_match_name():
+
+ matched = com._maybe_match_name(Series([1], name='x'), Series([2], name='x'))
+ assert(matched == 'x')
+
+ matched = com._maybe_match_name(Series([1], name='x'), Series([2], name='y'))
+ assert(matched is None)
+
+ matched = com._maybe_match_name(Series([1]), Series([2], name='x'))
+ assert(matched is None)
+
+ matched = com._maybe_match_name(Series([1], name='x'), Series([2]))
+ assert(matched is None)
+
+ matched = com._maybe_match_name(Series([1], name='x'), [2])
+ assert(matched == 'x')
+
+ matched = com._maybe_match_name([1], Series([2], name='y'))
+ assert(matched == 'y')
+
+
class TestTake(tm.TestCase):
# standard incompatible fill error
fill_error = re.compile("Incompatible type for fill_value")
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index f3803a04baf01..bd0869b9525b7 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -653,14 +653,18 @@ def _sub_datelike(self, other):
def _add_delta(self, delta):
from pandas import TimedeltaIndex
+ name = self.name
+
if isinstance(delta, (Tick, timedelta, np.timedelta64)):
new_values = self._add_delta_td(delta)
elif isinstance(delta, TimedeltaIndex):
new_values = self._add_delta_tdi(delta)
+ # update name when delta is Index
+ name = com._maybe_match_name(self, delta)
else:
new_values = self.astype('O') + delta
tz = 'UTC' if self.tz is not None else None
- result = DatetimeIndex(new_values, tz=tz, freq='infer')
+ result = DatetimeIndex(new_values, tz=tz, name=name, freq='infer')
utc = _utc()
if self.tz is not None and self.tz is not utc:
result = result.tz_convert(self.tz)
diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py
index fd97ca4c45fc0..1443c22909689 100644
--- a/pandas/tseries/tdi.py
+++ b/pandas/tseries/tdi.py
@@ -281,12 +281,15 @@ def __setstate__(self, state):
def _add_delta(self, delta):
if isinstance(delta, (Tick, timedelta, np.timedelta64)):
new_values = self._add_delta_td(delta)
+ name = self.name
elif isinstance(delta, TimedeltaIndex):
new_values = self._add_delta_tdi(delta)
+ # update name when delta is index
+ name = com._maybe_match_name(self, delta)
else:
raise ValueError("cannot add the type {0} to a TimedeltaIndex".format(type(delta)))
- result = TimedeltaIndex(new_values, freq='infer')
+ result = TimedeltaIndex(new_values, freq='infer', name=name)
return result
def _evaluate_with_timedelta_like(self, other, op, opstr):
diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py
index d1b986e7a7a1c..55482401a20f4 100644
--- a/pandas/tseries/tests/test_base.py
+++ b/pandas/tseries/tests/test_base.py
@@ -634,27 +634,27 @@ def test_dti_dti_deprecated_ops(self):
def test_dti_tdi_numeric_ops(self):
# These are normally union/diff set-like ops
- tdi = TimedeltaIndex(['1 days',pd.NaT,'2 days'], name='foo')
- dti = date_range('20130101',periods=3, name='bar')
+ tdi = TimedeltaIndex(['1 days', pd.NaT, '2 days'], name='foo')
+ dti = date_range('20130101', periods=3, name='bar')
td = Timedelta('1 days')
dt = Timestamp('20130101')
result = tdi - tdi
expected = TimedeltaIndex(['0 days', pd.NaT, '0 days'], name='foo')
- tm.assert_index_equal(result, expected, check_names=False) # must be foo
+ tm.assert_index_equal(result, expected)
result = tdi + tdi
expected = TimedeltaIndex(['2 days', pd.NaT, '4 days'], name='foo')
- tm.assert_index_equal(result, expected, check_names=False) # must be foo
+ tm.assert_index_equal(result, expected)
- result = dti - tdi
+ result = dti - tdi # name will be reset
expected = DatetimeIndex(['20121231', pd.NaT, '20130101'])
tm.assert_index_equal(result, expected)
def test_addition_ops(self):
# with datetimes/timedelta and tdi/dti
- tdi = TimedeltaIndex(['1 days',pd.NaT,'2 days'], name='foo')
+ tdi = TimedeltaIndex(['1 days', pd.NaT, '2 days'], name='foo')
dti = date_range('20130101', periods=3, name='bar')
td = Timedelta('1 days')
dt = Timestamp('20130101')
@@ -669,11 +669,11 @@ def test_addition_ops(self):
result = td + tdi
expected = TimedeltaIndex(['2 days', pd.NaT, '3 days'], name='foo')
- tm.assert_index_equal(result, expected, check_names=False) # must be foo
+ tm.assert_index_equal(result, expected)
result = tdi + td
expected = TimedeltaIndex(['2 days', pd.NaT, '3 days'], name='foo')
- tm.assert_index_equal(result,expected, check_names=False) # must be foo
+ tm.assert_index_equal(result, expected)
# unequal length
self.assertRaises(ValueError, lambda : tdi + dti[0:1])
@@ -685,21 +685,21 @@ def test_addition_ops(self):
# this is a union!
#self.assertRaises(TypeError, lambda : Int64Index([1,2,3]) + tdi)
- result = tdi + dti
+ result = tdi + dti # name will be reset
expected = DatetimeIndex(['20130102', pd.NaT, '20130105'])
- tm.assert_index_equal(result,expected)
+ tm.assert_index_equal(result, expected)
- result = dti + tdi
- expected = DatetimeIndex(['20130102',pd.NaT,'20130105'])
- tm.assert_index_equal(result,expected)
+ result = dti + tdi # name will be reset
+ expected = DatetimeIndex(['20130102', pd.NaT, '20130105'])
+ tm.assert_index_equal(result, expected)
result = dt + td
expected = Timestamp('20130102')
- self.assertEqual(result,expected)
+ self.assertEqual(result, expected)
result = td + dt
expected = Timestamp('20130102')
- self.assertEqual(result,expected)
+ self.assertEqual(result, expected)
def test_value_counts_unique(self):
# GH 7735
| Closes #9926.
CC @hsperr I've changed `common._maybe_match_names` to work with not-named objects, and hopefully can use in #9965 also.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10158 | 2015-05-16T21:31:49Z | 2015-05-18T11:59:45Z | 2015-05-18T11:59:45Z | 2015-06-02T19:26:12Z |
BUG: Index.union cannot handle array-likes | diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt
index dae1342c3cd76..bebce2d3e2d87 100644
--- a/doc/source/whatsnew/v0.17.0.txt
+++ b/doc/source/whatsnew/v0.17.0.txt
@@ -67,6 +67,7 @@ Bug Fixes
- Bug in ``mean()`` where integer dtypes can overflow (:issue:`10172`)
- Bug where Panel.from_dict does not set dtype when specified (:issue:`10058`)
+- Bug in ``Index.union`` raises ``AttributeError`` when passing array-likes. (:issue:`10149`)
- Bug in ``Timestamp``'s' ``microsecond``, ``quarter``, ``dayofyear``, ``week`` and ``daysinmonth`` properties return ``np.int`` type, not built-in ``int``. (:issue:`10050`)
- Bug in ``NaT`` raises ``AttributeError`` when accessing to ``daysinmonth``, ``dayofweek`` properties. (:issue:`10096`)
@@ -91,3 +92,4 @@ Bug Fixes
- Bug where infer_freq infers timerule (WOM-5XXX) unsupported by to_offset (:issue:`9425`)
+
diff --git a/pandas/core/index.py b/pandas/core/index.py
index de30fee4009f4..2bd96fcec2e42 100644
--- a/pandas/core/index.py
+++ b/pandas/core/index.py
@@ -580,8 +580,18 @@ def to_datetime(self, dayfirst=False):
return DatetimeIndex(self.values)
def _assert_can_do_setop(self, other):
+ if not com.is_list_like(other):
+ raise TypeError('Input must be Index or array-like')
return True
+ def _convert_can_do_setop(self, other):
+ if not isinstance(other, Index):
+ other = Index(other, name=self.name)
+ result_name = self.name
+ else:
+ result_name = self.name if self.name == other.name else None
+ return other, result_name
+
@property
def nlevels(self):
return 1
@@ -1364,16 +1374,14 @@ def union(self, other):
-------
union : Index
"""
- if not hasattr(other, '__iter__'):
- raise TypeError('Input must be iterable.')
+ self._assert_can_do_setop(other)
+ other = _ensure_index(other)
if len(other) == 0 or self.equals(other):
return self
if len(self) == 0:
- return _ensure_index(other)
-
- self._assert_can_do_setop(other)
+ return other
if not is_dtype_equal(self.dtype,other.dtype):
this = self.astype('O')
@@ -1439,11 +1447,7 @@ def intersection(self, other):
-------
intersection : Index
"""
- if not hasattr(other, '__iter__'):
- raise TypeError('Input must be iterable!')
-
self._assert_can_do_setop(other)
-
other = _ensure_index(other)
if self.equals(other):
@@ -1492,18 +1496,12 @@ def difference(self, other):
>>> index.difference(index2)
"""
-
- if not hasattr(other, '__iter__'):
- raise TypeError('Input must be iterable!')
+ self._assert_can_do_setop(other)
if self.equals(other):
return Index([], name=self.name)
- if not isinstance(other, Index):
- other = np.asarray(other)
- result_name = self.name
- else:
- result_name = self.name if self.name == other.name else None
+ other, result_name = self._convert_can_do_setop(other)
theDiff = sorted(set(self) - set(other))
return Index(theDiff, name=result_name)
@@ -1517,7 +1515,7 @@ def sym_diff(self, other, result_name=None):
Parameters
----------
- other : array-like
+ other : Index or array-like
result_name : str
Returns
@@ -1545,13 +1543,10 @@ def sym_diff(self, other, result_name=None):
>>> idx1 ^ idx2
Int64Index([1, 5], dtype='int64')
"""
- if not hasattr(other, '__iter__'):
- raise TypeError('Input must be iterable!')
-
- if not isinstance(other, Index):
- other = Index(other)
- result_name = result_name or self.name
-
+ self._assert_can_do_setop(other)
+ other, result_name_update = self._convert_can_do_setop(other)
+ if result_name is None:
+ result_name = result_name_update
the_diff = sorted(set((self.difference(other)).union(other.difference(self))))
return Index(the_diff, name=result_name)
@@ -5460,12 +5455,11 @@ def union(self, other):
>>> index.union(index2)
"""
self._assert_can_do_setop(other)
+ other, result_names = self._convert_can_do_setop(other)
if len(other) == 0 or self.equals(other):
return self
- result_names = self.names if self.names == other.names else None
-
uniq_tuples = lib.fast_unique_multiple([self.values, other.values])
return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
names=result_names)
@@ -5483,12 +5477,11 @@ def intersection(self, other):
Index
"""
self._assert_can_do_setop(other)
+ other, result_names = self._convert_can_do_setop(other)
if self.equals(other):
return self
- result_names = self.names if self.names == other.names else None
-
self_tuples = self.values
other_tuples = other.values
uniq_tuples = sorted(set(self_tuples) & set(other_tuples))
@@ -5509,18 +5502,10 @@ def difference(self, other):
diff : MultiIndex
"""
self._assert_can_do_setop(other)
+ other, result_names = self._convert_can_do_setop(other)
- if not isinstance(other, MultiIndex):
- if len(other) == 0:
+ if len(other) == 0:
return self
- try:
- other = MultiIndex.from_tuples(other)
- except:
- raise TypeError('other must be a MultiIndex or a list of'
- ' tuples')
- result_names = self.names
- else:
- result_names = self.names if self.names == other.names else None
if self.equals(other):
return MultiIndex(levels=[[]] * self.nlevels,
@@ -5537,15 +5522,30 @@ def difference(self, other):
return MultiIndex.from_tuples(difference, sortorder=0,
names=result_names)
- def _assert_can_do_setop(self, other):
- pass
-
def astype(self, dtype):
if not is_object_dtype(np.dtype(dtype)):
raise TypeError('Setting %s dtype to anything other than object '
'is not supported' % self.__class__)
return self._shallow_copy()
+ def _convert_can_do_setop(self, other):
+ result_names = self.names
+
+ if not hasattr(other, 'names'):
+ if len(other) == 0:
+ other = MultiIndex(levels=[[]] * self.nlevels,
+ labels=[[]] * self.nlevels,
+ verify_integrity=False)
+ else:
+ msg = 'other must be a MultiIndex or a list of tuples'
+ try:
+ other = MultiIndex.from_tuples(other)
+ except:
+ raise TypeError(msg)
+ else:
+ result_names = self.names if self.names == other.names else None
+ return other, result_names
+
def insert(self, loc, item):
"""
Make new MultiIndex inserting new item at location
diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py
index 93299292cf353..ed84c9764dd84 100644
--- a/pandas/tests/test_index.py
+++ b/pandas/tests/test_index.py
@@ -251,6 +251,136 @@ def test_take(self):
expected = ind[indexer]
self.assertTrue(result.equals(expected))
+ def test_setops_errorcases(self):
+ for name, idx in compat.iteritems(self.indices):
+ # # non-iterable input
+ cases = [0.5, 'xxx']
+ methods = [idx.intersection, idx.union, idx.difference, idx.sym_diff]
+
+ for method in methods:
+ for case in cases:
+ assertRaisesRegexp(TypeError,
+ "Input must be Index or array-like",
+ method, case)
+
+ def test_intersection_base(self):
+ for name, idx in compat.iteritems(self.indices):
+ first = idx[:5]
+ second = idx[:3]
+ intersect = first.intersection(second)
+
+ if isinstance(idx, CategoricalIndex):
+ pass
+ else:
+ self.assertTrue(tm.equalContents(intersect, second))
+
+ # GH 10149
+ cases = [klass(second.values) for klass in [np.array, Series, list]]
+ for case in cases:
+ if isinstance(idx, PeriodIndex):
+ msg = "can only call with other PeriodIndex-ed objects"
+ with tm.assertRaisesRegexp(ValueError, msg):
+ result = first.intersection(case)
+ elif isinstance(idx, CategoricalIndex):
+ pass
+ else:
+ result = first.intersection(case)
+ self.assertTrue(tm.equalContents(result, second))
+
+ if isinstance(idx, MultiIndex):
+ msg = "other must be a MultiIndex or a list of tuples"
+ with tm.assertRaisesRegexp(TypeError, msg):
+ result = first.intersection([1, 2, 3])
+
+ def test_union_base(self):
+ for name, idx in compat.iteritems(self.indices):
+ first = idx[3:]
+ second = idx[:5]
+ everything = idx
+ union = first.union(second)
+ self.assertTrue(tm.equalContents(union, everything))
+
+ # GH 10149
+ cases = [klass(second.values) for klass in [np.array, Series, list]]
+ for case in cases:
+ if isinstance(idx, PeriodIndex):
+ msg = "can only call with other PeriodIndex-ed objects"
+ with tm.assertRaisesRegexp(ValueError, msg):
+ result = first.union(case)
+ elif isinstance(idx, CategoricalIndex):
+ pass
+ else:
+ result = first.union(case)
+ self.assertTrue(tm.equalContents(result, everything))
+
+ if isinstance(idx, MultiIndex):
+ msg = "other must be a MultiIndex or a list of tuples"
+ with tm.assertRaisesRegexp(TypeError, msg):
+ result = first.union([1, 2, 3])
+
+ def test_difference_base(self):
+ for name, idx in compat.iteritems(self.indices):
+ first = idx[2:]
+ second = idx[:4]
+ answer = idx[4:]
+ result = first.difference(second)
+
+ if isinstance(idx, CategoricalIndex):
+ pass
+ else:
+ self.assertTrue(tm.equalContents(result, answer))
+
+ # GH 10149
+ cases = [klass(second.values) for klass in [np.array, Series, list]]
+ for case in cases:
+ if isinstance(idx, PeriodIndex):
+ msg = "can only call with other PeriodIndex-ed objects"
+ with tm.assertRaisesRegexp(ValueError, msg):
+ result = first.difference(case)
+ elif isinstance(idx, CategoricalIndex):
+ pass
+ elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
+ self.assertEqual(result.__class__, answer.__class__)
+ self.assert_numpy_array_equal(result.asi8, answer.asi8)
+ else:
+ result = first.difference(case)
+ self.assertTrue(tm.equalContents(result, answer))
+
+ if isinstance(idx, MultiIndex):
+ msg = "other must be a MultiIndex or a list of tuples"
+ with tm.assertRaisesRegexp(TypeError, msg):
+ result = first.difference([1, 2, 3])
+
+ def test_symmetric_diff(self):
+ for name, idx in compat.iteritems(self.indices):
+ first = idx[1:]
+ second = idx[:-1]
+ if isinstance(idx, CategoricalIndex):
+ pass
+ else:
+ answer = idx[[0, -1]]
+ result = first.sym_diff(second)
+ self.assertTrue(tm.equalContents(result, answer))
+
+ # GH 10149
+ cases = [klass(second.values) for klass in [np.array, Series, list]]
+ for case in cases:
+ if isinstance(idx, PeriodIndex):
+ msg = "can only call with other PeriodIndex-ed objects"
+ with tm.assertRaisesRegexp(ValueError, msg):
+ result = first.sym_diff(case)
+ elif isinstance(idx, CategoricalIndex):
+ pass
+ else:
+ result = first.sym_diff(case)
+ self.assertTrue(tm.equalContents(result, answer))
+
+ if isinstance(idx, MultiIndex):
+ msg = "other must be a MultiIndex or a list of tuples"
+ with tm.assertRaisesRegexp(TypeError, msg):
+ result = first.sym_diff([1, 2, 3])
+
+
class TestIndex(Base, tm.TestCase):
_holder = Index
_multiprocess_can_split_ = True
@@ -620,16 +750,12 @@ def test_intersection(self):
first = self.strIndex[:20]
second = self.strIndex[:10]
intersect = first.intersection(second)
-
self.assertTrue(tm.equalContents(intersect, second))
# Corner cases
inter = first.intersection(first)
self.assertIs(inter, first)
- # non-iterable input
- assertRaisesRegexp(TypeError, "iterable", first.intersection, 0.5)
-
idx1 = Index([1, 2, 3, 4, 5], name='idx')
# if target has the same name, it is preserved
idx2 = Index([3, 4, 5, 6, 7], name='idx')
@@ -671,6 +797,12 @@ def test_union(self):
union = first.union(second)
self.assertTrue(tm.equalContents(union, everything))
+ # GH 10149
+ cases = [klass(second.values) for klass in [np.array, Series, list]]
+ for case in cases:
+ result = first.union(case)
+ self.assertTrue(tm.equalContents(result, everything))
+
# Corner cases
union = first.union(first)
self.assertIs(union, first)
@@ -681,9 +813,6 @@ def test_union(self):
union = Index([]).union(first)
self.assertIs(union, first)
- # non-iterable input
- assertRaisesRegexp(TypeError, "iterable", first.union, 0.5)
-
# preserve names
first.name = 'A'
second.name = 'A'
@@ -792,11 +921,7 @@ def test_difference(self):
self.assertEqual(len(result), 0)
self.assertEqual(result.name, first.name)
- # non-iterable input
- assertRaisesRegexp(TypeError, "iterable", first.difference, 0.5)
-
def test_symmetric_diff(self):
-
# smoke
idx1 = Index([1, 2, 3, 4], name='idx1')
idx2 = Index([2, 3, 4, 5])
@@ -842,10 +967,6 @@ def test_symmetric_diff(self):
self.assertTrue(tm.equalContents(result, expected))
self.assertEqual(result.name, 'new_name')
- # other isn't iterable
- with tm.assertRaises(TypeError):
- Index(idx1,dtype='object').difference(1)
-
def test_is_numeric(self):
self.assertFalse(self.dateIndex.is_numeric())
self.assertFalse(self.strIndex.is_numeric())
@@ -1786,6 +1907,7 @@ def test_equals(self):
self.assertFalse(CategoricalIndex(list('aabca') + [np.nan],categories=['c','a','b',np.nan]).equals(list('aabca')))
self.assertTrue(CategoricalIndex(list('aabca') + [np.nan],categories=['c','a','b',np.nan]).equals(list('aabca') + [np.nan]))
+
class Numeric(Base):
def test_numeric_compat(self):
@@ -2661,6 +2783,36 @@ def test_time_overflow_for_32bit_machines(self):
idx2 = pd.date_range(end='2000', periods=periods, freq='S')
self.assertEqual(len(idx2), periods)
+ def test_intersection(self):
+ first = self.index
+ second = self.index[5:]
+ intersect = first.intersection(second)
+ self.assertTrue(tm.equalContents(intersect, second))
+
+ # GH 10149
+ cases = [klass(second.values) for klass in [np.array, Series, list]]
+ for case in cases:
+ result = first.intersection(case)
+ self.assertTrue(tm.equalContents(result, second))
+
+ third = Index(['a', 'b', 'c'])
+ result = first.intersection(third)
+ expected = pd.Index([], dtype=object)
+ self.assert_index_equal(result, expected)
+
+ def test_union(self):
+ first = self.index[:5]
+ second = self.index[5:]
+ everything = self.index
+ union = first.union(second)
+ self.assertTrue(tm.equalContents(union, everything))
+
+ # GH 10149
+ cases = [klass(second.values) for klass in [np.array, Series, list]]
+ for case in cases:
+ result = first.union(case)
+ self.assertTrue(tm.equalContents(result, everything))
+
class TestPeriodIndex(DatetimeLike, tm.TestCase):
_holder = PeriodIndex
@@ -2671,7 +2823,7 @@ def setUp(self):
self.setup_indices()
def create_index(self):
- return period_range('20130101',periods=5,freq='D')
+ return period_range('20130101', periods=5, freq='D')
def test_pickle_compat_construction(self):
pass
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index bd0869b9525b7..745c536914e47 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -804,6 +804,7 @@ def union(self, other):
-------
y : Index or DatetimeIndex
"""
+ self._assert_can_do_setop(other)
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
@@ -1039,6 +1040,7 @@ def intersection(self, other):
-------
y : Index or DatetimeIndex
"""
+ self._assert_can_do_setop(other)
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py
index 510887a185054..6627047f0c335 100644
--- a/pandas/tseries/period.py
+++ b/pandas/tseries/period.py
@@ -679,6 +679,8 @@ def join(self, other, how='left', level=None, return_indexers=False):
return self._apply_meta(result)
def _assert_can_do_setop(self, other):
+ super(PeriodIndex, self)._assert_can_do_setop(other)
+
if not isinstance(other, PeriodIndex):
raise ValueError('can only call with other PeriodIndex-ed objects')
diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py
index 1443c22909689..de68dd763d68c 100644
--- a/pandas/tseries/tdi.py
+++ b/pandas/tseries/tdi.py
@@ -436,12 +436,12 @@ def union(self, other):
-------
y : Index or TimedeltaIndex
"""
- if _is_convertible_to_index(other):
+ self._assert_can_do_setop(other)
+ if not isinstance(other, TimedeltaIndex):
try:
other = TimedeltaIndex(other)
- except TypeError:
+ except (TypeError, ValueError):
pass
-
this, other = self, other
if this._can_fast_union(other):
@@ -581,6 +581,7 @@ def intersection(self, other):
-------
y : Index or TimedeltaIndex
"""
+ self._assert_can_do_setop(other)
if not isinstance(other, TimedeltaIndex):
try:
other = TimedeltaIndex(other)
| Closes #10149.
- Added explicit tests for array-likes in set-ops, `intersection`, `union`, `difference` and `sym_diff`.
- `TimedeltaIndex` previously did additional input check which is inconsistent with others. Made it work as the same manner as others.
- `MultiIndex` set-ops should only accept list-likes of tuples. Implement the logic and add explicit test.
- Changed to understandable error message for `str` which has `__iter__` in py3.
```
# current error msg
idx.intersection('aaa')
# TypeError: Index(...) must be called with a collection of some kind, 'aaa' was passed
```
NOTE: This DOESN'T care the `name` attribute checks, which is being worked in #9965.
| https://api.github.com/repos/pandas-dev/pandas/pulls/10157 | 2015-05-16T21:26:19Z | 2015-06-01T11:45:10Z | 2015-06-01T11:45:10Z | 2015-06-02T19:26:12Z |
TST: use compat.long in test_tslib for py3 | diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py
index c5843862306ca..948a0be91b276 100644
--- a/pandas/tseries/tests/test_timedeltas.py
+++ b/pandas/tseries/tests/test_timedeltas.py
@@ -23,6 +23,8 @@
import pandas.util.testing as tm
from numpy.random import rand, randn
from pandas import _np_version_under1p8
+import pandas.compat as compat
+
iNaT = tslib.iNaT
@@ -311,7 +313,7 @@ def test_fields(self):
def check(value):
# that we are int/long like
- self.assertTrue(isinstance(value, (int, long)))
+ self.assertTrue(isinstance(value, (int, compat.long)))
# compat to datetime.timedelta
rng = to_timedelta('1 days, 10:11:12')
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index 27e5b927f9719..341450f504e2a 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -14,6 +14,8 @@
import pandas.tseries.offsets as offsets
import pandas.util.testing as tm
from pandas.util.testing import assert_series_equal
+import pandas.compat as compat
+
class TestTimestamp(tm.TestCase):
@@ -373,7 +375,7 @@ def test_fields(self):
def check(value, equal):
# that we are int/long like
- self.assertTrue(isinstance(value, (int, long)))
+ self.assertTrue(isinstance(value, (int, compat.long)))
self.assertEqual(value, equal)
# GH 10050
| https://api.github.com/repos/pandas-dev/pandas/pulls/10152 | 2015-05-15T23:45:07Z | 2015-05-16T15:01:07Z | 2015-05-16T15:01:07Z | 2015-05-17T14:40:11Z | |
ENH: groupby.apply for Categorical should preserve categories (closes… | diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index b571aab0b19a5..1a8fc90b9683f 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -55,7 +55,7 @@ Bug Fixes
multi-indexed (:issue:`7212`)
- Bug in ``Categorical`` repr with ``display.width`` of ``None`` in Python 3 (:issue:`10087`)
-
+- Bug in groupby.apply aggregation for Categorical not preserving categories (:issue:`10138`)
- Bug in ``mean()`` where integer dtypes can overflow (:issue:`10172`)
- Bug where Panel.from_dict does not set dtype when specified (:issue:`10058`)
- Bug in ``Index.union`` raises ``AttributeError`` when passing array-likes. (:issue:`10149`)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 51674bad60f5b..4abdd1112c721 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -2944,7 +2944,8 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
cd = 'coerce'
else:
cd = True
- return result.convert_objects(convert_dates=cd)
+ result = result.convert_objects(convert_dates=cd)
+ return self._reindex_output(result)
else:
# only coerce dates if we find at least 1 datetime
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 0789e20df3945..ab78bd63a7c94 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -2595,6 +2595,35 @@ def get_stats(group):
result = self.df.groupby(cats).D.apply(get_stats)
self.assertEqual(result.index.names[0], 'C')
+ def test_apply_categorical_data(self):
+ # GH 10138
+ for ordered in [True, False]:
+ dense = Categorical(list('abc'), ordered=ordered)
+ # 'b' is in the categories but not in the list
+ missing = Categorical(list('aaa'), categories=['a', 'b'], ordered=ordered)
+ values = np.arange(len(dense))
+ df = DataFrame({'missing': missing,
+ 'dense': dense,
+ 'values': values})
+ grouped = df.groupby(['missing', 'dense'])
+
+ # missing category 'b' should still exist in the output index
+ idx = MultiIndex.from_product([['a', 'b'], ['a', 'b', 'c']],
+ names=['missing', 'dense'])
+ expected = DataFrame([0, 1, 2, np.nan, np.nan, np.nan],
+ index=idx,
+ columns=['values'])
+
+ assert_frame_equal(grouped.apply(lambda x: np.mean(x)), expected)
+ assert_frame_equal(grouped.mean(), expected)
+ assert_frame_equal(grouped.agg(np.mean), expected)
+
+ # but for transform we should still get back the original index
+ idx = MultiIndex.from_product([['a'], ['a', 'b', 'c']],
+ names=['missing', 'dense'])
+ expected = Series(1, index=idx)
+ assert_series_equal(grouped.apply(lambda x: 1), expected)
+
def test_apply_corner_cases(self):
# #535, can't use sliding iterator
| closes #10138
| https://api.github.com/repos/pandas-dev/pandas/pulls/10142 | 2015-05-15T04:33:27Z | 2015-06-04T13:50:27Z | 2015-06-04T13:50:27Z | 2015-06-04T15:18:02Z |
CLN: clean up unused imports part II | diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py
index f3a7aa0bfa4c6..88b4117d4807c 100644
--- a/pandas/tseries/base.py
+++ b/pandas/tseries/base.py
@@ -3,7 +3,7 @@
"""
import warnings
-from datetime import datetime, time, timedelta
+from datetime import datetime, timedelta
from pandas import compat
import numpy as np
@@ -13,11 +13,9 @@
import pandas.lib as lib
from pandas.core.index import Index
from pandas.util.decorators import Appender, cache_readonly
-from pandas.tseries.frequencies import (
- infer_freq, to_offset, get_period_alias,
- Resolution)
+from pandas.tseries.frequencies import infer_freq, to_offset, Resolution
import pandas.algos as _algos
-from pandas.core.config import get_option
+
class DatetimeIndexOpsMixin(object):
""" common ops mixin to support a unified inteface datetimelike Index """
diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py
index 8e468a7701462..c273906ef3d05 100644
--- a/pandas/tseries/common.py
+++ b/pandas/tseries/common.py
@@ -6,7 +6,7 @@
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
from pandas.tseries.tdi import TimedeltaIndex
-from pandas import lib, tslib
+from pandas import tslib
from pandas.core.common import (_NS_DTYPE, _TD_DTYPE, is_period_arraylike,
is_datetime_arraylike, is_integer_dtype, is_list_like,
get_dtype_kinds)
diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py
index f56b40a70d551..f3803a04baf01 100644
--- a/pandas/tseries/index.py
+++ b/pandas/tseries/index.py
@@ -1,13 +1,8 @@
# pylint: disable=E1101
import operator
-
from datetime import time, datetime
from datetime import timedelta
-
import numpy as np
-
-import warnings
-
from pandas.core.common import (_NS_DTYPE, _INT64_DTYPE,
_values_from_object, _maybe_box,
ABCSeries, is_integer, is_float)
diff --git a/pandas/tseries/interval.py b/pandas/tseries/interval.py
index 104e088ee4e84..bcce64c3a71bf 100644
--- a/pandas/tseries/interval.py
+++ b/pandas/tseries/interval.py
@@ -1,4 +1,3 @@
-import numpy as np
from pandas.core.index import Index
diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py
index 8b7dc90738bd0..98d9f9f14d3da 100644
--- a/pandas/tseries/period.py
+++ b/pandas/tseries/period.py
@@ -1,10 +1,6 @@
# pylint: disable=E1101,E1103,W0232
-import operator
-
-from datetime import datetime, date, timedelta
+from datetime import datetime, timedelta
import numpy as np
-from pandas.core.base import PandasObject
-
import pandas.tseries.frequencies as frequencies
from pandas.tseries.frequencies import get_freq_code as _gfc
from pandas.tseries.index import DatetimeIndex, Int64Index, Index
diff --git a/pandas/tseries/plotting.py b/pandas/tseries/plotting.py
index 899d2bfdc9c76..9d28fa11f646f 100644
--- a/pandas/tseries/plotting.py
+++ b/pandas/tseries/plotting.py
@@ -5,17 +5,13 @@
#!!! TODO: Use the fact that axis can have units to simplify the process
from matplotlib import pylab
-
-import numpy as np
-
-from pandas import isnull
from pandas.tseries.period import Period
from pandas.tseries.offsets import DateOffset
import pandas.tseries.frequencies as frequencies
from pandas.tseries.index import DatetimeIndex
import pandas.core.common as com
-from pandas.tseries.converter import (PeriodConverter, TimeSeries_DateLocator,
+from pandas.tseries.converter import (TimeSeries_DateLocator,
TimeSeries_DateFormatter)
#----------------------------------------------------------------------
diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py
index 942dea84f501a..53c1292204f71 100644
--- a/pandas/tseries/resample.py
+++ b/pandas/tseries/resample.py
@@ -1,14 +1,11 @@
from datetime import timedelta
-
import numpy as np
-
from pandas.core.groupby import BinGrouper, Grouper
from pandas.tseries.frequencies import to_offset, is_subperiod, is_superperiod
from pandas.tseries.index import DatetimeIndex, date_range
from pandas.tseries.tdi import TimedeltaIndex
from pandas.tseries.offsets import DateOffset, Tick, Day, _delta_to_nanoseconds
from pandas.tseries.period import PeriodIndex, period_range
-import pandas.tseries.tools as tools
import pandas.core.common as com
import pandas.compat as compat
diff --git a/pandas/tseries/tdi.py b/pandas/tseries/tdi.py
index 80475fc8426db..fd97ca4c45fc0 100644
--- a/pandas/tseries/tdi.py
+++ b/pandas/tseries/tdi.py
@@ -1,17 +1,13 @@
""" implement the TimedeltaIndex """
-import operator
-import datetime
from datetime import timedelta
import numpy as np
-
from pandas.core.common import (ABCSeries, _TD_DTYPE, _INT64_DTYPE,
is_timedelta64_dtype, _maybe_box,
_values_from_object, isnull, is_integer, is_float)
from pandas.core.index import Index, Int64Index
import pandas.compat as compat
from pandas.compat import u
-from pandas.core.base import PandasObject
from pandas.util.decorators import cache_readonly
from pandas.tseries.frequencies import to_offset
import pandas.core.common as com
diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py
index 5b353058f0093..624981c5536f5 100644
--- a/pandas/tseries/timedeltas.py
+++ b/pandas/tseries/timedeltas.py
@@ -3,14 +3,12 @@
"""
import re
-from datetime import timedelta
-
import numpy as np
import pandas.tslib as tslib
from pandas import compat
-from pandas.core.common import (ABCSeries, is_integer, is_integer_dtype,
- is_timedelta64_dtype, _values_from_object,
- is_list_like, isnull, _ensure_object)
+from pandas.core.common import (ABCSeries, is_integer_dtype,
+ is_timedelta64_dtype, is_list_like,
+ isnull, _ensure_object)
def to_timedelta(arg, unit='ns', box=True, coerce=False):
"""
diff --git a/pandas/tseries/util.py b/pandas/tseries/util.py
index 72b12ea495ba0..6c534de0a7aaa 100644
--- a/pandas/tseries/util.py
+++ b/pandas/tseries/util.py
@@ -1,8 +1,5 @@
from pandas.compat import range, lrange
import numpy as np
-
-import pandas as pd
-
import pandas.core.common as com
from pandas.core.frame import DataFrame
import pandas.core.nanops as nanops
| found quite a few more under `pandas/tseries/` ... this should be it for now :)
| https://api.github.com/repos/pandas-dev/pandas/pulls/10141 | 2015-05-15T02:03:35Z | 2015-05-15T13:09:15Z | 2015-05-15T13:09:15Z | 2015-06-02T19:26:12Z |
DOC: consistent imports (GH9886) part II | diff --git a/doc/source/10min.rst b/doc/source/10min.rst
index 94c2d921eb116..1714e00030026 100644
--- a/doc/source/10min.rst
+++ b/doc/source/10min.rst
@@ -6,18 +6,16 @@
:suppress:
import numpy as np
- import random
+ import pandas as pd
import os
np.random.seed(123456)
- from pandas import options
- import pandas as pd
np.set_printoptions(precision=4, suppress=True)
import matplotlib
try:
matplotlib.style.use('ggplot')
except AttributeError:
- options.display.mpl_style = 'default'
- options.display.max_rows=15
+ pd.options.display.mpl_style = 'default'
+ pd.options.display.max_rows = 15
#### portions of this were borrowed from the
#### Pandas cheatsheet
@@ -298,7 +296,7 @@ Using the :func:`~Series.isin` method for filtering:
.. ipython:: python
df2 = df.copy()
- df2['E']=['one', 'one','two','three','four','three']
+ df2['E'] = ['one', 'one','two','three','four','three']
df2
df2[df2['E'].isin(['two','four'])]
@@ -310,7 +308,7 @@ by the indexes
.. ipython:: python
- s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))
+ s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
s1
df['F'] = s1
@@ -359,7 +357,7 @@ returns a copy of the data.
.. ipython:: python
- df1 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E'])
+ df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
df1.loc[dates[0]:dates[1],'E'] = 1
df1
@@ -409,9 +407,9 @@ In addition, pandas automatically broadcasts along the specified dimension.
.. ipython:: python
- s = pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2)
+ s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
s
- df.sub(s,axis='index')
+ df.sub(s, axis='index')
Apply
@@ -431,7 +429,7 @@ See more at :ref:`Histogramming and Discretization <basics.discretization>`
.. ipython:: python
- s = pd.Series(np.random.randint(0,7,size=10))
+ s = pd.Series(np.random.randint(0, 7, size=10))
s
s.value_counts()
@@ -516,9 +514,9 @@ See the :ref:`Grouping section <groupby>`
.. ipython:: python
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
- 'foo', 'bar', 'foo', 'foo'],
+ 'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
- 'two', 'two', 'one', 'three'],
+ 'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
df
diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst
index 688935c6b104d..656eff744bb47 100644
--- a/doc/source/advanced.rst
+++ b/doc/source/advanced.rst
@@ -6,15 +6,10 @@
:suppress:
import numpy as np
- import random
- np.random.seed(123456)
- from pandas import *
- options.display.max_rows=15
import pandas as pd
- randn = np.random.randn
- randint = np.random.randint
+ np.random.seed(123456)
np.set_printoptions(precision=4, suppress=True)
- from pandas.compat import range, zip
+ pd.options.display.max_rows=15
******************************
MultiIndex / Advanced Indexing
@@ -80,10 +75,10 @@ demo different ways to initialize MultiIndexes.
tuples = list(zip(*arrays))
tuples
- index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
+ index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
index
- s = Series(randn(8), index=index)
+ s = pd.Series(np.random.randn(8), index=index)
s
When you want every pairing of the elements in two iterables, it can be easier
@@ -92,7 +87,7 @@ to use the ``MultiIndex.from_product`` function:
.. ipython:: python
iterables = [['bar', 'baz', 'foo', 'qux'], ['one', 'two']]
- MultiIndex.from_product(iterables, names=['first', 'second'])
+ pd.MultiIndex.from_product(iterables, names=['first', 'second'])
As a convenience, you can pass a list of arrays directly into Series or
DataFrame to construct a MultiIndex automatically:
@@ -101,9 +96,9 @@ DataFrame to construct a MultiIndex automatically:
arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
- s = Series(randn(8), index=arrays)
+ s = pd.Series(np.random.randn(8), index=arrays)
s
- df = DataFrame(randn(8, 4), index=arrays)
+ df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
df
All of the ``MultiIndex`` constructors accept a ``names`` argument which stores
@@ -119,9 +114,9 @@ of the index is up to you:
.. ipython:: python
- df = DataFrame(randn(3, 8), index=['A', 'B', 'C'], columns=index)
+ df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)
df
- DataFrame(randn(6, 6), index=index[:6], columns=index[:6])
+ pd.DataFrame(np.random.randn(6, 6), index=index[:6], columns=index[:6])
We've "sparsified" the higher levels of the indexes to make the console output a
bit easier on the eyes.
@@ -131,7 +126,7 @@ tuples as atomic labels on an axis:
.. ipython:: python
- Series(randn(8), index=tuples)
+ pd.Series(np.random.randn(8), index=tuples)
The reason that the ``MultiIndex`` matters is that it can allow you to do
grouping, selection, and reshaping operations as we will describe below and in
@@ -282,16 +277,16 @@ As usual, **both sides** of the slicers are included as this is label indexing.
def mklbl(prefix,n):
return ["%s%s" % (prefix,i) for i in range(n)]
- miindex = MultiIndex.from_product([mklbl('A',4),
- mklbl('B',2),
- mklbl('C',4),
- mklbl('D',2)])
- micolumns = MultiIndex.from_tuples([('a','foo'),('a','bar'),
- ('b','foo'),('b','bah')],
- names=['lvl0', 'lvl1'])
- dfmi = DataFrame(np.arange(len(miindex)*len(micolumns)).reshape((len(miindex),len(micolumns))),
- index=miindex,
- columns=micolumns).sortlevel().sortlevel(axis=1)
+ miindex = pd.MultiIndex.from_product([mklbl('A',4),
+ mklbl('B',2),
+ mklbl('C',4),
+ mklbl('D',2)])
+ micolumns = pd.MultiIndex.from_tuples([('a','foo'),('a','bar'),
+ ('b','foo'),('b','bah')],
+ names=['lvl0', 'lvl1'])
+ dfmi = pd.DataFrame(np.arange(len(miindex)*len(micolumns)).reshape((len(miindex),len(micolumns))),
+ index=miindex,
+ columns=micolumns).sortlevel().sortlevel(axis=1)
dfmi
Basic multi-index slicing using slices, lists, and labels.
@@ -418,9 +413,9 @@ instance:
.. ipython:: python
- midx = MultiIndex(levels=[['zero', 'one'], ['x','y']],
- labels=[[1,1,0,0],[1,0,1,0]])
- df = DataFrame(randn(4,2), index=midx)
+ midx = pd.MultiIndex(levels=[['zero', 'one'], ['x','y']],
+ labels=[[1,1,0,0],[1,0,1,0]])
+ df = pd.DataFrame(np.random.randn(4,2), index=midx)
df
df2 = df.mean(level=0)
df2
@@ -471,7 +466,7 @@ labels will be sorted lexicographically!
.. ipython:: python
import random; random.shuffle(tuples)
- s = Series(randn(8), index=MultiIndex.from_tuples(tuples))
+ s = pd.Series(np.random.randn(8), index=pd.MultiIndex.from_tuples(tuples))
s
s.sortlevel(0)
s.sortlevel(1)
@@ -509,13 +504,13 @@ an exception. Here is a concrete example to illustrate this:
.. ipython:: python
tuples = [('a', 'a'), ('a', 'b'), ('b', 'a'), ('b', 'b')]
- idx = MultiIndex.from_tuples(tuples)
+ idx = pd.MultiIndex.from_tuples(tuples)
idx.lexsort_depth
reordered = idx[[1, 0, 3, 2]]
reordered.lexsort_depth
- s = Series(randn(4), index=reordered)
+ s = pd.Series(np.random.randn(4), index=reordered)
s.ix['a':'a']
However:
@@ -540,7 +535,7 @@ index positions. ``take`` will also accept negative integers as relative positio
.. ipython:: python
- index = Index(randint(0, 1000, 10))
+ index = pd.Index(np.random.randint(0, 1000, 10))
index
positions = [0, 9, 3]
@@ -548,7 +543,7 @@ index positions. ``take`` will also accept negative integers as relative positio
index[positions]
index.take(positions)
- ser = Series(randn(10))
+ ser = pd.Series(np.random.randn(10))
ser.iloc[positions]
ser.take(positions)
@@ -558,7 +553,7 @@ row or column positions.
.. ipython:: python
- frm = DataFrame(randn(5, 3))
+ frm = pd.DataFrame(np.random.randn(5, 3))
frm.take([1, 4, 3])
@@ -569,11 +564,11 @@ intended to work on boolean indices and may return unexpected results.
.. ipython:: python
- arr = randn(10)
+ arr = np.random.randn(10)
arr.take([False, False, True, True])
arr[[0, 1]]
- ser = Series(randn(10))
+ ser = pd.Series(np.random.randn(10))
ser.take([False, False, True, True])
ser.ix[[0, 1]]
@@ -583,14 +578,14 @@ faster than fancy indexing.
.. ipython::
- arr = randn(10000, 5)
+ arr = np.random.randn(10000, 5)
indexer = np.arange(10000)
random.shuffle(indexer)
timeit arr[indexer]
timeit arr.take(indexer, axis=0)
- ser = Series(arr[:, 0])
+ ser = pd.Series(arr[:, 0])
timeit ser.ix[indexer]
timeit ser.take(indexer)
@@ -608,10 +603,9 @@ setting the index of a ``DataFrame/Series`` with a ``category`` dtype would conv
.. ipython:: python
- df = DataFrame({'A' : np.arange(6),
- 'B' : Series(list('aabbca')).astype('category',
- categories=list('cab'))
- })
+ df = pd.DataFrame({'A': np.arange(6),
+ 'B': list('aabbca')})
+ df['B'] = df['B'].astype('category', categories=list('cab'))
df
df.dtypes
df.B.cat.categories
@@ -669,10 +663,10 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.
.. code-block:: python
- In [10]: df3 = DataFrame({'A' : np.arange(6),
- 'B' : Series(list('aabbca')).astype('category',
- categories=list('abc'))
- }).set_index('B')
+ In [9]: df3 = pd.DataFrame({'A' : np.arange(6),
+ 'B' : pd.Series(list('aabbca')).astype('category')})
+
+ In [11]: df3 = df3.set_index('B')
In [11]: df3.index
Out[11]:
@@ -680,7 +674,7 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index.
categories=[u'a', u'b', u'c'],
ordered=False)
- In [12]: pd.concat([df2,df3]
+ In [12]: pd.concat([df2, df3]
TypeError: categories must match existing categories when appending
.. _indexing.float64index:
@@ -705,9 +699,9 @@ same.
.. ipython:: python
- indexf = Index([1.5, 2, 3, 4.5, 5])
+ indexf = pd.Index([1.5, 2, 3, 4.5, 5])
indexf
- sf = Series(range(5),index=indexf)
+ sf = pd.Series(range(5), index=indexf)
sf
Scalar selection for ``[],.ix,.loc`` will always be label based. An integer will match an equal float index (e.g. ``3`` is equivalent to ``3.0``)
@@ -749,17 +743,17 @@ In non-float indexes, slicing using floats will raise a ``TypeError``
.. code-block:: python
- In [1]: Series(range(5))[3.5]
+ In [1]: pd.Series(range(5))[3.5]
TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index)
- In [1]: Series(range(5))[3.5:4.5]
+ In [1]: pd.Series(range(5))[3.5:4.5]
TypeError: the slice start [3.5] is not a proper indexer for this index type (Int64Index)
Using a scalar float indexer will be deprecated in a future version, but is allowed for now.
.. code-block:: python
- In [3]: Series(range(5))[3.0]
+ In [3]: pd.Series(range(5))[3.0]
Out[3]: 3
Here is a typical use-case for using this type of indexing. Imagine that you have a somewhat
@@ -768,12 +762,12 @@ example be millisecond offsets.
.. ipython:: python
- dfir = concat([DataFrame(randn(5,2),
- index=np.arange(5) * 250.0,
- columns=list('AB')),
- DataFrame(randn(6,2),
- index=np.arange(4,10) * 250.1,
- columns=list('AB'))])
+ dfir = pd.concat([pd.DataFrame(np.random.randn(5,2),
+ index=np.arange(5) * 250.0,
+ columns=list('AB')),
+ pd.DataFrame(np.random.randn(6,2),
+ index=np.arange(4,10) * 250.1,
+ columns=list('AB'))])
dfir
Selection operations then will always work on a value basis, for all selection operators.
diff --git a/doc/source/basics.rst b/doc/source/basics.rst
index 76efdc0553c7d..96372ddab68bc 100644
--- a/doc/source/basics.rst
+++ b/doc/source/basics.rst
@@ -1,16 +1,14 @@
.. currentmodule:: pandas
-.. _basics:
.. ipython:: python
:suppress:
import numpy as np
- from pandas import *
- randn = np.random.randn
+ import pandas as pd
np.set_printoptions(precision=4, suppress=True)
- from pandas.compat import lrange
- options.display.max_rows=15
+ pd.options.display.max_rows = 15
+.. _basics:
==============================
Essential Basic Functionality
@@ -22,13 +20,13 @@ the previous section:
.. ipython:: python
- index = date_range('1/1/2000', periods=8)
- s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
- df = DataFrame(randn(8, 3), index=index,
- columns=['A', 'B', 'C'])
- wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
- major_axis=date_range('1/1/2000', periods=5),
- minor_axis=['A', 'B', 'C', 'D'])
+ index = pd.date_range('1/1/2000', periods=8)
+ s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
+ df = pd.DataFrame(np.random.randn(8, 3), index=index,
+ columns=['A', 'B', 'C'])
+ wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
+ major_axis=pd.date_range('1/1/2000', periods=5),
+ minor_axis=['A', 'B', 'C', 'D'])
.. _basics.head_tail:
@@ -41,7 +39,7 @@ of elements to display is five, but you may pass a custom number.
.. ipython:: python
- long_series = Series(randn(1000))
+ long_series = pd.Series(np.random.randn(1000))
long_series.head()
long_series.tail(3)
@@ -143,9 +141,9 @@ either match on the *index* or *columns* via the **axis** keyword:
.. ipython:: python
- df = DataFrame({'one' : Series(randn(3), index=['a', 'b', 'c']),
- 'two' : Series(randn(4), index=['a', 'b', 'c', 'd']),
- 'three' : Series(randn(3), index=['b', 'c', 'd'])})
+ df = pd.DataFrame({'one' : pd.Series(np.random.randn(3), index=['a', 'b', 'c']),
+ 'two' : pd.Series(np.random.randn(4), index=['a', 'b', 'c', 'd']),
+ 'three' : pd.Series(np.random.randn(3), index=['b', 'c', 'd'])})
df
row = df.ix[1]
column = df['two']
@@ -166,8 +164,8 @@ Furthermore you can align a level of a multi-indexed DataFrame with a Series.
.. ipython:: python
dfmi = df.copy()
- dfmi.index = MultiIndex.from_tuples([(1,'a'),(1,'b'),(1,'c'),(2,'a')],
- names=['first','second'])
+ dfmi.index = pd.MultiIndex.from_tuples([(1,'a'),(1,'b'),(1,'c'),(2,'a')],
+ names=['first','second'])
dfmi.sub(column, axis=0, level='second')
With Panel, describing the matching behavior is a bit more difficult, so
@@ -256,17 +254,17 @@ You can test if a pandas object is empty, via the :attr:`~DataFrame.empty` prope
.. ipython:: python
df.empty
- DataFrame(columns=list('ABC')).empty
+ pd.DataFrame(columns=list('ABC')).empty
To evaluate single-element pandas objects in a boolean context, use the method
:meth:`~DataFrame.bool`:
.. ipython:: python
- Series([True]).bool()
- Series([False]).bool()
- DataFrame([[True]]).bool()
- DataFrame([[False]]).bool()
+ pd.Series([True]).bool()
+ pd.Series([False]).bool()
+ pd.DataFrame([[True]]).bool()
+ pd.DataFrame([[False]]).bool()
.. warning::
@@ -327,8 +325,8 @@ equality to be True:
.. ipython:: python
- df1 = DataFrame({'col':['foo', 0, np.nan]})
- df2 = DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0])
+ df1 = pd.DataFrame({'col':['foo', 0, np.nan]})
+ df2 = pd.DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0])
df1.equals(df2)
df1.equals(df2.sort())
@@ -348,10 +346,10 @@ which we illustrate:
.. ipython:: python
- df1 = DataFrame({'A' : [1., np.nan, 3., 5., np.nan],
- 'B' : [np.nan, 2., 3., np.nan, 6.]})
- df2 = DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.],
- 'B' : [np.nan, np.nan, 3., 4., 6., 8.]})
+ df1 = pd.DataFrame({'A' : [1., np.nan, 3., 5., np.nan],
+ 'B' : [np.nan, 2., 3., np.nan, 6.]})
+ df2 = pd.DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.],
+ 'B' : [np.nan, np.nan, 3., 4., 6., 8.]})
df1
df2
df1.combine_first(df2)
@@ -368,7 +366,7 @@ So, for instance, to reproduce :meth:`~DataFrame.combine_first` as above:
.. ipython:: python
- combiner = lambda x, y: np.where(isnull(x), y, x)
+ combiner = lambda x, y: np.where(pd.isnull(x), y, x)
df1.combine(df2, combiner)
.. _basics.stats:
@@ -467,7 +465,7 @@ number of unique non-null values:
.. ipython:: python
- series = Series(randn(500))
+ series = pd.Series(np.random.randn(500))
series[20:500] = np.nan
series[10:20] = 5
series.nunique()
@@ -483,10 +481,10 @@ course):
.. ipython:: python
- series = Series(randn(1000))
+ series = pd.Series(np.random.randn(1000))
series[::2] = np.nan
series.describe()
- frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
+ frame = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame.ix[::2] = np.nan
frame.describe()
@@ -503,7 +501,7 @@ summary of the number of unique values and most frequently occurring values:
.. ipython:: python
- s = Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])
+ s = pd.Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])
s.describe()
Note that on a mixed-type DataFrame object, :meth:`~DataFrame.describe` will
@@ -512,7 +510,7 @@ categorical columns:
.. ipython:: python
- frame = DataFrame({'a': ['Yes', 'Yes', 'No', 'No'], 'b': range(4)})
+ frame = pd.DataFrame({'a': ['Yes', 'Yes', 'No', 'No'], 'b': range(4)})
frame.describe()
This behaviour can be controlled by providing a list of types as ``include``/``exclude``
@@ -538,11 +536,11 @@ corresponding values:
.. ipython:: python
- s1 = Series(randn(5))
+ s1 = pd.Series(np.random.randn(5))
s1
s1.idxmin(), s1.idxmax()
- df1 = DataFrame(randn(5,3), columns=['A','B','C'])
+ df1 = pd.DataFrame(np.random.randn(5,3), columns=['A','B','C'])
df1
df1.idxmin(axis=0)
df1.idxmax(axis=1)
@@ -553,7 +551,7 @@ matching index:
.. ipython:: python
- df3 = DataFrame([2, 1, 1, 3, np.nan], columns=['A'], index=list('edcba'))
+ df3 = pd.DataFrame([2, 1, 1, 3, np.nan], columns=['A'], index=list('edcba'))
df3
df3['A'].idxmin()
@@ -573,18 +571,18 @@ of a 1D array of values. It can also be used as a function on regular arrays:
data = np.random.randint(0, 7, size=50)
data
- s = Series(data)
+ s = pd.Series(data)
s.value_counts()
- value_counts(data)
+ pd.value_counts(data)
Similarly, you can get the most frequently occurring value(s) (the mode) of the values in a Series or DataFrame:
.. ipython:: python
- s5 = Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
+ s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
s5.mode()
- df5 = DataFrame({"A": np.random.randint(0, 7, size=50),
- "B": np.random.randint(-10, 15, size=50)})
+ df5 = pd.DataFrame({"A": np.random.randint(0, 7, size=50),
+ "B": np.random.randint(-10, 15, size=50)})
df5.mode()
@@ -597,10 +595,10 @@ and :func:`qcut` (bins based on sample quantiles) functions:
.. ipython:: python
arr = np.random.randn(20)
- factor = cut(arr, 4)
+ factor = pd.cut(arr, 4)
factor
- factor = cut(arr, [-5, -1, 0, 1, 5])
+ factor = pd.cut(arr, [-5, -1, 0, 1, 5])
factor
:func:`qcut` computes sample quantiles. For example, we could slice up some
@@ -609,16 +607,16 @@ normally distributed data into equal-size quartiles like so:
.. ipython:: python
arr = np.random.randn(30)
- factor = qcut(arr, [0, .25, .5, .75, 1])
+ factor = pd.qcut(arr, [0, .25, .5, .75, 1])
factor
- value_counts(factor)
+ pd.value_counts(factor)
We can also pass infinite values to define the bins:
.. ipython:: python
arr = np.random.randn(20)
- factor = cut(arr, [-np.inf, 0, np.inf])
+ factor = pd.cut(arr, [-np.inf, 0, np.inf])
factor
.. _basics.apply:
@@ -647,8 +645,8 @@ maximum value for each column occurred:
.. ipython:: python
- tsdf = DataFrame(randn(1000, 3), columns=['A', 'B', 'C'],
- index=date_range('1/1/2000', periods=1000))
+ tsdf = pd.DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C'],
+ index=pd.date_range('1/1/2000', periods=1000))
tsdf.apply(lambda x: x.idxmax())
You may also pass additional arguments and keyword arguments to the :meth:`~DataFrame.apply`
@@ -671,14 +669,14 @@ Series operation on each column or row:
.. ipython:: python
:suppress:
- tsdf = DataFrame(randn(10, 3), columns=['A', 'B', 'C'],
- index=date_range('1/1/2000', periods=10))
+ tsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
+ index=pd.date_range('1/1/2000', periods=10))
tsdf.values[3:7] = np.nan
.. ipython:: python
tsdf
- tsdf.apply(Series.interpolate)
+ tsdf.apply(pd.Series.interpolate)
Finally, :meth:`~DataFrame.apply` takes an argument ``raw`` which is False by default, which
converts each row or column into a Series before applying the function. When
@@ -718,9 +716,9 @@ to :ref:`merging/joining functionality <merging>`:
.. ipython:: python
- s = Series(['six', 'seven', 'six', 'seven', 'six'],
- index=['a', 'b', 'c', 'd', 'e'])
- t = Series({'six' : 6., 'seven' : 7.})
+ s = pd.Series(['six', 'seven', 'six', 'seven', 'six'],
+ index=['a', 'b', 'c', 'd', 'e'])
+ t = pd.Series({'six' : 6., 'seven' : 7.})
s
s.map(t)
@@ -797,7 +795,7 @@ This is equivalent to the following
.. ipython:: python
- result = Panel(dict([ (ax,f(panel.loc[:,:,ax]))
+ result = pd.Panel(dict([ (ax, f(panel.loc[:,:,ax]))
for ax in panel.minor_axis ]))
result
result.loc[:,:,'ItemA']
@@ -823,7 +821,7 @@ Here is a simple example:
.. ipython:: python
- s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
+ s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
s
s.reindex(['e', 'b', 'f', 'd'])
@@ -909,7 +907,7 @@ It returns a tuple with both of the reindexed Series:
.. ipython:: python
- s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
+ s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
s1 = s[:4]
s2 = s[1:]
s1.align(s2)
@@ -960,8 +958,8 @@ We illustrate these fill methods on a simple Series:
.. ipython:: python
- rng = date_range('1/3/2000', periods=8)
- ts = Series(randn(8), index=rng)
+ rng = pd.date_range('1/3/2000', periods=8)
+ ts = pd.Series(np.random.randn(8), index=rng)
ts2 = ts[[0, 3, 6]]
ts
ts2
@@ -1095,11 +1093,11 @@ For instance, a contrived way to transpose the DataFrame would be:
.. ipython:: python
- df2 = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
+ df2 = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
print(df2)
print(df2.T)
- df2_t = DataFrame(dict((idx,values) for idx, values in df2.iterrows()))
+ df2_t = pd.DataFrame(dict((idx,values) for idx, values in df2.iterrows()))
print(df2_t)
.. note::
@@ -1109,7 +1107,7 @@ For instance, a contrived way to transpose the DataFrame would be:
.. ipython:: python
- df_iter = DataFrame([[1, 1.0]], columns=['x', 'y'])
+ df_iter = pd.DataFrame([[1, 1.0]], columns=['x', 'y'])
row = next(df_iter.iterrows())[1]
print(row['x'].dtype)
print(df_iter['x'].dtype)
@@ -1140,7 +1138,7 @@ This will return a Series, indexed like the existing Series.
.. ipython:: python
# datetime
- s = Series(date_range('20130101 09:10:12',periods=4))
+ s = pd.Series(pd.date_range('20130101 09:10:12',periods=4))
s
s.dt.hour
s.dt.second
@@ -1171,7 +1169,7 @@ The ``.dt`` accessor works for period and timedelta dtypes.
.. ipython:: python
# period
- s = Series(period_range('20130101',periods=4,freq='D'))
+ s = pd.Series(pd.period_range('20130101', periods=4,freq='D'))
s
s.dt.year
s.dt.day
@@ -1179,7 +1177,7 @@ The ``.dt`` accessor works for period and timedelta dtypes.
.. ipython:: python
# timedelta
- s = Series(timedelta_range('1 day 00:00:05',periods=4,freq='s'))
+ s = pd.Series(pd.timedelta_range('1 day 00:00:05',periods=4,freq='s'))
s
s.dt.days
s.dt.seconds
@@ -1200,7 +1198,7 @@ built-in string methods. For example:
.. ipython:: python
- s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
+ s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
s.str.lower()
Powerful pattern-matching methods are provided as well, but note that
@@ -1234,7 +1232,7 @@ determine the sort order:
.. ipython:: python
- df1 = DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})
+ df1 = pd.DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})
df1.sort_index(by='two')
The ``by`` argument can take a list of column names, e.g.:
@@ -1265,12 +1263,12 @@ Series has the :meth:`~Series.searchsorted` method, which works similar to
.. ipython:: python
- ser = Series([1, 2, 3])
+ ser = pd.Series([1, 2, 3])
ser.searchsorted([0, 3])
ser.searchsorted([0, 4])
ser.searchsorted([1, 3], side='right')
ser.searchsorted([1, 3], side='left')
- ser = Series([3, 1, 2])
+ ser = pd.Series([3, 1, 2])
ser.searchsorted([0, 3], sorter=np.argsort(ser))
.. _basics.nsorted:
@@ -1286,7 +1284,7 @@ faster than sorting the entire Series and calling ``head(n)`` on the result.
.. ipython:: python
- s = Series(np.random.permutation(10))
+ s = pd.Series(np.random.permutation(10))
s
s.order()
s.nsmallest(3)
@@ -1303,7 +1301,7 @@ all levels to ``by``.
.. ipython:: python
- df1.columns = MultiIndex.from_tuples([('a','one'),('a','two'),('b','three')])
+ df1.columns = pd.MultiIndex.from_tuples([('a','one'),('a','two'),('b','three')])
df1.sort_index(by=('a','two'))
@@ -1336,13 +1334,13 @@ attribute for DataFrames returns a Series with the data type of each column.
.. ipython:: python
- dft = DataFrame(dict( A = np.random.rand(3),
- B = 1,
- C = 'foo',
- D = Timestamp('20010102'),
- E = Series([1.0]*3).astype('float32'),
- F = False,
- G = Series([1]*3,dtype='int8')))
+ dft = pd.DataFrame(dict(A = np.random.rand(3),
+ B = 1,
+ C = 'foo',
+ D = pd.Timestamp('20010102'),
+ E = pd.Series([1.0]*3).astype('float32'),
+ F = False,
+ G = pd.Series([1]*3,dtype='int8')))
dft
dft.dtypes
@@ -1359,10 +1357,10 @@ general).
.. ipython:: python
# these ints are coerced to floats
- Series([1, 2, 3, 4, 5, 6.])
+ pd.Series([1, 2, 3, 4, 5, 6.])
# string data forces an ``object`` dtype
- Series([1, 2, 3, 6., 'foo'])
+ pd.Series([1, 2, 3, 6., 'foo'])
The method :meth:`~DataFrame.get_dtype_counts` will return the number of columns of
each type in a ``DataFrame``:
@@ -1378,12 +1376,12 @@ different numeric dtypes will **NOT** be combined. The following example will gi
.. ipython:: python
- df1 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')
+ df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
df1
df1.dtypes
- df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'),
- B = Series(randn(8)),
- C = Series(np.array(randn(8),dtype='uint8')) ))
+ df2 = pd.DataFrame(dict( A = pd.Series(np.random.randn(8), dtype='float16'),
+ B = pd.Series(np.random.randn(8)),
+ C = pd.Series(np.array(np.random.randn(8), dtype='uint8')) ))
df2
df2.dtypes
@@ -1395,16 +1393,16 @@ By default integer types are ``int64`` and float types are ``float64``,
.. ipython:: python
- DataFrame([1, 2], columns=['a']).dtypes
- DataFrame({'a': [1, 2]}).dtypes
- DataFrame({'a': 1 }, index=list(range(2))).dtypes
+ pd.DataFrame([1, 2], columns=['a']).dtypes
+ pd.DataFrame({'a': [1, 2]}).dtypes
+ pd.DataFrame({'a': 1 }, index=list(range(2))).dtypes
Numpy, however will choose *platform-dependent* types when creating arrays.
The following **WILL** result in ``int32`` on 32-bit platform.
.. ipython:: python
- frame = DataFrame(np.array([1, 2]))
+ frame = pd.DataFrame(np.array([1, 2]))
upcasting
@@ -1473,9 +1471,10 @@ but occasionally has non-dates intermixed and you want to represent as missing.
.. ipython:: python
- s = Series([datetime(2001,1,1,0,0),
- 'foo', 1.0, 1, Timestamp('20010104'),
- '20010105'],dtype='O')
+ import datetime
+ s = pd.Series([datetime.datetime(2001,1,1,0,0),
+ 'foo', 1.0, 1, pd.Timestamp('20010104'),
+ '20010105'], dtype='O')
s
s.convert_objects(convert_dates='coerce')
@@ -1527,14 +1526,14 @@ dtypes:
.. ipython:: python
- df = DataFrame({'string': list('abc'),
- 'int64': list(range(1, 4)),
- 'uint8': np.arange(3, 6).astype('u1'),
- 'float64': np.arange(4.0, 7.0),
- 'bool1': [True, False, True],
- 'bool2': [False, True, False],
- 'dates': pd.date_range('now', periods=3).values,
- 'category': pd.Categorical(list("ABC"))})
+ df = pd.DataFrame({'string': list('abc'),
+ 'int64': list(range(1, 4)),
+ 'uint8': np.arange(3, 6).astype('u1'),
+ 'float64': np.arange(4.0, 7.0),
+ 'bool1': [True, False, True],
+ 'bool2': [False, True, False],
+ 'dates': pd.date_range('now', periods=3).values,
+ 'category': pd.Series(list("ABC")).astype('category')})
df['tdeltas'] = df.dates.diff()
df['uint64'] = np.arange(3, 6).astype('u8')
df['other_dates'] = pd.date_range('20130101', periods=3).values
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst
index 11e7fb0fd4117..85fab1367114e 100644
--- a/doc/source/categorical.rst
+++ b/doc/source/categorical.rst
@@ -6,14 +6,10 @@
:suppress:
import numpy as np
- import random
- import os
- np.random.seed(123456)
- from pandas import options
- from pandas import *
import pandas as pd
+ np.random.seed(123456)
np.set_printoptions(precision=4, suppress=True)
- options.display.max_rows=15
+ pd.options.display.max_rows = 15
****************
@@ -65,14 +61,14 @@ By specifying ``dtype="category"`` when constructing a `Series`:
.. ipython:: python
- s = Series(["a","b","c","a"], dtype="category")
+ s = pd.Series(["a","b","c","a"], dtype="category")
s
By converting an existing `Series` or column to a ``category`` dtype:
.. ipython:: python
- df = DataFrame({"A":["a","b","c","a"]})
+ df = pd.DataFrame({"A":["a","b","c","a"]})
df["B"] = df["A"].astype('category')
df
@@ -80,7 +76,7 @@ By using some special functions:
.. ipython:: python
- df = DataFrame({'value': np.random.randint(0, 100, 20)})
+ df = pd.DataFrame({'value': np.random.randint(0, 100, 20)})
labels = [ "{0} - {1}".format(i, i + 9) for i in range(0, 100, 10) ]
df['group'] = pd.cut(df.value, range(0, 105, 10), right=False, labels=labels)
@@ -92,11 +88,11 @@ By passing a :class:`pandas.Categorical` object to a `Series` or assigning it to
.. ipython:: python
- raw_cat = Categorical(["a","b","c","a"], categories=["b","c","d"],
+ raw_cat = pd.Categorical(["a","b","c","a"], categories=["b","c","d"],
ordered=False)
- s = Series(raw_cat)
+ s = pd.Series(raw_cat)
s
- df = DataFrame({"A":["a","b","c","a"]})
+ df = pd.DataFrame({"A":["a","b","c","a"]})
df["B"] = raw_cat
df
@@ -104,7 +100,7 @@ You can also specify differently ordered categories or make the resulting data o
.. ipython:: python
- s = Series(["a","b","c","a"])
+ s = pd.Series(["a","b","c","a"])
s_cat = s.astype("category", categories=["b","c","d"], ordered=False)
s_cat
@@ -129,7 +125,7 @@ To get back to the original Series or `numpy` array, use ``Series.astype(origina
.. ipython:: python
- s = Series(["a","b","c","a"])
+ s = pd.Series(["a","b","c","a"])
s
s2 = s.astype('category')
s2
@@ -143,7 +139,7 @@ constructor to save the factorize step during normal constructor mode:
.. ipython:: python
splitter = np.random.choice([0,1], 5, p=[0.5,0.5])
- s = Series(Categorical.from_codes(splitter, categories=["train", "test"]))
+ s = pd.Series(pd.Categorical.from_codes(splitter, categories=["train", "test"]))
Description
-----------
@@ -153,8 +149,8 @@ Using ``.describe()`` on categorical data will produce similar output to a `Seri
.. ipython:: python
- cat = Categorical(["a","c","c",np.nan], categories=["b","a","c",np.nan] )
- df = DataFrame({"cat":cat, "s":["a","c","c",np.nan]})
+ cat = pd.Categorical(["a","c","c",np.nan], categories=["b","a","c",np.nan] )
+ df = pd.DataFrame({"cat":cat, "s":["a","c","c",np.nan]})
df.describe()
df["cat"].describe()
@@ -168,7 +164,7 @@ passed in values.
.. ipython:: python
- s = Series(["a","b","c","a"], dtype="category")
+ s = pd.Series(["a","b","c","a"], dtype="category")
s.cat.categories
s.cat.ordered
@@ -176,7 +172,7 @@ It's also possible to pass in the categories in a specific order:
.. ipython:: python
- s = Series(Categorical(["a","b","c","a"], categories=["c","b","a"]))
+ s = pd.Series(pd.Categorical(["a","b","c","a"], categories=["c","b","a"]))
s.cat.categories
s.cat.ordered
@@ -194,7 +190,7 @@ by using the :func:`Categorical.rename_categories` method:
.. ipython:: python
- s = Series(["a","b","c","a"], dtype="category")
+ s = pd.Series(["a","b","c","a"], dtype="category")
s
s.cat.categories = ["Group %s" % g for g in s.cat.categories]
s
@@ -247,7 +243,7 @@ Removing unused categories can also be done:
.. ipython:: python
- s = Series(Categorical(["a","b","a"], categories=["a","b","c","d"]))
+ s = pd.Series(pd.Categorical(["a","b","a"], categories=["a","b","c","d"]))
s
s.cat.remove_unused_categories()
@@ -259,7 +255,7 @@ or simply set the categories to a predefined scale, use :func:`Categorical.set_c
.. ipython:: python
- s = Series(["one","two","four", "-"], dtype="category")
+ s = pd.Series(["one","two","four", "-"], dtype="category")
s
s = s.cat.set_categories(["one","two","three","four"])
s
@@ -283,9 +279,9 @@ meaning and certain operations are possible. If the categorical is unordered, ``
.. ipython:: python
- s = Series(Categorical(["a","b","c","a"], ordered=False))
+ s = pd.Series(pd.Categorical(["a","b","c","a"], ordered=False))
s.sort()
- s = Series(["a","b","c","a"]).astype('category', ordered=True)
+ s = pd.Series(["a","b","c","a"]).astype('category', ordered=True)
s.sort()
s
s.min(), s.max()
@@ -303,7 +299,7 @@ This is even true for strings and numeric data:
.. ipython:: python
- s = Series([1,2,3,1], dtype="category")
+ s = pd.Series([1,2,3,1], dtype="category")
s = s.cat.set_categories([2,3,1], ordered=True)
s
s.sort()
@@ -321,7 +317,7 @@ necessarily make the sort order the same as the categories order.
.. ipython:: python
- s = Series([1,2,3,1], dtype="category")
+ s = pd.Series([1,2,3,1], dtype="category")
s = s.cat.reorder_categories([2,3,1], ordered=True)
s
s.sort()
@@ -351,8 +347,8 @@ The ordering of the categorical is determined by the ``categories`` of that colu
.. ipython:: python
- dfs = DataFrame({'A' : Categorical(list('bbeebbaa'), categories=['e','a','b'], ordered=True),
- 'B' : [1,2,1,2,2,1,2,1] })
+ dfs = pd.DataFrame({'A' : pd.Categorical(list('bbeebbaa'), categories=['e','a','b'], ordered=True),
+ 'B' : [1,2,1,2,2,1,2,1] })
dfs.sort(['A', 'B'])
Reordering the ``categories`` changes a future sort.
@@ -385,9 +381,9 @@ categories or a categorical with any list-like object, will raise a TypeError.
.. ipython:: python
- cat = Series([1,2,3]).astype("category", categories=[3,2,1], ordered=True)
- cat_base = Series([2,2,2]).astype("category", categories=[3,2,1], ordered=True)
- cat_base2 = Series([2,2,2]).astype("category", ordered=True)
+ cat = pd.Series([1,2,3]).astype("category", categories=[3,2,1], ordered=True)
+ cat_base = pd.Series([2,2,2]).astype("category", categories=[3,2,1], ordered=True)
+ cat_base2 = pd.Series([2,2,2]).astype("category", ordered=True)
cat
cat_base
@@ -443,19 +439,19 @@ present in the data:
.. ipython:: python
- s = Series(Categorical(["a","b","c","c"], categories=["c","a","b","d"]))
+ s = pd.Series(pd.Categorical(["a","b","c","c"], categories=["c","a","b","d"]))
s.value_counts()
Groupby will also show "unused" categories:
.. ipython:: python
- cats = Categorical(["a","b","b","b","c","c","c"], categories=["a","b","c","d"])
- df = DataFrame({"cats":cats,"values":[1,2,2,2,3,4,5]})
+ cats = pd.Categorical(["a","b","b","b","c","c","c"], categories=["a","b","c","d"])
+ df = pd.DataFrame({"cats":cats,"values":[1,2,2,2,3,4,5]})
df.groupby("cats").mean()
- cats2 = Categorical(["a","a","b","b"], categories=["a","b","c"])
- df2 = DataFrame({"cats":cats2,"B":["c","d","c","d"], "values":[1,2,3,4]})
+ cats2 = pd.Categorical(["a","a","b","b"], categories=["a","b","c"])
+ df2 = pd.DataFrame({"cats":cats2,"B":["c","d","c","d"], "values":[1,2,3,4]})
df2.groupby(["cats","B"]).mean()
@@ -463,8 +459,8 @@ Pivot tables:
.. ipython:: python
- raw_cat = Categorical(["a","a","b","b"], categories=["a","b","c"])
- df = DataFrame({"A":raw_cat,"B":["c","d","c","d"], "values":[1,2,3,4]})
+ raw_cat = pd.Categorical(["a","a","b","b"], categories=["a","b","c"])
+ df = pd.DataFrame({"A":raw_cat,"B":["c","d","c","d"], "values":[1,2,3,4]})
pd.pivot_table(df, values='values', index=['A', 'B'])
Data munging
@@ -482,10 +478,10 @@ the ``category`` dtype is preserved.
.. ipython:: python
- idx = Index(["h","i","j","k","l","m","n",])
- cats = Series(["a","b","b","b","c","c","c"], dtype="category", index=idx)
+ idx = pd.Index(["h","i","j","k","l","m","n",])
+ cats = pd.Series(["a","b","b","b","c","c","c"], dtype="category", index=idx)
values= [1,2,2,2,3,4,5]
- df = DataFrame({"cats":cats,"values":values}, index=idx)
+ df = pd.DataFrame({"cats":cats,"values":values}, index=idx)
df.iloc[2:4,:]
df.iloc[2:4,:].dtypes
df.loc["h":"j","cats"]
@@ -527,10 +523,10 @@ Setting values in a categorical column (or `Series`) works as long as the value
.. ipython:: python
- idx = Index(["h","i","j","k","l","m","n"])
- cats = Categorical(["a","a","a","a","a","a","a"], categories=["a","b"])
+ idx = pd.Index(["h","i","j","k","l","m","n"])
+ cats = pd.Categorical(["a","a","a","a","a","a","a"], categories=["a","b"])
values = [1,1,1,1,1,1,1]
- df = DataFrame({"cats":cats,"values":values}, index=idx)
+ df = pd.DataFrame({"cats":cats,"values":values}, index=idx)
df.iloc[2:4,:] = [["b",2],["b",2]]
df
@@ -543,10 +539,10 @@ Setting values by assigning categorical data will also check that the `categorie
.. ipython:: python
- df.loc["j":"k","cats"] = Categorical(["a","a"], categories=["a","b"])
+ df.loc["j":"k","cats"] = pd.Categorical(["a","a"], categories=["a","b"])
df
try:
- df.loc["j":"k","cats"] = Categorical(["b","b"], categories=["a","b","c"])
+ df.loc["j":"k","cats"] = pd.Categorical(["b","b"], categories=["a","b","c"])
except ValueError as e:
print("ValueError: " + str(e))
@@ -554,9 +550,9 @@ Assigning a `Categorical` to parts of a column of other types will use the value
.. ipython:: python
- df = DataFrame({"a":[1,1,1,1,1], "b":["a","a","a","a","a"]})
- df.loc[1:2,"a"] = Categorical(["b","b"], categories=["a","b"])
- df.loc[2:3,"b"] = Categorical(["b","b"], categories=["a","b"])
+ df = pd.DataFrame({"a":[1,1,1,1,1], "b":["a","a","a","a","a"]})
+ df.loc[1:2,"a"] = pd.Categorical(["b","b"], categories=["a","b"])
+ df.loc[2:3,"b"] = pd.Categorical(["b","b"], categories=["a","b"])
df
df.dtypes
@@ -569,9 +565,9 @@ but the categories of these categoricals need to be the same:
.. ipython:: python
- cat = Series(["a","b"], dtype="category")
+ cat = pd.Series(["a","b"], dtype="category")
vals = [1,2]
- df = DataFrame({"cats":cat, "vals":vals})
+ df = pd.DataFrame({"cats":cat, "vals":vals})
res = pd.concat([df,df])
res
res.dtypes
@@ -611,12 +607,12 @@ relevant columns back to `category` and assign the right categories and categori
.. ipython:: python
- s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'd']))
+ s = pd.Series(pd.Categorical(['a', 'b', 'b', 'a', 'a', 'd']))
# rename the categories
s.cat.categories = ["very good", "good", "bad"]
# reorder the categories and add missing categories
s = s.cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
- df = DataFrame({"cats":s, "vals":[1,2,3,4,5,6]})
+ df = pd.DataFrame({"cats":s, "vals":[1,2,3,4,5,6]})
csv = StringIO()
df.to_csv(csv)
df2 = pd.read_csv(StringIO(csv.getvalue()))
@@ -643,10 +639,10 @@ available ("missing value") or `np.nan` is a valid category.
.. ipython:: python
- s = Series(["a","b",np.nan,"a"], dtype="category")
+ s = pd.Series(["a","b",np.nan,"a"], dtype="category")
# only two categories
s
- s2 = Series(["a","b","c","a"], dtype="category")
+ s2 = pd.Series(["a","b","c","a"], dtype="category")
s2.cat.categories = [1,2,np.nan]
# three categories, np.nan included
s2
@@ -660,11 +656,11 @@ available ("missing value") or `np.nan` is a valid category.
.. ipython:: python
- c = Series(["a","b",np.nan], dtype="category")
+ c = pd.Series(["a","b",np.nan], dtype="category")
c.cat.set_categories(["a","b",np.nan], inplace=True)
# will be inserted as a NA category:
c[0] = np.nan
- s = Series(c)
+ s = pd.Series(c)
s
pd.isnull(s)
s.fillna("a")
@@ -697,7 +693,7 @@ an ``object`` dtype is a constant times the length of the data.
.. ipython:: python
- s = Series(['foo','bar']*1000)
+ s = pd.Series(['foo','bar']*1000)
# object dtype
s.nbytes
@@ -712,7 +708,7 @@ an ``object`` dtype is a constant times the length of the data.
.. ipython:: python
- s = Series(['foo%04d' % i for i in range(2000)])
+ s = pd.Series(['foo%04d' % i for i in range(2000)])
# object dtype
s.nbytes
@@ -734,7 +730,7 @@ will work with the current pandas version, resulting in subtle bugs:
.. code-block:: python
- >>> cat = Categorical([1,2], [1,2,3])
+ >>> cat = pd.Categorical([1,2], [1,2,3])
>>> # old version
>>> cat.get_values()
array([2, 3], dtype=int64)
@@ -762,7 +758,7 @@ object and not as a low-level `numpy` array dtype. This leads to some problems.
except TypeError as e:
print("TypeError: " + str(e))
- dtype = Categorical(["a"]).dtype
+ dtype = pd.Categorical(["a"]).dtype
try:
np.dtype(dtype)
except TypeError as e:
@@ -780,15 +776,15 @@ To check if a Series contains Categorical data, with pandas 0.16 or later, use
.. ipython:: python
- hasattr(Series(['a'], dtype='category'), 'cat')
- hasattr(Series(['a']), 'cat')
+ hasattr(pd.Series(['a'], dtype='category'), 'cat')
+ hasattr(pd.Series(['a']), 'cat')
Using `numpy` functions on a `Series` of type ``category`` should not work as `Categoricals`
are not numeric data (even in the case that ``.categories`` is numeric).
.. ipython:: python
- s = Series(Categorical([1,2,3,4]))
+ s = pd.Series(pd.Categorical([1,2,3,4]))
try:
np.sum(s)
#same with np.log(s),..
@@ -807,9 +803,9 @@ basic type) and applying along columns will also convert to object.
.. ipython:: python
- df = DataFrame({"a":[1,2,3,4],
- "b":["a","b","c","d"],
- "cats":Categorical([1,2,3,2])})
+ df = pd.DataFrame({"a":[1,2,3,4],
+ "b":["a","b","c","d"],
+ "cats":pd.Categorical([1,2,3,2])})
df.apply(lambda row: type(row["cats"]), axis=1)
df.apply(lambda col: col.dtype, axis=0)
@@ -822,10 +818,10 @@ ordering of the categories:
.. ipython:: python
- cats = Categorical([1,2,3,4], categories=[4,2,3,1])
+ cats = pd.Categorical([1,2,3,4], categories=[4,2,3,1])
strings = ["a","b","c","d"]
values = [4,2,3,1]
- df = DataFrame({"strings":strings, "values":values}, index=cats)
+ df = pd.DataFrame({"strings":strings, "values":values}, index=cats)
df.index
# This should sort by categories but does not as there is no CategoricalIndex!
df.sort_index()
@@ -843,12 +839,12 @@ means that changes to the `Series` will in most cases change the original `Categ
.. ipython:: python
- cat = Categorical([1,2,3,10], categories=[1,2,3,4,10])
- s = Series(cat, name="cat")
+ cat = pd.Categorical([1,2,3,10], categories=[1,2,3,4,10])
+ s = pd.Series(cat, name="cat")
cat
s.iloc[0:2] = 10
cat
- df = DataFrame(s)
+ df = pd.DataFrame(s)
df["cat"].cat.categories = [1,2,3,4,5]
cat
@@ -856,8 +852,8 @@ Use ``copy=True`` to prevent such a behaviour or simply don't reuse `Categorical
.. ipython:: python
- cat = Categorical([1,2,3,10], categories=[1,2,3,4,10])
- s = Series(cat, name="cat", copy=True)
+ cat = pd.Categorical([1,2,3,10], categories=[1,2,3,4,10])
+ s = pd.Series(cat, name="cat", copy=True)
cat
s.iloc[0:2] = 10
cat
diff --git a/doc/source/computation.rst b/doc/source/computation.rst
index 4621d7bd9b216..dfb9fab19bf31 100644
--- a/doc/source/computation.rst
+++ b/doc/source/computation.rst
@@ -258,7 +258,7 @@ These functions can be applied to ndarrays or Series objects:
ts.plot(style='k--')
@savefig rolling_mean_ex.png
- rolling_mean(ts, 60).plot(style='k')
+ pd.rolling_mean(ts, 60).plot(style='k')
They can also be applied to DataFrame objects. This is really just syntactic
sugar for applying the moving window operator to all of the DataFrame's columns:
@@ -275,7 +275,7 @@ sugar for applying the moving window operator to all of the DataFrame's columns:
df = df.cumsum()
@savefig rolling_mean_frame.png
- rolling_sum(df, 60).plot(subplots=True)
+ pd.rolling_sum(df, 60).plot(subplots=True)
The ``rolling_apply`` function takes an extra ``func`` argument and performs
generic rolling computations. The ``func`` argument should be a single function
@@ -286,7 +286,7 @@ compute the mean absolute deviation on a rolling basis:
mad = lambda x: np.fabs(x - x.mean()).mean()
@savefig rolling_apply_ex.png
- rolling_apply(ts, 60, mad).plot(style='k')
+ pd.rolling_apply(ts, 60, mad).plot(style='k')
The ``rolling_window`` function performs a generic rolling window computation
on the input data. The weights used in the window are specified by the ``win_type``
@@ -311,21 +311,21 @@ keyword. The list of recognized types are:
ser = pd.Series(np.random.randn(10), index=pd.date_range('1/1/2000', periods=10))
- rolling_window(ser, 5, 'triang')
+ pd.rolling_window(ser, 5, 'triang')
Note that the ``boxcar`` window is equivalent to ``rolling_mean``.
.. ipython:: python
- rolling_window(ser, 5, 'boxcar')
+ pd.rolling_window(ser, 5, 'boxcar')
- rolling_mean(ser, 5)
+ pd.rolling_mean(ser, 5)
For some windowing functions, additional parameters must be specified:
.. ipython:: python
- rolling_window(ser, 5, 'gaussian', std=0.1)
+ pd.rolling_window(ser, 5, 'gaussian', std=0.1)
By default the labels are set to the right edge of the window, but a
``center`` keyword is available so the labels can be set at the center.
@@ -333,11 +333,11 @@ This keyword is available in other rolling functions as well.
.. ipython:: python
- rolling_window(ser, 5, 'boxcar')
+ pd.rolling_window(ser, 5, 'boxcar')
- rolling_window(ser, 5, 'boxcar', center=True)
+ pd.rolling_window(ser, 5, 'boxcar', center=True)
- rolling_mean(ser, 5, center=True)
+ pd.rolling_mean(ser, 5, center=True)
.. _stats.moments.normalization:
@@ -376,7 +376,7 @@ For example:
.. ipython:: python
df2 = df[:20]
- rolling_corr(df2, df2['B'], window=5)
+ pd.rolling_corr(df2, df2['B'], window=5)
.. _stats.moments.corr_pairwise:
@@ -401,12 +401,12 @@ can even be omitted:
.. ipython:: python
- covs = rolling_cov(df[['B','C','D']], df[['A','B','C']], 50, pairwise=True)
+ covs = pd.rolling_cov(df[['B','C','D']], df[['A','B','C']], 50, pairwise=True)
covs[df.index[-50]]
.. ipython:: python
- correls = rolling_corr(df, 50)
+ correls = pd.rolling_corr(df, 50)
correls[df.index[-50]]
.. note::
@@ -440,9 +440,9 @@ they are implemented in pandas such that the following two calls are equivalent:
.. ipython:: python
- rolling_mean(df, window=len(df), min_periods=1)[:5]
+ pd.rolling_mean(df, window=len(df), min_periods=1)[:5]
- expanding_mean(df)[:5]
+ pd.expanding_mean(df)[:5]
Like the ``rolling_`` functions, the following methods are included in the
``pandas`` namespace or can be located in ``pandas.stats.moments``.
@@ -501,7 +501,7 @@ relative impact of an individual data point. As an example, here is the
ts.plot(style='k--')
@savefig expanding_mean_frame.png
- expanding_mean(ts).plot(style='k')
+ pd.expanding_mean(ts).plot(style='k')
.. _stats.moments.exponentially_weighted:
@@ -583,7 +583,7 @@ Here is an example for a univariate time series:
ts.plot(style='k--')
@savefig ewma_ex.png
- ewma(ts, span=20).plot(style='k')
+ pd.ewma(ts, span=20).plot(style='k')
All the EW functions have a ``min_periods`` argument, which has the same
meaning it does for all the ``expanding_`` and ``rolling_`` functions:
| Further work on #9886
| https://api.github.com/repos/pandas-dev/pandas/pulls/10136 | 2015-05-14T15:53:07Z | 2015-05-19T21:21:48Z | 2015-05-19T21:21:48Z | 2015-06-02T19:26:11Z |
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