after_merge
stringlengths 28
79.6k
| before_merge
stringlengths 20
79.6k
| url
stringlengths 38
71
| full_traceback
stringlengths 43
922k
| traceback_type
stringclasses 555
values |
|---|---|---|---|---|
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
if items.is_unique:
for block in self.blocks:
indexer = items.get_indexer(block.items)
assert (indexer != -1).all()
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
else:
for block in self.blocks:
mask = items.isin(block.items)
indexer = mask.nonzero()[0]
assert len(indexer) == len(block.items)
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
assert itemmask.all()
return result
|
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
for block in self.blocks:
indexer = items.get_indexer(block.items)
assert (indexer != -1).all()
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
assert itemmask.all()
return result
|
https://github.com/pandas-dev/pandas/issues/2236
|
import pandas as pd
def runNow():
#identify sheet
source = 'C:\Users\jlalonde\Desktop\startup_geno\startupgenome_w_id_xl_20121109.xlsx'
xls_file = pd.ExcelFile(source)
sd = xls_file.parse('Sheet1')
source_u = sd.drop_duplicates(cols = 'id_company', take_last=False)
source_r = source_u[['id_company','id_good','description', 'website','keyword', 'company_name','founded_month', 'founded_year', 'description']]
source_i = source_r.reindex() #hail mary
tup_r = [tuple(x) for x in source_i.values]
Traceback (most recent call last):
File "<pyshell#10>", line 1, in <module>
sg_sql_2.runNow()
File "sg_sql_2.py", line 31, in runNow
tup_r = [tuple(x) for x in source_r.values]
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1443, in as_matrix
return self._data.as_matrix(columns).T
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 723, in as_matrix
mat = self._interleave(self.items)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 743, in _interleave
indexer = items.get_indexer(block.items)
File "C:\Python27\lib\site-packages\pandas\core\index.py", line 748, in get_indexer
raise Exception('Reindexing only valid with uniquely valued Index '
Exception: Reindexing only valid with uniquely valued Index objects
|
Exception
|
def _isnull_ndarraylike(obj):
from pandas import Series
values = np.asarray(obj)
if values.dtype.kind in ("O", "S", "U"):
# Working around NumPy ticket 1542
shape = values.shape
if values.dtype.kind in ("S", "U"):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(values.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif values.dtype == np.dtype("M8[ns]"):
# this is the NaT pattern
result = values.view("i8") == lib.iNaT
elif issubclass(values.dtype.type, np.timedelta64):
result = -np.isfinite(values.view("i8"))
else:
result = -np.isfinite(obj)
return result
|
def _isnull_ndarraylike(obj):
from pandas import Series
values = np.asarray(obj)
if values.dtype.kind in ("O", "S", "U"):
# Working around NumPy ticket 1542
shape = values.shape
if values.dtype.kind in ("S", "U"):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(values.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif values.dtype == np.dtype("M8[ns]"):
# this is the NaT pattern
result = values.view("i8") == lib.iNaT
else:
result = -np.isfinite(obj)
return result
|
https://github.com/pandas-dev/pandas/issues/2146
|
import pandas as pd
import numpy as np
print pd.Series(np.array([1100, 20], dtype='timedelta64[s]'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 911, in __str__
return repr(self)
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 847, in __repr__
name=True)
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 908, in _get_repr
return formatter.to_string()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 126, in to_string
fmt_values = self._get_formatted_values()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 117, in _get_formatted_values
na_rep=self.na_rep)
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 691, in format_array
return fmt_obj.get_result()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 814, in get_result
fmt_values = [formatter(x) for x in self.values]
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 812, in <lambda>
formatter = lambda x: '% d' % x
TypeError: %d format: a number is required, not numpy.timedelta64
|
TypeError
|
def is_integer_dtype(arr_or_dtype):
if isinstance(arr_or_dtype, np.dtype):
tipo = arr_or_dtype.type
else:
tipo = arr_or_dtype.dtype.type
return issubclass(tipo, np.integer) and not (
issubclass(tipo, np.datetime64) or issubclass(tipo, np.timedelta64)
)
|
def is_integer_dtype(arr_or_dtype):
if isinstance(arr_or_dtype, np.dtype):
tipo = arr_or_dtype.type
else:
tipo = arr_or_dtype.dtype.type
return issubclass(tipo, np.integer) and not issubclass(tipo, np.datetime64)
|
https://github.com/pandas-dev/pandas/issues/2146
|
import pandas as pd
import numpy as np
print pd.Series(np.array([1100, 20], dtype='timedelta64[s]'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 911, in __str__
return repr(self)
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 847, in __repr__
name=True)
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 908, in _get_repr
return formatter.to_string()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 126, in to_string
fmt_values = self._get_formatted_values()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 117, in _get_formatted_values
na_rep=self.na_rep)
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 691, in format_array
return fmt_obj.get_result()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 814, in get_result
fmt_values = [formatter(x) for x in self.values]
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 812, in <lambda>
formatter = lambda x: '% d' % x
TypeError: %d format: a number is required, not numpy.timedelta64
|
TypeError
|
def __new__(
cls,
data=None,
freq=None,
start=None,
end=None,
periods=None,
copy=False,
name=None,
tz=None,
verify_integrity=True,
normalize=False,
**kwds,
):
dayfirst = kwds.pop("dayfirst", None)
yearfirst = kwds.pop("yearfirst", None)
warn = False
if "offset" in kwds and kwds["offset"]:
freq = kwds["offset"]
warn = True
freq_infer = False
if not isinstance(freq, DateOffset):
if freq != "infer":
freq = to_offset(freq)
else:
freq_infer = True
freq = None
if warn:
import warnings
warnings.warn(
"parameter 'offset' is deprecated, please use 'freq' instead", FutureWarning
)
offset = freq
if periods is not None:
if com.is_float(periods):
periods = int(periods)
elif not com.is_integer(periods):
raise ValueError("Periods must be a number, got %s" % str(periods))
if data is None and offset is None:
raise ValueError("Must provide freq argument if no data is supplied")
if data is None:
return cls._generate(
start, end, periods, name, offset, tz=tz, normalize=normalize
)
if not isinstance(data, np.ndarray):
if np.isscalar(data):
raise ValueError(
"DatetimeIndex() must be called with a "
"collection of some kind, %s was passed" % repr(data)
)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data, dtype="O")
# try a few ways to make it datetime64
if lib.is_string_array(data):
data = _str_to_dt_array(
data, offset, dayfirst=dayfirst, yearfirst=yearfirst
)
else:
data = tools.to_datetime(data)
data.offset = offset
if isinstance(data, DatetimeIndex):
if name is not None:
data.name = name
return data
if issubclass(data.dtype.type, basestring):
subarr = _str_to_dt_array(data, offset, dayfirst=dayfirst, yearfirst=yearfirst)
elif issubclass(data.dtype.type, np.datetime64):
if isinstance(data, DatetimeIndex):
if tz is None:
tz = data.tz
subarr = data.values
if offset is None:
offset = data.offset
verify_integrity = False
else:
if data.dtype != _NS_DTYPE:
subarr = lib.cast_to_nanoseconds(data)
else:
subarr = data
elif data.dtype == _INT64_DTYPE:
if isinstance(data, Int64Index):
raise TypeError("cannot convert Int64Index->DatetimeIndex")
if copy:
subarr = np.asarray(data, dtype=_NS_DTYPE)
else:
subarr = data.view(_NS_DTYPE)
else:
try:
subarr = tools.to_datetime(data)
except ValueError:
# tz aware
subarr = tools.to_datetime(data, utc=True)
if not np.issubdtype(subarr.dtype, np.datetime64):
raise TypeError("Unable to convert %s to datetime dtype" % str(data))
if isinstance(subarr, DatetimeIndex):
if tz is None:
tz = subarr.tz
else:
if tz is not None:
tz = tools._maybe_get_tz(tz)
# Convert local to UTC
ints = subarr.view("i8")
subarr = lib.tz_localize_to_utc(ints, tz)
subarr = subarr.view(_NS_DTYPE)
subarr = subarr.view(cls)
subarr.name = name
subarr.offset = offset
subarr.tz = tz
if verify_integrity and len(subarr) > 0:
if offset is not None and not freq_infer:
inferred = subarr.inferred_freq
if inferred != offset.freqstr:
raise ValueError("Dates do not conform to passed frequency")
if freq_infer:
inferred = subarr.inferred_freq
if inferred:
subarr.offset = to_offset(inferred)
return subarr
|
def __new__(
cls,
data=None,
freq=None,
start=None,
end=None,
periods=None,
copy=False,
name=None,
tz=None,
verify_integrity=True,
normalize=False,
**kwds,
):
dayfirst = kwds.pop("dayfirst", None)
yearfirst = kwds.pop("yearfirst", None)
warn = False
if "offset" in kwds and kwds["offset"]:
freq = kwds["offset"]
warn = True
freq_infer = False
if not isinstance(freq, DateOffset):
if freq != "infer":
freq = to_offset(freq)
else:
freq_infer = True
freq = None
if warn:
import warnings
warnings.warn(
"parameter 'offset' is deprecated, please use 'freq' instead", FutureWarning
)
offset = freq
if periods is not None:
if com.is_float(periods):
periods = int(periods)
elif not com.is_integer(periods):
raise ValueError("Periods must be a number, got %s" % str(periods))
if data is None and offset is None:
raise ValueError("Must provide freq argument if no data is supplied")
if data is None:
return cls._generate(
start, end, periods, name, offset, tz=tz, normalize=normalize
)
if not isinstance(data, np.ndarray):
if np.isscalar(data):
raise ValueError(
"DatetimeIndex() must be called with a "
"collection of some kind, %s was passed" % repr(data)
)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data, dtype="O")
# try a few ways to make it datetime64
if lib.is_string_array(data):
data = _str_to_dt_array(
data, offset, dayfirst=dayfirst, yearfirst=yearfirst
)
else:
data = tools.to_datetime(data)
data.offset = offset
if isinstance(data, DatetimeIndex):
if name is not None:
data.name = name
return data
if issubclass(data.dtype.type, basestring):
subarr = _str_to_dt_array(data, offset, dayfirst=dayfirst, yearfirst=yearfirst)
elif issubclass(data.dtype.type, np.datetime64):
if isinstance(data, DatetimeIndex):
if tz is None:
tz = data.tz
subarr = data.values
if offset is None:
offset = data.offset
verify_integrity = False
else:
if data.dtype != _NS_DTYPE:
subarr = lib.cast_to_nanoseconds(data)
else:
subarr = data
elif data.dtype == _INT64_DTYPE:
if copy:
subarr = np.asarray(data, dtype=_NS_DTYPE)
else:
subarr = data.view(_NS_DTYPE)
else:
try:
subarr = tools.to_datetime(data)
except ValueError:
# tz aware
subarr = tools.to_datetime(data, utc=True)
if not np.issubdtype(subarr.dtype, np.datetime64):
raise TypeError("Unable to convert %s to datetime dtype" % str(data))
if isinstance(subarr, DatetimeIndex):
if tz is None:
tz = subarr.tz
else:
if tz is not None:
tz = tools._maybe_get_tz(tz)
# Convert local to UTC
ints = subarr.view("i8")
subarr = lib.tz_localize_to_utc(ints, tz)
subarr = subarr.view(_NS_DTYPE)
subarr = subarr.view(cls)
subarr.name = name
subarr.offset = offset
subarr.tz = tz
if verify_integrity and len(subarr) > 0:
if offset is not None and not freq_infer:
inferred = subarr.inferred_freq
if inferred != offset.freqstr:
raise ValueError("Dates do not conform to passed frequency")
if freq_infer:
inferred = subarr.inferred_freq
if inferred:
subarr.offset = to_offset(inferred)
return subarr
|
https://github.com/pandas-dev/pandas/issues/1866
|
building [html]: targets for 22 source files that are out of date
updating environment: 218 added, 0 changed, 0 removed
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-767-cc6f0e841b62> in <module>()
1 df = read_csv('tmp.csv', header=None, parse_dates=date_spec,
----> 2 date_parser=conv.parse_date_time)
/home/wesm/code/pandas/pandas/io/parsers.pyc in read_csv(filepath_or_buffer, sep, dialect, header, index_col, names, skiprows, na_values, keep_default_na, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding, squeeze)
239 kwds['delimiter'] = sep
240
--> 241 return _read(TextParser, filepath_or_buffer, kwds)
242
243 @Appender(_read_table_doc)
/home/wesm/code/pandas/pandas/io/parsers.pyc in _read(cls, filepath_or_buffer, kwds)
192 return parser
193
--> 194 return parser.get_chunk()
195
196 @Appender(_read_csv_doc)
/home/wesm/code/pandas/pandas/io/parsers.pyc in get_chunk(self, rows)
812 columns = list(self.columns)
813 if self.parse_dates is not None:
--> 814 data, columns = self._process_date_conversion(data)
815
816 df = DataFrame(data=data, columns=columns, index=index)
/home/wesm/code/pandas/pandas/io/parsers.pyc in _process_date_conversion(self, data_dict)
989
990 _, col, old_names = _try_convert_dates(
--> 991 self._conv_date, colspec, data_dict, self.orig_columns)
992
993 new_data[new_name] = col
/home/wesm/code/pandas/pandas/io/parsers.pyc in _try_convert_dates(parser, colspec, data_dict, columns)
1120 to_parse = [data_dict[c] for c in colspec if c in data_dict]
1121 try:
-> 1122 new_col = parser(*to_parse)
1123 except DateConversionError:
1124 new_col = parser(_concat_date_cols(to_parse))
/home/wesm/code/pandas/pandas/io/parsers.pyc in _conv_date(self, *date_cols)
951 return lib.try_parse_dates(_concat_date_cols(date_cols),
952 parser=self.date_parser,
--> 953 dayfirst=self.dayfirst)
954
955 def _process_date_conversion(self, data_dict):
/home/wesm/code/pandas/pandas/lib.so in pandas.lib.try_parse_dates (pandas/src/tseries.c:98928)()
/home/wesm/code/pandas/pandas/lib.so in pandas.lib.try_parse_dates (pandas/src/tseries.c:98870)()
TypeError: parse_date_time() takes exactly 2 arguments (1 given)
reading sources... [100%] whatsnew
/home/wesm/code/pandas/doc/source/faq.rst:74: ERROR: Unknown interpreted text role "issue".
source/v0.8.0.txt:151: WARNING: Inline literal start-st
|
TypeError
|
def take_2d_multi(arr, row_idx, col_idx, fill_value=np.nan, out=None):
dtype_str = arr.dtype.name
out_shape = len(row_idx), len(col_idx)
if dtype_str in ("int32", "int64", "bool"):
row_mask = row_idx == -1
col_mask = col_idx == -1
needs_masking = row_mask.any() or col_mask.any()
if needs_masking:
return take_2d_multi(
_maybe_upcast(arr), row_idx, col_idx, fill_value=fill_value, out=out
)
else:
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis="multi")
take_f(
arr,
_ensure_int64(row_idx),
_ensure_int64(col_idx),
out=out,
fill_value=fill_value,
)
return out
elif dtype_str in ("float64", "object", "datetime64[ns]"):
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis="multi")
take_f(
arr,
_ensure_int64(row_idx),
_ensure_int64(col_idx),
out=out,
fill_value=fill_value,
)
return out
else:
if out is not None:
raise ValueError("Cannot pass out in this case")
return take_2d(
take_2d(arr, row_idx, axis=0, fill_value=fill_value),
col_idx,
axis=1,
fill_value=fill_value,
)
|
def take_2d_multi(arr, row_idx, col_idx, fill_value=np.nan):
dtype_str = arr.dtype.name
out_shape = len(row_idx), len(col_idx)
if dtype_str in ("int32", "int64", "bool"):
row_mask = row_idx == -1
col_mask = col_idx == -1
needs_masking = row_mask.any() or col_mask.any()
if needs_masking:
return take_2d_multi(
_maybe_upcast(arr), row_idx, col_idx, fill_value=fill_value
)
else:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis="multi")
take_f(
arr,
_ensure_int64(row_idx),
_ensure_int64(col_idx),
out=out,
fill_value=fill_value,
)
return out
elif dtype_str in ("float64", "object", "datetime64[ns]"):
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis="multi")
take_f(
arr,
_ensure_int64(row_idx),
_ensure_int64(col_idx),
out=out,
fill_value=fill_value,
)
return out
else:
return take_2d(
take_2d(arr, row_idx, axis=0, fill_value=fill_value),
col_idx,
axis=1,
fill_value=fill_value,
)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def resample(
self,
rule,
how=None,
axis=0,
fill_method=None,
closed="right",
label="right",
convention=None,
kind=None,
loffset=None,
limit=None,
base=0,
):
"""
Convenience method for frequency conversion and resampling of regular
time-series data.
Parameters
----------
rule : the offset string or object representing target conversion
how : string, method for down- or re-sampling, default to 'mean' for
downsampling
fill_method : string, fill_method for upsampling, default None
axis : int, optional, default 0
closed : {'right', 'left'}, default 'right'
Which side of bin interval is closed
label : {'right', 'left'}, default 'right'
Which bin edge label to label bucket with
convention : {'start', 'end', 's', 'e'}
loffset : timedelta
Adjust the resampled time labels
base : int, default 0
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '5min' frequency, base could
range from 0 through 4. Defaults to 0
"""
from pandas.tseries.resample import TimeGrouper
sampler = TimeGrouper(
rule,
label=label,
closed=closed,
how=how,
axis=axis,
kind=kind,
loffset=loffset,
fill_method=fill_method,
convention=convention,
limit=limit,
base=base,
)
return sampler.resample(self)
|
def resample(
self,
rule,
how="mean",
axis=0,
fill_method=None,
closed="right",
label="right",
convention=None,
kind=None,
loffset=None,
limit=None,
base=0,
):
"""
Convenience method for frequency conversion and resampling of regular
time-series data.
Parameters
----------
rule : the offset string or object representing target conversion
how : string, method for down- or re-sampling, default 'mean'
fill_method : string, fill_method for upsampling, default None
axis : int, optional, default 0
closed : {'right', 'left'}, default 'right'
Which side of bin interval is closed
label : {'right', 'left'}, default 'right'
Which bin edge label to label bucket with
convention : {'start', 'end', 's', 'e'}
loffset : timedelta
Adjust the resampled time labels
base : int, default 0
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '5min' frequency, base could
range from 0 through 4. Defaults to 0
"""
from pandas.tseries.resample import TimeGrouper
sampler = TimeGrouper(
rule,
label=label,
closed=closed,
how=how,
axis=axis,
kind=kind,
loffset=loffset,
fill_method=fill_method,
convention=convention,
limit=limit,
base=base,
)
return sampler.resample(self)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _nanmin(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, (np.integer, np.datetime64)):
values = values.copy()
np.putmask(values, mask, np.inf)
# numpy 1.6.1 workaround in Python 3.x
if values.dtype == np.object_ and sys.version_info[0] >= 3: # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.min, apply_ax, values)
else:
result = __builtin__.min(values)
else:
if (axis is not None and values.shape[axis] == 0) or values.size == 0:
result = values.sum(axis)
result.fill(np.nan)
else:
result = values.min(axis)
return _maybe_null_out(result, axis, mask)
|
def _nanmin(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, (np.integer, np.datetime64)):
values = values.copy()
np.putmask(values, mask, np.inf)
# numpy 1.6.1 workaround in Python 3.x
if values.dtype == np.object_ and sys.version_info[0] >= 3: # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.min, apply_ax, values)
else:
result = __builtin__.min(values)
else:
result = values.min(axis)
return _maybe_null_out(result, axis, mask)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _nanmax(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, (np.integer, np.datetime64)):
values = values.copy()
np.putmask(values, mask, -np.inf)
# numpy 1.6.1 workaround in Python 3.x
if values.dtype == np.object_ and sys.version_info[0] >= 3: # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.max, apply_ax, values)
else:
result = __builtin__.max(values)
else:
if (axis is not None and values.shape[axis] == 0) or values.size == 0:
result = values.sum(axis)
result.fill(np.nan)
else:
result = values.max(axis)
return _maybe_null_out(result, axis, mask)
|
def _nanmax(values, axis=None, skipna=True):
mask = isnull(values)
if skipna and not issubclass(values.dtype.type, (np.integer, np.datetime64)):
values = values.copy()
np.putmask(values, mask, -np.inf)
# numpy 1.6.1 workaround in Python 3.x
if values.dtype == np.object_ and sys.version_info[0] >= 3: # pragma: no cover
import __builtin__
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(__builtin__.max, apply_ax, values)
else:
result = __builtin__.max(values)
else:
result = values.max(axis)
return _maybe_null_out(result, axis, mask)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def get_corr_func(method):
if method in ["kendall", "spearman"]:
from scipy.stats import kendalltau, spearmanr
def _pearson(a, b):
return np.corrcoef(a, b)[0, 1]
def _kendall(a, b):
rs = kendalltau(a, b)
if isinstance(rs, tuple):
return rs[0]
return rs
def _spearman(a, b):
return spearmanr(a, b)[0]
_cor_methods = {"pearson": _pearson, "kendall": _kendall, "spearman": _spearman}
return _cor_methods[method]
|
def get_corr_func(method):
if method in ["kendall", "spearman"]:
from scipy.stats import kendalltau, spearmanr
def _pearson(a, b):
return np.corrcoef(a, b)[0, 1]
def _kendall(a, b):
return kendalltau(a, b)[0]
def _spearman(a, b):
return spearmanr(a, b)[0]
_cor_methods = {"pearson": _pearson, "kendall": _kendall, "spearman": _spearman}
return _cor_methods[method]
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _kendall(a, b):
rs = kendalltau(a, b)
if isinstance(rs, tuple):
return rs[0]
return rs
|
def _kendall(a, b):
return kendalltau(a, b)[0]
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def reindex(
self,
major=None,
items=None,
minor=None,
method=None,
major_axis=None,
minor_axis=None,
copy=True,
):
"""
Conform panel to new axis or axes
Parameters
----------
major : Index or sequence, default None
Can also use 'major_axis' keyword
items : Index or sequence, default None
minor : Index or sequence, default None
Can also use 'minor_axis' keyword
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
copy : boolean, default True
Return a new object, even if the passed indexes are the same
Returns
-------
Panel (new object)
"""
result = self
major = _mut_exclusive(major, major_axis)
minor = _mut_exclusive(minor, minor_axis)
if (
method is None
and not self._is_mixed_type
and com._count_not_none(items, major, minor) == 3
):
return self._reindex_multi(items, major, minor)
if major is not None:
result = result._reindex_axis(major, method, 1, copy)
if minor is not None:
result = result._reindex_axis(minor, method, 2, copy)
if items is not None:
result = result._reindex_axis(items, method, 0, copy)
if result is self and copy:
raise ValueError("Must specify at least one axis")
return result
|
def reindex(
self,
major=None,
items=None,
minor=None,
method=None,
major_axis=None,
minor_axis=None,
copy=True,
):
"""
Conform panel to new axis or axes
Parameters
----------
major : Index or sequence, default None
Can also use 'major_axis' keyword
items : Index or sequence, default None
minor : Index or sequence, default None
Can also use 'minor_axis' keyword
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
copy : boolean, default True
Return a new object, even if the passed indexes are the same
Returns
-------
Panel (new object)
"""
result = self
major = _mut_exclusive(major, major_axis)
minor = _mut_exclusive(minor, minor_axis)
if major is not None:
result = result._reindex_axis(major, method, 1, copy)
if minor is not None:
result = result._reindex_axis(minor, method, 2, copy)
if items is not None:
result = result._reindex_axis(items, method, 0, copy)
if result is self and copy:
raise ValueError("Must specify at least one axis")
return result
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def block2d_to_block3d(
values, items, shape, major_labels, minor_labels, ref_items=None
):
"""
Developer method for pivoting DataFrame -> Panel. Used in HDFStore and
DataFrame.to_panel
"""
from pandas.core.internals import make_block
panel_shape = (len(items),) + shape
# TODO: lexsort depth needs to be 2!!
# Create observation selection vector using major and minor
# labels, for converting to panel format.
selector = minor_labels + shape[1] * major_labels
mask = np.zeros(np.prod(shape), dtype=bool)
mask.put(selector, True)
pvalues = np.empty(panel_shape, dtype=values.dtype)
if not issubclass(pvalues.dtype.type, (np.integer, np.bool_)):
pvalues.fill(np.nan)
elif not mask.all():
pvalues = com._maybe_upcast(pvalues)
pvalues.fill(np.nan)
values = values
for i in xrange(len(items)):
pvalues[i].flat[mask] = values[:, i]
if ref_items is None:
ref_items = items
return make_block(pvalues, items, ref_items)
|
def block2d_to_block3d(
values, items, shape, major_labels, minor_labels, ref_items=None
):
"""
Developer method for pivoting DataFrame -> Panel. Used in HDFStore and
DataFrame.to_panel
"""
from pandas.core.internals import make_block
panel_shape = (len(items),) + shape
# TODO: lexsort depth needs to be 2!!
# Create observation selection vector using major and minor
# labels, for converting to panel format.
selector = minor_labels + shape[1] * major_labels
mask = np.zeros(np.prod(shape), dtype=bool)
mask.put(selector, True)
pvalues = np.empty(panel_shape, dtype=values.dtype)
if not issubclass(pvalues.dtype.type, np.integer):
pvalues.fill(np.nan)
values = values
for i in xrange(len(items)):
pvalues[i].flat[mask] = values[:, i]
if ref_items is None:
ref_items = items
return make_block(pvalues, items, ref_items)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _insert_column(self, key):
self.columns = self.columns.insert(len(self.columns), key)
|
def _insert_column(self, key):
self.columns = Index(np.concatenate((self.columns, [key])))
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def __getitem__(self, key):
"""
Retrieve column or slice from DataFrame
"""
try:
# unsure about how kludgy this is
s = self._series[key]
s.name = key
return s
except (TypeError, KeyError):
if isinstance(key, slice):
date_rng = self.index[key]
return self.reindex(date_rng)
elif isinstance(key, (np.ndarray, list)):
if isinstance(key, list):
key = lib.list_to_object_array(key)
# also raises Exception if object array with NA values
if com._is_bool_indexer(key):
key = np.asarray(key, dtype=bool)
return self._getitem_array(key)
else: # pragma: no cover
raise
|
def __getitem__(self, item):
"""
Retrieve column or slice from DataFrame
"""
try:
# unsure about how kludgy this is
s = self._series[item]
s.name = item
return s
except (TypeError, KeyError):
if isinstance(item, slice):
date_rng = self.index[item]
return self.reindex(date_rng)
elif isinstance(item, np.ndarray):
if len(item) != len(self.index):
raise Exception(
"Item wrong length %d instead of %d!" % (len(item), len(self.index))
)
newIndex = self.index[item]
return self.reindex(newIndex)
else: # pragma: no cover
raise
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def ewma(
arg, com=None, span=None, min_periods=0, freq=None, time_rule=None, adjust=True
):
com = _get_center_of_mass(com, span)
arg = _conv_timerule(arg, freq, time_rule)
def _ewma(v):
result = lib.ewma(v, com, int(adjust))
first_index = _first_valid_index(v)
result[first_index : first_index + min_periods] = NaN
return result
return_hook, values = _process_data_structure(arg)
output = np.apply_along_axis(_ewma, 0, values)
return return_hook(output)
|
def ewma(arg, com=None, span=None, min_periods=0, freq=None, time_rule=None):
com = _get_center_of_mass(com, span)
arg = _conv_timerule(arg, freq, time_rule)
def _ewma(v):
result = _tseries.ewma(v, com)
first_index = _first_valid_index(v)
result[first_index : first_index + min_periods] = NaN
return result
return_hook, values = _process_data_structure(arg)
output = np.apply_along_axis(_ewma, 0, values)
return return_hook(output)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _ewma(v):
result = lib.ewma(v, com, int(adjust))
first_index = _first_valid_index(v)
result[first_index : first_index + min_periods] = NaN
return result
|
def _ewma(v):
result = _tseries.ewma(v, com)
first_index = _first_valid_index(v)
result[first_index : first_index + min_periods] = NaN
return result
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def rolling_quantile(
arg, window, quantile, min_periods=None, freq=None, time_rule=None
):
"""Moving quantile
Parameters
----------
arg : Series, DataFrame
window : Number of observations used for calculating statistic
quantile : 0 <= quantile <= 1
min_periods : int
Minimum number of observations in window required to have a value
freq : None or string alias / date offset object, default=None
Frequency to conform to before computing statistic
Returns
-------
y : type of input argument
"""
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return lib.roll_quantile(arg, window, minp, quantile)
return _rolling_moment(
arg, window, call_cython, min_periods, freq=freq, time_rule=time_rule
)
|
def rolling_quantile(
arg, window, quantile, min_periods=None, freq=None, time_rule=None
):
"""Moving quantile
Parameters
----------
arg : Series, DataFrame
window : Number of observations used for calculating statistic
quantile : 0 <= quantile <= 1
min_periods : int
Minimum number of observations in window required to have a value
freq : None or string alias / date offset object, default=None
Frequency to conform to before computing statistic
Returns
-------
y : type of input argument
"""
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return _tseries.roll_quantile(arg, window, minp, quantile)
return _rolling_moment(
arg, window, call_cython, min_periods, freq=freq, time_rule=time_rule
)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return lib.roll_quantile(arg, window, minp, quantile)
|
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return _tseries.roll_quantile(arg, window, minp, quantile)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def rolling_apply(arg, window, func, min_periods=None, freq=None, time_rule=None):
"""Generic moving function application
Parameters
----------
arg : Series, DataFrame
window : Number of observations used for calculating statistic
func : function
Must produce a single value from an ndarray input
min_periods : int
Minimum number of observations in window required to have a value
freq : None or string alias / date offset object, default=None
Frequency to conform to before computing statistic
Returns
-------
y : type of input argument
"""
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return lib.roll_generic(arg, window, minp, func)
return _rolling_moment(
arg, window, call_cython, min_periods, freq=freq, time_rule=time_rule
)
|
def rolling_apply(arg, window, func, min_periods=None, freq=None, time_rule=None):
"""Generic moving function application
Parameters
----------
arg : Series, DataFrame
window : Number of observations used for calculating statistic
func : function
Must produce a single value from an ndarray input
min_periods : int
Minimum number of observations in window required to have a value
freq : None or string alias / date offset object, default=None
Frequency to conform to before computing statistic
Returns
-------
y : type of input argument
"""
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return _tseries.roll_generic(arg, window, minp, func)
return _rolling_moment(
arg, window, call_cython, min_periods, freq=freq, time_rule=time_rule
)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return lib.roll_generic(arg, window, minp, func)
|
def call_cython(arg, window, minp):
minp = _use_window(minp, window)
return _tseries.roll_generic(arg, window, minp, func)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def scatter_matrix(
frame,
alpha=0.5,
figsize=None,
ax=None,
grid=False,
diagonal="hist",
marker=".",
**kwds,
):
"""
Draw a matrix of scatter plots.
Parameters
----------
alpha : amount of transparency applied
figsize : a tuple (width, height) in inches
ax : Matplotlib axis object
grid : setting this to True will show the grid
diagonal : pick between 'kde' and 'hist' for
either Kernel Density Estimation or Histogram
plon in the diagonal
kwds : other plotting keyword arguments
To be passed to scatter function
Examples
--------
>>> df = DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> scatter_matrix(df, alpha=0.2)
"""
from matplotlib.artist import setp
df = frame._get_numeric_data()
n = df.columns.size
fig, axes = _subplots(nrows=n, ncols=n, figsize=figsize, ax=ax, squeeze=False)
# no gaps between subplots
fig.subplots_adjust(wspace=0, hspace=0)
mask = com.notnull(df)
marker = _get_marker_compat(marker)
for i, a in zip(range(n), df.columns):
for j, b in zip(range(n), df.columns):
ax = axes[i, j]
if i == j:
values = df[a].values[mask[a].values]
# Deal with the diagonal by drawing a histogram there.
if diagonal == "hist":
ax.hist(values)
elif diagonal in ("kde", "density"):
from scipy.stats import gaussian_kde
y = values
gkde = gaussian_kde(y)
ind = np.linspace(y.min(), y.max(), 1000)
ax.plot(ind, gkde.evaluate(ind), **kwds)
else:
common = (mask[a] & mask[b]).values
ax.scatter(
df[b][common], df[a][common], marker=marker, alpha=alpha, **kwds
)
ax.set_xlabel("")
ax.set_ylabel("")
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
# setup labels
if i == 0 and j % 2 == 1:
ax.set_xlabel(b, visible=True)
ax.xaxis.set_visible(True)
ax.set_xlabel(b)
ax.xaxis.set_ticks_position("top")
ax.xaxis.set_label_position("top")
setp(ax.get_xticklabels(), rotation=90)
elif i == n - 1 and j % 2 == 0:
ax.set_xlabel(b, visible=True)
ax.xaxis.set_visible(True)
ax.set_xlabel(b)
ax.xaxis.set_ticks_position("bottom")
ax.xaxis.set_label_position("bottom")
setp(ax.get_xticklabels(), rotation=90)
elif j == 0 and i % 2 == 0:
ax.set_ylabel(a, visible=True)
ax.yaxis.set_visible(True)
ax.set_ylabel(a)
ax.yaxis.set_ticks_position("left")
ax.yaxis.set_label_position("left")
elif j == n - 1 and i % 2 == 1:
ax.set_ylabel(a, visible=True)
ax.yaxis.set_visible(True)
ax.set_ylabel(a)
ax.yaxis.set_ticks_position("right")
ax.yaxis.set_label_position("right")
# ax.grid(b=grid)
axes[0, 0].yaxis.set_visible(False)
axes[n - 1, n - 1].xaxis.set_visible(False)
axes[n - 1, n - 1].yaxis.set_visible(False)
axes[0, n - 1].yaxis.tick_right()
for ax in axes.flat:
setp(ax.get_xticklabels(), fontsize=8)
setp(ax.get_yticklabels(), fontsize=8)
return axes
|
def scatter_matrix(
frame,
alpha=0.5,
figsize=None,
ax=None,
grid=False,
diagonal="hist",
marker=".",
**kwds,
):
"""
Draw a matrix of scatter plots.
Parameters
----------
alpha : amount of transparency applied
figsize : a tuple (width, height) in inches
ax : Matplotlib axis object
grid : setting this to True will show the grid
diagonal : pick between 'kde' and 'hist' for
either Kernel Density Estimation or Histogram
plon in the diagonal
kwds : other plotting keyword arguments
To be passed to scatter function
Examples
--------
>>> df = DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> scatter_matrix(df, alpha=0.2)
"""
df = frame._get_numeric_data()
n = df.columns.size
fig, axes = _subplots(nrows=n, ncols=n, figsize=figsize, ax=ax, squeeze=False)
# no gaps between subplots
fig.subplots_adjust(wspace=0, hspace=0)
mask = com.notnull(df)
marker = _get_marker_compat(marker)
for i, a in zip(range(n), df.columns):
for j, b in zip(range(n), df.columns):
if i == j:
values = df[a].values[mask[a].values]
# Deal with the diagonal by drawing a histogram there.
if diagonal == "hist":
axes[i, j].hist(values)
elif diagonal in ("kde", "density"):
from scipy.stats import gaussian_kde
y = values
gkde = gaussian_kde(y)
ind = np.linspace(y.min(), y.max(), 1000)
axes[i, j].plot(ind, gkde.evaluate(ind), **kwds)
else:
common = (mask[a] & mask[b]).values
axes[i, j].scatter(
df[b][common], df[a][common], marker=marker, alpha=alpha, **kwds
)
axes[i, j].set_xlabel("")
axes[i, j].set_ylabel("")
axes[i, j].set_xticklabels([])
axes[i, j].set_yticklabels([])
ticks = df.index
is_datetype = ticks.inferred_type in ("datetime", "date", "datetime64")
if ticks.is_numeric() or is_datetype:
"""
Matplotlib supports numeric values or datetime objects as
xaxis values. Taking LBYL approach here, by the time
matplotlib raises exception when using non numeric/datetime
values for xaxis, several actions are already taken by plt.
"""
ticks = ticks._mpl_repr()
# setup labels
if i == 0 and j % 2 == 1:
axes[i, j].set_xlabel(b, visible=True)
# axes[i, j].xaxis.set_visible(True)
axes[i, j].set_xlabel(b)
axes[i, j].set_xticklabels(ticks)
axes[i, j].xaxis.set_ticks_position("top")
axes[i, j].xaxis.set_label_position("top")
if i == n - 1 and j % 2 == 0:
axes[i, j].set_xlabel(b, visible=True)
# axes[i, j].xaxis.set_visible(True)
axes[i, j].set_xlabel(b)
axes[i, j].set_xticklabels(ticks)
axes[i, j].xaxis.set_ticks_position("bottom")
axes[i, j].xaxis.set_label_position("bottom")
if j == 0 and i % 2 == 0:
axes[i, j].set_ylabel(a, visible=True)
# axes[i, j].yaxis.set_visible(True)
axes[i, j].set_ylabel(a)
axes[i, j].set_yticklabels(ticks)
axes[i, j].yaxis.set_ticks_position("left")
axes[i, j].yaxis.set_label_position("left")
if j == n - 1 and i % 2 == 1:
axes[i, j].set_ylabel(a, visible=True)
# axes[i, j].yaxis.set_visible(True)
axes[i, j].set_ylabel(a)
axes[i, j].set_yticklabels(ticks)
axes[i, j].yaxis.set_ticks_position("right")
axes[i, j].yaxis.set_label_position("right")
axes[i, j].grid(b=grid)
return axes
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def __init__(
self,
data,
kind=None,
by=None,
subplots=False,
sharex=True,
sharey=False,
use_index=True,
figsize=None,
grid=None,
legend=True,
rot=None,
ax=None,
fig=None,
title=None,
xlim=None,
ylim=None,
xticks=None,
yticks=None,
sort_columns=False,
fontsize=None,
secondary_y=False,
**kwds,
):
self.data = data
self.by = by
self.kind = kind
self.sort_columns = sort_columns
self.subplots = subplots
self.sharex = sharex
self.sharey = sharey
self.figsize = figsize
self.xticks = xticks
self.yticks = yticks
self.xlim = xlim
self.ylim = ylim
self.title = title
self.use_index = use_index
self.fontsize = fontsize
self.rot = rot
if grid is None:
grid = False if secondary_y else True
self.grid = grid
self.legend = legend
for attr in self._pop_attributes:
value = kwds.pop(attr, self._attr_defaults.get(attr, None))
setattr(self, attr, value)
self.ax = ax
self.fig = fig
self.axes = None
if not isinstance(secondary_y, (bool, tuple, list, np.ndarray)):
secondary_y = [secondary_y]
self.secondary_y = secondary_y
self.kwds = kwds
|
def __init__(
self,
data,
kind=None,
by=None,
subplots=False,
sharex=True,
sharey=False,
use_index=True,
figsize=None,
grid=None,
legend=True,
rot=None,
ax=None,
fig=None,
title=None,
xlim=None,
ylim=None,
xticks=None,
yticks=None,
sort_columns=False,
fontsize=None,
secondary_y=False,
**kwds,
):
self.data = data
self.by = by
self.kind = kind
self.sort_columns = sort_columns
self.subplots = subplots
self.sharex = sharex
self.sharey = sharey
self.figsize = figsize
self.xticks = xticks
self.yticks = yticks
self.xlim = xlim
self.ylim = ylim
self.title = title
self.use_index = use_index
self.fontsize = fontsize
self.rot = rot
if grid is None:
grid = False if secondary_y else True
self.grid = grid
self.legend = legend
for attr in self._pop_attributes:
value = kwds.pop(attr, self._attr_defaults.get(attr, None))
setattr(self, attr, value)
self.ax = ax
self.fig = fig
self.axes = None
self.secondary_y = secondary_y
self.kwds = kwds
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _iter_data(self):
from pandas.core.frame import DataFrame
if isinstance(self.data, (Series, np.ndarray)):
yield self.label, np.asarray(self.data)
elif isinstance(self.data, DataFrame):
df = self.data
if self.sort_columns:
columns = com._try_sort(df.columns)
else:
columns = df.columns
for col in columns:
empty = df[col].count() == 0
# is this right?
values = df[col].values if not empty else np.zeros(len(df))
yield col, values
|
def _iter_data(self):
from pandas.core.frame import DataFrame
if isinstance(self.data, (Series, np.ndarray)):
yield com._stringify(self.label), np.asarray(self.data)
elif isinstance(self.data, DataFrame):
df = self.data
if self.sort_columns:
columns = com._try_sort(df.columns)
else:
columns = df.columns
for col in columns:
empty = df[col].count() == 0
# is this right?
values = df[col].values if not empty else np.zeros(len(df))
col = com._stringify(col)
yield col, values
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _maybe_right_yaxis(self, ax):
_types = (list, tuple, np.ndarray)
sec_true = isinstance(self.secondary_y, bool) and self.secondary_y
list_sec = isinstance(self.secondary_y, _types)
has_sec = list_sec and len(self.secondary_y) > 0
all_sec = list_sec and len(self.secondary_y) == self.nseries
if (sec_true or has_sec) and not hasattr(ax, "right_ax"):
orig_ax, new_ax = ax, ax.twinx()
orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax
if len(orig_ax.get_lines()) == 0: # no data on left y
orig_ax.get_yaxis().set_visible(False)
if len(new_ax.get_lines()) == 0:
new_ax.get_yaxis().set_visible(False)
if sec_true or all_sec:
ax = new_ax
else:
ax.get_yaxis().set_visible(True)
return ax
|
def _maybe_right_yaxis(self, ax):
ypos = ax.get_yaxis().get_ticks_position().strip().lower()
if self.secondary_y and ypos != "right":
orig_ax = ax
ax = ax.twinx()
if len(orig_ax.get_lines()) == 0: # no data on left y
orig_ax.get_yaxis().set_visible(False)
else:
ax.get_yaxis().set_visible(True)
return ax
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _setup_subplots(self):
if self.subplots:
nrows, ncols = self._get_layout()
if self.ax is None:
fig, axes = _subplots(
nrows=nrows,
ncols=ncols,
sharex=self.sharex,
sharey=self.sharey,
figsize=self.figsize,
secondary_y=self.secondary_y,
data=self.data,
)
else:
fig, axes = _subplots(
nrows=nrows,
ncols=ncols,
sharex=self.sharex,
sharey=self.sharey,
figsize=self.figsize,
ax=self.ax,
secondary_y=self.secondary_y,
data=self.data,
)
else:
if self.ax is None:
fig = self.plt.figure(figsize=self.figsize)
ax = fig.add_subplot(111)
ax = self._maybe_right_yaxis(ax)
else:
fig = self.ax.get_figure()
ax = self._maybe_right_yaxis(self.ax)
axes = [ax]
self.fig = fig
self.axes = axes
|
def _setup_subplots(self):
if self.subplots:
nrows, ncols = self._get_layout()
if self.ax is None:
fig, axes = _subplots(
nrows=nrows,
ncols=ncols,
sharex=self.sharex,
sharey=self.sharey,
figsize=self.figsize,
secondary_y=self.secondary_y,
data=self.data,
)
else:
fig, axes = _subplots(
nrows=nrows,
ncols=ncols,
sharex=self.sharex,
sharey=self.sharey,
figsize=self.figsize,
ax=self.ax,
secondary_y=self.secondary_y,
data=self.data,
)
else:
if self.ax is None:
fig = self.plt.figure(figsize=self.figsize)
ax = fig.add_subplot(111)
ax = self._maybe_right_yaxis(ax)
self.ax = ax
else:
fig = self.ax.get_figure()
self.ax = self._maybe_right_yaxis(self.ax)
axes = [self.ax]
self.fig = fig
self.axes = axes
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _adorn_subplots(self):
to_adorn = self.axes
# todo: sharex, sharey handling?
for ax in to_adorn:
if self.yticks is not None:
ax.set_yticks(self.yticks)
if self.xticks is not None:
ax.set_xticks(self.xticks)
if self.ylim is not None:
ax.set_ylim(self.ylim)
if self.xlim is not None:
ax.set_xlim(self.xlim)
ax.grid(self.grid)
if self.title:
if self.subplots:
self.fig.suptitle(self.title)
else:
self.axes[0].set_title(self.title)
if self._need_to_set_index:
labels = [_stringify(key) for key in self.data.index]
labels = dict(zip(range(len(self.data.index)), labels))
for ax_ in self.axes:
# ax_.set_xticks(self.xticks)
xticklabels = [labels.get(x, "") for x in ax_.get_xticks()]
ax_.set_xticklabels(xticklabels, rotation=self.rot)
|
def _adorn_subplots(self):
if self.subplots:
to_adorn = self.axes
else:
to_adorn = [self.ax]
# todo: sharex, sharey handling?
for ax in to_adorn:
if self.yticks is not None:
ax.set_yticks(self.yticks)
if self.xticks is not None:
ax.set_xticks(self.xticks)
if self.ylim is not None:
ax.set_ylim(self.ylim)
if self.xlim is not None:
ax.set_xlim(self.xlim)
ax.grid(self.grid)
if self.legend and not self.subplots:
self.ax.legend(loc="best", title=self.legend_title)
if self.title:
if self.subplots:
self.fig.suptitle(self.title)
else:
self.ax.set_title(self.title)
if self._need_to_set_index:
labels = [_stringify(key) for key in self.data.index]
labels = dict(zip(range(len(self.data.index)), labels))
for ax_ in self.axes:
# ax_.set_xticks(self.xticks)
xticklabels = [labels.get(x, "") for x in ax_.get_xticks()]
ax_.set_xticklabels(xticklabels, rotation=self.rot)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _make_plot(self):
from scipy.stats import gaussian_kde
plotf = self._get_plot_function()
for i, (label, y) in enumerate(self._iter_data()):
ax = self._get_ax(i)
style = self._get_style(i, label)
label = com._stringify(label)
gkde = gaussian_kde(y)
sample_range = max(y) - min(y)
ind = np.linspace(
min(y) - 0.5 * sample_range, max(y) + 0.5 * sample_range, 1000
)
ax.set_ylabel("Density")
plotf(ax, ind, gkde.evaluate(ind), style, label=label, **self.kwds)
ax.grid(self.grid)
|
def _make_plot(self):
from scipy.stats import gaussian_kde
plotf = self._get_plot_function()
for i, (label, y) in enumerate(self._iter_data()):
ax, style = self._get_ax_and_style(i)
if self.style:
style = self.style
gkde = gaussian_kde(y)
sample_range = max(y) - min(y)
ind = np.linspace(
min(y) - 0.5 * sample_range, max(y) + 0.5 * sample_range, 1000
)
ax.set_ylabel("Density")
plotf(ax, ind, gkde.evaluate(ind), style, label=label, **self.kwds)
ax.grid(self.grid)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _post_plot_logic(self):
df = self.data
if self.subplots and self.legend:
for ax in self.axes:
ax.legend(loc="best")
|
def _post_plot_logic(self):
df = self.data
if self.subplots and self.legend:
self.axes[0].legend(loc="best")
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def __init__(self, data, **kwargs):
self.mark_right = kwargs.pop("mark_right", True)
MPLPlot.__init__(self, data, **kwargs)
|
def __init__(self, data, **kwargs):
MPLPlot.__init__(self, data, **kwargs)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _use_dynamic_x(self):
freq = self._index_freq()
ax = self._get_ax(0)
ax_freq = getattr(ax, "freq", None)
if freq is None: # convert irregular if axes has freq info
freq = ax_freq
else: # do not use tsplot if irregular was plotted first
if (ax_freq is None) and (len(ax.get_lines()) > 0):
return False
return (freq is not None) and self._is_dynamic_freq(freq)
|
def _use_dynamic_x(self):
freq = self._index_freq()
ax, _ = self._get_ax_and_style(0)
ax_freq = getattr(ax, "freq", None)
if freq is None: # convert irregular if axes has freq info
freq = ax_freq
else: # do not use tsplot if irregular was plotted first
if (ax_freq is None) and (len(ax.get_lines()) > 0):
return False
return (freq is not None) and self._is_dynamic_freq(freq)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _make_plot(self):
# this is slightly deceptive
if self.use_index and self._use_dynamic_x():
data = self._maybe_convert_index(self.data)
self._make_ts_plot(data, **self.kwds)
else:
import matplotlib.pyplot as plt
cycle = "".join(plt.rcParams.get("axes.color_cycle", list("bgrcmyk")))
colors = self.kwds.pop("colors", cycle)
lines = []
labels = []
x = self._get_xticks(convert_period=True)
plotf = self._get_plot_function()
for i, (label, y) in enumerate(self._iter_data()):
ax = self._get_ax(i)
style = self._get_style(i, label)
kwds = self.kwds.copy()
if re.match("[a-z]+", style) is None:
kwds["color"] = colors[i % len(colors)]
label = com._stringify(label)
mask = com.isnull(y)
if mask.any():
y = np.ma.array(y)
y = np.ma.masked_where(mask, y)
newline = plotf(ax, x, y, style, label=label, **kwds)[0]
lines.append(newline)
leg_label = label
if self.mark_right and self.on_right(i):
leg_label += " (right)"
labels.append(leg_label)
ax.grid(self.grid)
self._make_legend(lines, labels)
|
def _make_plot(self):
# this is slightly deceptive
if self.use_index and self._use_dynamic_x():
data = self._maybe_convert_index(self.data)
self._make_ts_plot(data)
else:
x = self._get_xticks(convert_period=True)
plotf = self._get_plot_function()
for i, (label, y) in enumerate(self._iter_data()):
ax, style = self._get_ax_and_style(i)
if self.style:
style = self.style
mask = com.isnull(y)
if mask.any():
y = np.ma.array(y)
y = np.ma.masked_where(mask, y)
plotf(ax, x, y, style, label=label, **self.kwds)
ax.grid(self.grid)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _make_ts_plot(self, data, **kwargs):
from pandas.tseries.plotting import tsplot
import matplotlib.pyplot as plt
kwargs = kwargs.copy()
cycle = "".join(plt.rcParams.get("axes.color_cycle", list("bgrcmyk")))
colors = kwargs.pop("colors", "".join(cycle))
plotf = self._get_plot_function()
lines = []
labels = []
def to_leg_label(label, i):
if self.mark_right and self.on_right(i):
return label + " (right)"
return label
if isinstance(data, Series):
ax = self._get_ax(0) # self.axes[0]
style = self.style or ""
label = com._stringify(self.label)
if re.match("[a-z]+", style) is None:
kwargs["color"] = colors[0]
newlines = tsplot(data, plotf, ax=ax, label=label, style=self.style, **kwargs)
ax.grid(self.grid)
lines.append(newlines[0])
leg_label = to_leg_label(label, 0)
labels.append(leg_label)
else:
for i, col in enumerate(data.columns):
label = com._stringify(col)
ax = self._get_ax(i)
style = self._get_style(i, col)
kwds = kwargs.copy()
if re.match("[a-z]+", style) is None:
kwds["color"] = colors[i % len(colors)]
newlines = tsplot(data[col], plotf, ax=ax, label=label, style=style, **kwds)
lines.append(newlines[0])
leg_label = to_leg_label(label, i)
labels.append(leg_label)
ax.grid(self.grid)
self._make_legend(lines, labels)
|
def _make_ts_plot(self, data, **kwargs):
from pandas.tseries.plotting import tsplot
plotf = self._get_plot_function()
if isinstance(data, Series):
ax, _ = self._get_ax_and_style(0) # self.axes[0]
label = com._stringify(self.label)
tsplot(data, plotf, ax=ax, label=label, style=self.style, **kwargs)
ax.grid(self.grid)
else:
for i, col in enumerate(data.columns):
ax, _ = self._get_ax_and_style(i)
label = com._stringify(col)
tsplot(data[col], plotf, ax=ax, label=label, **kwargs)
ax.grid(self.grid)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _maybe_convert_index(self, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
from pandas.core.frame import DataFrame
if isinstance(data.index, DatetimeIndex) and isinstance(data, DataFrame):
freq = getattr(data.index, "freq", None)
if freq is None:
freq = getattr(data.index, "inferred_freq", None)
if isinstance(freq, DateOffset):
freq = freq.rule_code
freq = get_period_alias(freq)
if freq is None:
ax = self._get_ax(0)
freq = getattr(ax, "freq", None)
if freq is None:
raise ValueError("Could not get frequency alias for plotting")
data = DataFrame(
data.values, index=data.index.to_period(freq=freq), columns=data.columns
)
return data
|
def _maybe_convert_index(self, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
from pandas.core.frame import DataFrame
if isinstance(data.index, DatetimeIndex) and isinstance(data, DataFrame):
freq = getattr(data.index, "freq", None)
if freq is None:
freq = getattr(data.index, "inferred_freq", None)
if isinstance(freq, DateOffset):
freq = freq.rule_code
freq = get_period_alias(freq)
if freq is None:
ax, _ = self._get_ax_and_style(0)
freq = getattr(ax, "freq", None)
if freq is None:
raise ValueError("Could not get frequency alias for plotting")
data = DataFrame(
data.values, index=data.index.to_period(freq=freq), columns=data.columns
)
return data
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _post_plot_logic(self):
df = self.data
condition = (
not self._use_dynamic_x
and df.index.is_all_dates
and not self.subplots
or (self.subplots and self.sharex)
)
index_name = self._get_index_name()
for ax in self.axes:
if condition:
format_date_labels(ax)
if index_name is not None:
ax.set_xlabel(index_name)
if self.subplots and self.legend:
for ax in self.axes:
ax.legend(loc="best")
|
def _post_plot_logic(self):
df = self.data
if self.legend:
if self.subplots:
for ax in self.axes:
ax.legend(loc="best")
else:
self.axes[0].legend(loc="best")
condition = (
not self._use_dynamic_x
and df.index.is_all_dates
and not self.subplots
or (self.subplots and self.sharex)
)
index_name = self._get_index_name()
for ax in self.axes:
if condition:
format_date_labels(ax)
if index_name is not None:
ax.set_xlabel(index_name)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _make_plot(self):
colors = self.kwds.get("color", "brgyk")
rects = []
labels = []
ax = self._get_ax(0) # self.axes[0]
bar_f = self.bar_f
pos_prior = neg_prior = np.zeros(len(self.data))
K = self.nseries
for i, (label, y) in enumerate(self._iter_data()):
label = com._stringify(label)
kwds = self.kwds.copy()
kwds["color"] = colors[i % len(colors)]
if self.subplots:
ax = self._get_ax(i) # self.axes[i]
rect = bar_f(ax, self.ax_pos, y, 0.5, start=pos_prior, linewidth=1, **kwds)
ax.set_title(label)
elif self.stacked:
mask = y > 0
start = np.where(mask, pos_prior, neg_prior)
rect = bar_f(
ax, self.ax_pos, y, 0.5, start=start, label=label, linewidth=1, **kwds
)
pos_prior = pos_prior + np.where(mask, y, 0)
neg_prior = neg_prior + np.where(mask, 0, y)
else:
rect = bar_f(
ax,
self.ax_pos + i * 0.75 / K,
y,
0.75 / K,
start=pos_prior,
label=label,
**kwds,
)
rects.append(rect)
labels.append(label)
if self.legend and not self.subplots:
patches = [r[0] for r in rects]
self.axes[0].legend(patches, labels, loc="best", title=self.legend_title)
|
def _make_plot(self):
colors = self.kwds.get("color", "brgyk")
rects = []
labels = []
ax, _ = self._get_ax_and_style(0) # self.axes[0]
bar_f = self.bar_f
pos_prior = neg_prior = np.zeros(len(self.data))
K = self.nseries
for i, (label, y) in enumerate(self._iter_data()):
kwds = self.kwds.copy()
kwds["color"] = colors[i % len(colors)]
if self.subplots:
ax, _ = self._get_ax_and_style(i) # self.axes[i]
rect = bar_f(ax, self.ax_pos, y, 0.5, start=pos_prior, linewidth=1, **kwds)
ax.set_title(label)
elif self.stacked:
mask = y > 0
start = np.where(mask, pos_prior, neg_prior)
rect = bar_f(
ax, self.ax_pos, y, 0.5, start=start, label=label, linewidth=1, **kwds
)
pos_prior = pos_prior + np.where(mask, y, 0)
neg_prior = neg_prior + np.where(mask, 0, y)
else:
rect = bar_f(
ax,
self.ax_pos + i * 0.75 / K,
y,
0.75 / K,
start=pos_prior,
label=label,
**kwds,
)
rects.append(rect)
labels.append(label)
if self.legend and not self.subplots:
patches = [r[0] for r in rects]
# Legend to the right of the plot
# ax.legend(patches, labels, bbox_to_anchor=(1.05, 1),
# loc=2, borderaxespad=0.)
# self.fig.subplots_adjust(right=0.80)
ax.legend(patches, labels, loc="best", title=self.legend_title)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def plot_frame(
frame=None,
x=None,
y=None,
subplots=False,
sharex=True,
sharey=False,
use_index=True,
figsize=None,
grid=False,
legend=True,
rot=None,
ax=None,
style=None,
title=None,
xlim=None,
ylim=None,
logy=False,
xticks=None,
yticks=None,
kind="line",
sort_columns=False,
fontsize=None,
secondary_y=False,
**kwds,
):
"""
Make line or bar plot of DataFrame's series with the index on the x-axis
using matplotlib / pylab.
Parameters
----------
x : int or str, default None
y : int or str, default None
Allows plotting of one column versus another
subplots : boolean, default False
Make separate subplots for each time series
sharex : boolean, default True
In case subplots=True, share x axis
sharey : boolean, default False
In case subplots=True, share y axis
use_index : boolean, default True
Use index as ticks for x axis
stacked : boolean, default False
If True, create stacked bar plot. Only valid for DataFrame input
sort_columns: boolean, default False
Sort column names to determine plot ordering
title : string
Title to use for the plot
grid : boolean, default True
Axis grid lines
legend : boolean, default True
Place legend on axis subplots
ax : matplotlib axis object, default None
style : list or dict
matplotlib line style per column
kind : {'line', 'bar', 'barh'}
bar : vertical bar plot
barh : horizontal bar plot
logy : boolean, default False
For line plots, use log scaling on y axis
xticks : sequence
Values to use for the xticks
yticks : sequence
Values to use for the yticks
xlim : 2-tuple/list
ylim : 2-tuple/list
rot : int, default None
Rotation for ticks
secondary_y : boolean or sequence, default False
Whether to plot on the secondary y-axis
If dict then can select which columns to plot on secondary y-axis
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
ax_or_axes : matplotlib.AxesSubplot or list of them
"""
kind = _get_standard_kind(kind.lower().strip())
if kind == "line":
klass = LinePlot
elif kind in ("bar", "barh"):
klass = BarPlot
elif kind == "kde":
klass = KdePlot
else:
raise ValueError("Invalid chart type given %s" % kind)
if isinstance(x, int):
x = frame.columns[x]
if isinstance(y, int):
y = frame.columns[y]
if x is not None:
frame = frame.set_index(x).sort_index()
if y is not None:
return plot_series(
frame[y],
label=y,
kind=kind,
use_index=True,
rot=rot,
xticks=xticks,
yticks=yticks,
xlim=xlim,
ylim=ylim,
ax=ax,
style=style,
grid=grid,
logy=logy,
secondary_y=secondary_y,
**kwds,
)
plot_obj = klass(
frame,
kind=kind,
subplots=subplots,
rot=rot,
legend=legend,
ax=ax,
style=style,
fontsize=fontsize,
use_index=use_index,
sharex=sharex,
sharey=sharey,
xticks=xticks,
yticks=yticks,
xlim=xlim,
ylim=ylim,
title=title,
grid=grid,
figsize=figsize,
logy=logy,
sort_columns=sort_columns,
secondary_y=secondary_y,
**kwds,
)
plot_obj.generate()
plot_obj.draw()
if subplots:
return plot_obj.axes
else:
return plot_obj.axes[0]
|
def plot_frame(
frame=None,
subplots=False,
sharex=True,
sharey=False,
use_index=True,
figsize=None,
grid=False,
legend=True,
rot=None,
ax=None,
title=None,
xlim=None,
ylim=None,
logy=False,
xticks=None,
yticks=None,
kind="line",
sort_columns=False,
fontsize=None,
secondary_y=False,
**kwds,
):
"""
Make line or bar plot of DataFrame's series with the index on the x-axis
using matplotlib / pylab.
Parameters
----------
subplots : boolean, default False
Make separate subplots for each time series
sharex : boolean, default True
In case subplots=True, share x axis
sharey : boolean, default False
In case subplots=True, share y axis
use_index : boolean, default True
Use index as ticks for x axis
stacked : boolean, default False
If True, create stacked bar plot. Only valid for DataFrame input
sort_columns: boolean, default False
Sort column names to determine plot ordering
title : string
Title to use for the plot
grid : boolean, default True
Axis grid lines
legend : boolean, default True
Place legend on axis subplots
ax : matplotlib axis object, default None
kind : {'line', 'bar', 'barh'}
bar : vertical bar plot
barh : horizontal bar plot
logy : boolean, default False
For line plots, use log scaling on y axis
xticks : sequence
Values to use for the xticks
yticks : sequence
Values to use for the yticks
xlim : 2-tuple/list
ylim : 2-tuple/list
rot : int, default None
Rotation for ticks
secondary_y : boolean or sequence, default False
Whether to plot on the secondary y-axis
If dict then can select which columns to plot on secondary y-axis
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
ax_or_axes : matplotlib.AxesSubplot or list of them
"""
kind = _get_standard_kind(kind.lower().strip())
if kind == "line":
klass = LinePlot
elif kind in ("bar", "barh"):
klass = BarPlot
elif kind == "kde":
klass = KdePlot
else:
raise ValueError("Invalid chart type given %s" % kind)
plot_obj = klass(
frame,
kind=kind,
subplots=subplots,
rot=rot,
legend=legend,
ax=ax,
fontsize=fontsize,
use_index=use_index,
sharex=sharex,
sharey=sharey,
xticks=xticks,
yticks=yticks,
xlim=xlim,
ylim=ylim,
title=title,
grid=grid,
figsize=figsize,
logy=logy,
sort_columns=sort_columns,
secondary_y=secondary_y,
**kwds,
)
plot_obj.generate()
plot_obj.draw()
if subplots:
return plot_obj.axes
else:
return plot_obj.axes[0]
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def boxplot(
data,
column=None,
by=None,
ax=None,
fontsize=None,
rot=0,
grid=True,
figsize=None,
**kwds,
):
"""
Make a box plot from DataFrame column optionally grouped b ysome columns or
other inputs
Parameters
----------
data : DataFrame or Series
column : column name or list of names, or vector
Can be any valid input to groupby
by : string or sequence
Column in the DataFrame to group by
fontsize : int or string
rot : label rotation angle
kwds : other plotting keyword arguments to be passed to matplotlib boxplot
function
Returns
-------
ax : matplotlib.axes.AxesSubplot
"""
from pandas import Series, DataFrame
if isinstance(data, Series):
data = DataFrame({"x": data})
column = "x"
def plot_group(grouped, ax):
keys, values = zip(*grouped)
keys = [_stringify(x) for x in keys]
values = [remove_na(v) for v in values]
ax.boxplot(values, **kwds)
if kwds.get("vert", 1):
ax.set_xticklabels(keys, rotation=rot, fontsize=fontsize)
else:
ax.set_yticklabels(keys, rotation=rot, fontsize=fontsize)
if column == None:
columns = None
else:
if isinstance(column, (list, tuple)):
columns = column
else:
columns = [column]
if by is not None:
if not isinstance(by, (list, tuple)):
by = [by]
fig, axes = _grouped_plot_by_column(
plot_group, data, columns=columns, by=by, grid=grid, figsize=figsize, ax=ax
)
# Return axes in multiplot case, maybe revisit later # 985
ret = axes
else:
if ax is None:
ax = _gca()
fig = ax.get_figure()
data = data._get_numeric_data()
if columns:
cols = columns
else:
cols = data.columns
keys = [_stringify(x) for x in cols]
# Return boxplot dict in single plot case
clean_values = [remove_na(x) for x in data[cols].values.T]
bp = ax.boxplot(clean_values, **kwds)
if kwds.get("vert", 1):
ax.set_xticklabels(keys, rotation=rot, fontsize=fontsize)
else:
ax.set_yticklabels(keys, rotation=rot, fontsize=fontsize)
ax.grid(grid)
ret = bp
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
return ret
|
def boxplot(
data,
column=None,
by=None,
ax=None,
fontsize=None,
rot=0,
grid=True,
figsize=None,
**kwds,
):
"""
Make a box plot from DataFrame column optionally grouped b ysome columns or
other inputs
Parameters
----------
data : DataFrame or Series
column : column name or list of names, or vector
Can be any valid input to groupby
by : string or sequence
Column in the DataFrame to group by
fontsize : int or string
rot : label rotation angle
kwds : other plotting keyword arguments to be passed to matplotlib boxplot
function
Returns
-------
ax : matplotlib.axes.AxesSubplot
"""
from pandas import Series, DataFrame
if isinstance(data, Series):
data = DataFrame({"x": data})
column = "x"
def plot_group(grouped, ax):
keys, values = zip(*grouped)
keys = [_stringify(x) for x in keys]
values = [remove_na(v) for v in values]
ax.boxplot(values, **kwds)
if kwds.get("vert", 1):
ax.set_xticklabels(keys, rotation=rot, fontsize=fontsize)
else:
ax.set_yticklabels(keys, rotation=rot, fontsize=fontsize)
if column == None:
columns = None
else:
if isinstance(column, (list, tuple)):
columns = column
else:
columns = [column]
if by is not None:
if not isinstance(by, (list, tuple)):
by = [by]
fig, axes = _grouped_plot_by_column(
plot_group, data, columns=columns, by=by, grid=grid, figsize=figsize
)
# Return axes in multiplot case, maybe revisit later # 985
ret = axes
else:
if ax is None:
ax = _gca()
fig = ax.get_figure()
data = data._get_numeric_data()
if columns:
cols = columns
else:
cols = data.columns
keys = [_stringify(x) for x in cols]
# Return boxplot dict in single plot case
clean_values = [remove_na(x) for x in data[cols].values.T]
bp = ax.boxplot(clean_values, **kwds)
if kwds.get("vert", 1):
ax.set_xticklabels(keys, rotation=rot, fontsize=fontsize)
else:
ax.set_yticklabels(keys, rotation=rot, fontsize=fontsize)
ax.grid(grid)
ret = bp
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
return ret
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _subplots(
nrows=1,
ncols=1,
sharex=False,
sharey=False,
squeeze=True,
subplot_kw=None,
ax=None,
secondary_y=False,
data=None,
**fig_kw,
):
"""Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.
Keyword arguments:
nrows : int
Number of rows of the subplot grid. Defaults to 1.
ncols : int
Number of columns of the subplot grid. Defaults to 1.
sharex : bool
If True, the X axis will be shared amongst all subplots.
sharey : bool
If True, the Y axis will be shared amongst all subplots.
squeeze : bool
If True, extra dimensions are squeezed out from the returned axis object:
- if only one subplot is constructed (nrows=ncols=1), the resulting
single Axis object is returned as a scalar.
- for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
array of Axis objects are returned as numpy 1-d arrays.
- for NxM subplots with N>1 and M>1 are returned as a 2d array.
If False, no squeezing at all is done: the returned axis object is always
a 2-d array contaning Axis instances, even if it ends up being 1x1.
subplot_kw : dict
Dict with keywords passed to the add_subplot() call used to create each
subplots.
fig_kw : dict
Dict with keywords passed to the figure() call. Note that all keywords
not recognized above will be automatically included here.
ax : Matplotlib axis object, default None
secondary_y : boolean or sequence of ints, default False
If True then y-axis will be on the right
Returns:
fig, ax : tuple
- fig is the Matplotlib Figure object
- ax can be either a single axis object or an array of axis objects if
more than one supblot was created. The dimensions of the resulting array
can be controlled with the squeeze keyword, see above.
**Examples:**
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Just a figure and one subplot
f, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
# Two subplots, unpack the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
# Four polar axes
plt.subplots(2, 2, subplot_kw=dict(polar=True))
"""
import matplotlib.pyplot as plt
from pandas.core.frame import DataFrame
if subplot_kw is None:
subplot_kw = {}
if ax is None:
fig = plt.figure(**fig_kw)
else:
fig = ax.get_figure()
fig.clear()
# Create empty object array to hold all axes. It's easiest to make it 1-d
# so we can just append subplots upon creation, and then
nplots = nrows * ncols
axarr = np.empty(nplots, dtype=object)
def on_right(i):
if isinstance(secondary_y, bool):
return secondary_y
if isinstance(data, DataFrame):
return data.columns[i] in secondary_y
# Create first subplot separately, so we can share it if requested
ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
if on_right(0):
orig_ax = ax0
ax0 = ax0.twinx()
orig_ax.get_yaxis().set_visible(False)
orig_ax.right_ax = ax0
ax0.left_ax = orig_ax
if sharex:
subplot_kw["sharex"] = ax0
if sharey:
subplot_kw["sharey"] = ax0
axarr[0] = ax0
# Note off-by-one counting because add_subplot uses the MATLAB 1-based
# convention.
for i in range(1, nplots):
ax = fig.add_subplot(nrows, ncols, i + 1, **subplot_kw)
if on_right(i):
orig_ax = ax
ax = ax.twinx()
orig_ax.get_yaxis().set_visible(False)
axarr[i] = ax
if nplots > 1:
if sharex and nrows > 1:
for i, ax in enumerate(axarr):
if np.ceil(float(i + 1) / ncols) < nrows: # only last row
[label.set_visible(False) for label in ax.get_xticklabels()]
if sharey and ncols > 1:
for i, ax in enumerate(axarr):
if (i % ncols) != 0: # only first column
[label.set_visible(False) for label in ax.get_yticklabels()]
if squeeze:
# Reshape the array to have the final desired dimension (nrow,ncol),
# though discarding unneeded dimensions that equal 1. If we only have
# one subplot, just return it instead of a 1-element array.
if nplots == 1:
axes = axarr[0]
else:
axes = axarr.reshape(nrows, ncols).squeeze()
else:
# returned axis array will be always 2-d, even if nrows=ncols=1
axes = axarr.reshape(nrows, ncols)
return fig, axes
|
def _subplots(
nrows=1,
ncols=1,
sharex=False,
sharey=False,
squeeze=True,
subplot_kw=None,
ax=None,
secondary_y=False,
data=None,
**fig_kw,
):
"""Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.
Keyword arguments:
nrows : int
Number of rows of the subplot grid. Defaults to 1.
ncols : int
Number of columns of the subplot grid. Defaults to 1.
sharex : bool
If True, the X axis will be shared amongst all subplots.
sharey : bool
If True, the Y axis will be shared amongst all subplots.
squeeze : bool
If True, extra dimensions are squeezed out from the returned axis object:
- if only one subplot is constructed (nrows=ncols=1), the resulting
single Axis object is returned as a scalar.
- for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
array of Axis objects are returned as numpy 1-d arrays.
- for NxM subplots with N>1 and M>1 are returned as a 2d array.
If False, no squeezing at all is done: the returned axis object is always
a 2-d array contaning Axis instances, even if it ends up being 1x1.
subplot_kw : dict
Dict with keywords passed to the add_subplot() call used to create each
subplots.
fig_kw : dict
Dict with keywords passed to the figure() call. Note that all keywords
not recognized above will be automatically included here.
ax : Matplotlib axis object, default None
secondary_y : boolean or sequence of ints, default False
If True then y-axis will be on the right
Returns:
fig, ax : tuple
- fig is the Matplotlib Figure object
- ax can be either a single axis object or an array of axis objects if
more than one supblot was created. The dimensions of the resulting array
can be controlled with the squeeze keyword, see above.
**Examples:**
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Just a figure and one subplot
f, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
# Two subplots, unpack the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
# Four polar axes
plt.subplots(2, 2, subplot_kw=dict(polar=True))
"""
import matplotlib.pyplot as plt
from pandas.core.frame import DataFrame
if subplot_kw is None:
subplot_kw = {}
if ax is None:
fig = plt.figure(**fig_kw)
else:
fig = ax.get_figure()
fig.clear()
# Create empty object array to hold all axes. It's easiest to make it 1-d
# so we can just append subplots upon creation, and then
nplots = nrows * ncols
axarr = np.empty(nplots, dtype=object)
def on_right(i):
if isinstance(secondary_y, bool):
return secondary_y
if isinstance(data, DataFrame):
return data.columns[i] in secondary_y
# Create first subplot separately, so we can share it if requested
ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
if on_right(0):
orig_ax = ax0
ax0 = ax0.twinx()
orig_ax.get_yaxis().set_visible(False)
if sharex:
subplot_kw["sharex"] = ax0
if sharey:
subplot_kw["sharey"] = ax0
axarr[0] = ax0
# Note off-by-one counting because add_subplot uses the MATLAB 1-based
# convention.
for i in range(1, nplots):
ax = fig.add_subplot(nrows, ncols, i + 1, **subplot_kw)
if on_right(i):
orig_ax = ax
ax = ax.twinx()
orig_ax.get_yaxis().set_visible(False)
axarr[i] = ax
if nplots > 1:
if sharex and nrows > 1:
for i, ax in enumerate(axarr):
if np.ceil(float(i + 1) / ncols) < nrows: # only last row
[label.set_visible(False) for label in ax.get_xticklabels()]
if sharey and ncols > 1:
for i, ax in enumerate(axarr):
if (i % ncols) != 0: # only first column
[label.set_visible(False) for label in ax.get_yticklabels()]
if squeeze:
# Reshape the array to have the final desired dimension (nrow,ncol),
# though discarding unneeded dimensions that equal 1. If we only have
# one subplot, just return it instead of a 1-element array.
if nplots == 1:
axes = axarr[0]
else:
axes = axarr.reshape(nrows, ncols).squeeze()
else:
# returned axis array will be always 2-d, even if nrows=ncols=1
axes = axarr.reshape(nrows, ncols)
return fig, axes
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def __new__(
cls,
data=None,
freq=None,
start=None,
end=None,
periods=None,
copy=False,
name=None,
tz=None,
verify_integrity=True,
normalize=False,
**kwds,
):
warn = False
if "offset" in kwds and kwds["offset"]:
freq = kwds["offset"]
warn = True
freq_infer = False
if not isinstance(freq, DateOffset):
if freq != "infer":
freq = to_offset(freq)
else:
freq_infer = True
freq = None
if warn:
import warnings
warnings.warn(
"parameter 'offset' is deprecated, please use 'freq' instead", FutureWarning
)
offset = freq
if periods is not None:
if com.is_float(periods):
periods = int(periods)
elif not com.is_integer(periods):
raise ValueError("Periods must be a number, got %s" % str(periods))
if data is None and offset is None:
raise ValueError("Must provide freq argument if no data is supplied")
if data is None:
return cls._generate(
start, end, periods, name, offset, tz=tz, normalize=normalize
)
if not isinstance(data, np.ndarray):
if np.isscalar(data):
raise ValueError(
"DatetimeIndex() must be called with a "
"collection of some kind, %s was passed" % repr(data)
)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data, dtype="O")
# try a few ways to make it datetime64
if lib.is_string_array(data):
data = _str_to_dt_array(data, offset)
else:
data = tools.to_datetime(data)
data.offset = offset
if issubclass(data.dtype.type, basestring):
subarr = _str_to_dt_array(data, offset)
elif issubclass(data.dtype.type, np.datetime64):
if isinstance(data, DatetimeIndex):
subarr = data.values
if offset is None:
offset = data.offset
verify_integrity = False
else:
if data.dtype != _NS_DTYPE:
subarr = lib.cast_to_nanoseconds(data)
else:
subarr = data
elif data.dtype == _INT64_DTYPE:
if copy:
subarr = np.asarray(data, dtype=_NS_DTYPE)
else:
subarr = data.view(_NS_DTYPE)
else:
subarr = tools.to_datetime(data)
if not np.issubdtype(subarr.dtype, np.datetime64):
raise TypeError("Unable to convert %s to datetime dtype" % str(data))
if tz is not None:
tz = tools._maybe_get_tz(tz)
# Convert local to UTC
ints = subarr.view("i8")
subarr = lib.tz_localize_to_utc(ints, tz)
subarr = subarr.view(_NS_DTYPE)
subarr = subarr.view(cls)
subarr.name = name
subarr.offset = offset
subarr.tz = tz
if verify_integrity and len(subarr) > 0:
if offset is not None and not freq_infer:
inferred = subarr.inferred_freq
if inferred != offset.freqstr:
raise ValueError("Dates do not conform to passed frequency")
if freq_infer:
inferred = subarr.inferred_freq
if inferred:
subarr.offset = to_offset(inferred)
return subarr
|
def __new__(
cls,
data=None,
freq=None,
start=None,
end=None,
periods=None,
copy=False,
name=None,
tz=None,
verify_integrity=True,
normalize=False,
**kwds,
):
warn = False
if "offset" in kwds and kwds["offset"]:
freq = kwds["offset"]
warn = True
infer_freq = False
if not isinstance(freq, DateOffset):
if freq != "infer":
freq = to_offset(freq)
else:
infer_freq = True
freq = None
if warn:
import warnings
warnings.warn(
"parameter 'offset' is deprecated, please use 'freq' instead", FutureWarning
)
offset = freq
if periods is not None:
if com.is_float(periods):
periods = int(periods)
elif not com.is_integer(periods):
raise ValueError("Periods must be a number, got %s" % str(periods))
if data is None and offset is None:
raise ValueError("Must provide freq argument if no data is supplied")
if data is None:
return cls._generate(
start, end, periods, name, offset, tz=tz, normalize=normalize
)
if not isinstance(data, np.ndarray):
if np.isscalar(data):
raise ValueError(
"DatetimeIndex() must be called with a "
"collection of some kind, %s was passed" % repr(data)
)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data, dtype="O")
# try a few ways to make it datetime64
if lib.is_string_array(data):
data = _str_to_dt_array(data, offset)
else:
data = tools.to_datetime(data)
data.offset = offset
if issubclass(data.dtype.type, basestring):
subarr = _str_to_dt_array(data, offset)
elif issubclass(data.dtype.type, np.datetime64):
if isinstance(data, DatetimeIndex):
subarr = data.values
if offset is None:
offset = data.offset
verify_integrity = False
else:
if data.dtype != _NS_DTYPE:
subarr = lib.cast_to_nanoseconds(data)
else:
subarr = data
elif data.dtype == _INT64_DTYPE:
subarr = np.asarray(data, dtype=_NS_DTYPE)
else:
subarr = tools.to_datetime(data)
if not np.issubdtype(subarr.dtype, np.datetime64):
raise TypeError("Unable to convert %s to datetime dtype" % str(data))
if tz is not None:
tz = tools._maybe_get_tz(tz)
# Convert local to UTC
ints = subarr.view("i8")
subarr = lib.tz_localize_to_utc(ints, tz)
subarr = subarr.view(_NS_DTYPE)
subarr = subarr.view(cls)
subarr.name = name
subarr.offset = offset
subarr.tz = tz
if verify_integrity and len(subarr) > 0:
if offset is not None and not infer_freq:
inferred = subarr.inferred_freq
if inferred != offset.freqstr:
raise ValueError("Dates do not conform to passed frequency")
if infer_freq:
inferred = subarr.inferred_freq
if inferred:
subarr.offset = to_offset(inferred)
return subarr
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def __repr__(self):
from pandas.core.format import _format_datetime64
values = self.values
freq = None
if self.offset is not None:
freq = self.offset.freqstr
summary = str(self.__class__)
if len(self) == 1:
first = _format_datetime64(values[0], tz=self.tz)
summary += "\n[%s]" % first
elif len(self) == 2:
first = _format_datetime64(values[0], tz=self.tz)
last = _format_datetime64(values[-1], tz=self.tz)
summary += "\n[%s, %s]" % (first, last)
elif len(self) > 2:
first = _format_datetime64(values[0], tz=self.tz)
last = _format_datetime64(values[-1], tz=self.tz)
summary += "\n[%s, ..., %s]" % (first, last)
tagline = "\nLength: %d, Freq: %s, Timezone: %s"
summary += tagline % (len(self), freq, self.tz)
return summary
|
def __repr__(self):
from pandas.core.format import _format_datetime64
values = self.values
freq = None
if self.offset is not None:
freq = self.offset.freqstr
summary = str(self.__class__)
if len(self) > 0:
first = _format_datetime64(values[0], tz=self.tz)
last = _format_datetime64(values[-1], tz=self.tz)
summary += "\n[%s, ..., %s]" % (first, last)
tagline = "\nLength: %d, Freq: %s, Timezone: %s"
summary += tagline % (len(self), freq, self.tz)
return summary
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def intersection(self, other):
"""
Specialized intersection for DatetimeIndex objects. May be much faster
than Index.union
Parameters
----------
other : DatetimeIndex or array-like
Returns
-------
y : Index or DatetimeIndex
"""
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
except TypeError:
pass
result = Index.intersection(self, other)
if isinstance(result, DatetimeIndex):
if result.freq is None:
result.offset = to_offset(result.inferred_freq)
return result
elif (
other.offset is None
or self.offset is None
or other.offset != self.offset
or (not self.is_monotonic or not other.is_monotonic)
):
result = Index.intersection(self, other)
if isinstance(result, DatetimeIndex):
if result.freq is None:
result.offset = to_offset(result.inferred_freq)
return result
# to make our life easier, "sort" the two ranges
if self[0] <= other[0]:
left, right = self, other
else:
left, right = other, self
end = min(left[-1], right[-1])
start = right[0]
if end < start:
return type(self)(data=[])
else:
lslice = slice(*left.slice_locs(start, end))
left_chunk = left.values[lslice]
return self._view_like(left_chunk)
|
def intersection(self, other):
"""
Specialized intersection for DatetimeIndex objects. May be much faster
than Index.union
Parameters
----------
other : DatetimeIndex or array-like
Returns
-------
y : Index or DatetimeIndex
"""
if not isinstance(other, DatetimeIndex):
try:
other = DatetimeIndex(other)
except TypeError:
pass
result = Index.intersection(self, other)
if isinstance(result, DatetimeIndex):
if result.freq is None:
result.offset = to_offset(result.inferred_freq)
return result
elif other.offset != self.offset or (
not self.is_monotonic or not other.is_monotonic
):
result = Index.intersection(self, other)
if isinstance(result, DatetimeIndex):
if result.freq is None:
result.offset = to_offset(result.inferred_freq)
return result
# to make our life easier, "sort" the two ranges
if self[0] <= other[0]:
left, right = self, other
else:
left, right = other, self
end = min(left[-1], right[-1])
start = right[0]
if end < start:
return type(self)(data=[])
else:
lslice = slice(*left.slice_locs(start, end))
left_chunk = left.values[lslice]
return self._view_like(left_chunk)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def get_value(self, series, key):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing
"""
try:
return Index.get_value(self, series, key)
except KeyError:
try:
loc = self._get_string_slice(key)
return series[loc]
except (TypeError, ValueError, KeyError):
pass
if isinstance(key, time):
locs = self.indexer_at_time(key)
return series.take(locs)
if isinstance(key, basestring):
stamp = Timestamp(key, tz=self.tz)
else:
stamp = Timestamp(key)
try:
return self._engine.get_value(series, stamp)
except KeyError:
raise KeyError(stamp)
|
def get_value(self, series, key):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing
"""
try:
return Index.get_value(self, series, key)
except KeyError:
try:
loc = self._get_string_slice(key)
return series[loc]
except (TypeError, ValueError, KeyError):
pass
if isinstance(key, time):
locs = self.indexer_at_time(key)
return series.take(locs)
stamp = Timestamp(key)
try:
return self._engine.get_value(series, stamp)
except KeyError:
raise KeyError(stamp)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def date_range(
start=None, end=None, periods=None, freq="D", tz=None, normalize=False, name=None
):
"""
Return a fixed frequency datetime index, with day (calendar) as the default
frequency
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generating dates
periods : integer or None, default None
If None, must specify start and end
freq : string or DateOffset, default 'D' (calendar daily)
Frequency strings can have multiples, e.g. '5H'
tz : string or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
name : str, default None
Name of the resulting index
Notes
-----
2 of start, end, or periods must be specified
Returns
-------
rng : DatetimeIndex
"""
return DatetimeIndex(
start=start,
end=end,
periods=periods,
freq=freq,
tz=tz,
normalize=normalize,
name=name,
)
|
def date_range(start=None, end=None, periods=None, freq="D", tz=None, normalize=False):
"""
Return a fixed frequency datetime index, with day (calendar) as the default
frequency
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generating dates
periods : integer or None, default None
If None, must specify start and end
freq : string or DateOffset, default 'D' (calendar daily)
Frequency strings can have multiples, e.g. '5H'
tz : string or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
Notes
-----
2 of start, end, or periods must be specified
Returns
-------
rng : DatetimeIndex
"""
return DatetimeIndex(
start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize
)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def bdate_range(
start=None, end=None, periods=None, freq="B", tz=None, normalize=True, name=None
):
"""
Return a fixed frequency datetime index, with business day as the default
frequency
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generating dates
periods : integer or None, default None
If None, must specify start and end
freq : string or DateOffset, default 'B' (business daily)
Frequency strings can have multiples, e.g. '5H'
tz : string or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
name : str, default None
Name for the resulting index
Notes
-----
2 of start, end, or periods must be specified
Returns
-------
rng : DatetimeIndex
"""
return DatetimeIndex(
start=start,
end=end,
periods=periods,
freq=freq,
tz=tz,
normalize=normalize,
name=name,
)
|
def bdate_range(start=None, end=None, periods=None, freq="B", tz=None, normalize=True):
"""
Return a fixed frequency datetime index, with business day as the default
frequency
Parameters
----------
start : string or datetime-like, default None
Left bound for generating dates
end : string or datetime-like, default None
Right bound for generating dates
periods : integer or None, default None
If None, must specify start and end
freq : string or DateOffset, default 'B' (business daily)
Frequency strings can have multiples, e.g. '5H'
tz : string or None
Time zone name for returning localized DatetimeIndex, for example
Asia/Beijing
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
Notes
-----
2 of start, end, or periods must be specified
Returns
-------
rng : DatetimeIndex
"""
return DatetimeIndex(
start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize
)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def apply(self, other):
n = self.n
wkday, _ = lib.monthrange(other.year, other.month)
first = _get_firstbday(wkday)
if other.day > first and n <= 0:
# as if rolled forward already
n += 1
elif other.day < first and n > 0:
other = other + timedelta(days=first - other.day)
n -= 1
other = other + relativedelta(months=n)
wkday, _ = lib.monthrange(other.year, other.month)
first = _get_firstbday(wkday)
result = datetime(other.year, other.month, first)
return result
|
def apply(self, other):
n = self.n
wkday, _ = lib.monthrange(other.year, other.month)
first = _get_firstbday(wkday)
if other.day > first and n <= 0:
# as if rolled forward already
n += 1
other = other + relativedelta(months=n)
wkday, _ = lib.monthrange(other.year, other.month)
first = _get_firstbday(wkday)
result = datetime(other.year, other.month, first)
return result
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _from_arraylike(cls, data, freq):
if not isinstance(data, np.ndarray):
if np.isscalar(data) or isinstance(data, Period):
raise ValueError(
"PeriodIndex() must be called with a "
"collection of some kind, %s was passed" % repr(data)
)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
try:
data = com._ensure_int64(data)
if freq is None:
raise ValueError("freq not specified")
data = np.array(
[Period(x, freq=freq).ordinal for x in data], dtype=np.int64
)
except (TypeError, ValueError):
data = com._ensure_object(data)
if freq is None and len(data) > 0:
freq = getattr(data[0], "freq", None)
if freq is None:
raise ValueError(
"freq not specified and cannot be inferred from first element"
)
data = _get_ordinals(data, freq)
else:
if isinstance(data, PeriodIndex):
if freq is None or freq == data.freq:
freq = data.freq
data = data.values
else:
base1, _ = _gfc(data.freq)
base2, _ = _gfc(freq)
data = plib.period_asfreq_arr(data.values, base1, base2, 1)
else:
if freq is None and len(data) > 0:
freq = getattr(data[0], "freq", None)
if freq is None:
raise ValueError(
("freq not specified and cannot be inferred from first element")
)
if np.issubdtype(data.dtype, np.datetime64):
data = dt64arr_to_periodarr(data, freq)
elif data.dtype == np.int64:
pass
else:
try:
data = com._ensure_int64(data)
except (TypeError, ValueError):
data = com._ensure_object(data)
data = _get_ordinals(data, freq)
return data, freq
|
def _from_arraylike(cls, data, freq):
if not isinstance(data, np.ndarray):
if np.isscalar(data) or isinstance(data, Period):
raise ValueError(
"PeriodIndex() must be called with a "
"collection of some kind, %s was passed" % repr(data)
)
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
try:
data = np.array(data, dtype="i8")
except (TypeError, ValueError):
data = np.array(data, dtype="O")
if freq is None and len(data) > 0:
freq = getattr(data[0], "freq", None)
if freq is None:
raise ValueError(
("freq not specified and cannot be inferred from first element")
)
data = _period_unbox_array(data, check=freq)
else:
if isinstance(data, PeriodIndex):
if freq is None or freq == data.freq:
freq = data.freq
data = data.values
else:
base1, _ = _gfc(data.freq)
base2, _ = _gfc(freq)
data = plib.period_asfreq_arr(data.values, base1, base2, 1)
else:
if freq is None and len(data) > 0:
freq = getattr(data[0], "freq", None)
if freq is None:
raise ValueError(
("freq not specified and cannot be inferred from first element")
)
if np.issubdtype(data.dtype, np.datetime64):
data = dt64arr_to_periodarr(data, freq)
elif data.dtype == np.int64:
pass
else:
try:
data = data.astype("i8")
except (TypeError, ValueError):
data = data.astype("O")
data = _period_unbox_array(data, check=freq)
return data, freq
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def period_range(start=None, end=None, periods=None, freq="D", name=None):
"""
Return a fixed frequency datetime index, with day (calendar) as the default
frequency
Parameters
----------
start :
end :
periods : int, default None
Number of periods in the index
freq : str/DateOffset, default 'D'
Frequency alias
name : str, default None
Name for the resulting PeriodIndex
Returns
-------
prng : PeriodIndex
"""
return PeriodIndex(start=start, end=end, periods=periods, freq=freq, name=name)
|
def period_range(start=None, end=None, periods=None, freq="D"):
"""
Return a fixed frequency datetime index, with day (calendar) as the default
frequency
Parameters
----------
start :
end :
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
Returns
-------
"""
return PeriodIndex(start=start, end=end, periods=periods, freq=freq)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def tsplot(series, plotf, **kwargs):
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
"""
# Used inferred freq is possible, need a test case for inferred
if "ax" in kwargs:
ax = kwargs.pop("ax")
else:
import matplotlib.pyplot as plt
ax = plt.gca()
freq = _get_freq(ax, series)
# resample against axes freq if necessary
if freq is None: # pragma: no cover
raise ValueError("Cannot use dynamic axis without frequency info")
else:
# Convert DatetimeIndex to PeriodIndex
if isinstance(series.index, DatetimeIndex):
series = series.to_period(freq=freq)
freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf, kwargs)
# Set ax with freq info
_decorate_axes(ax, freq, kwargs)
# mask missing values
args = _maybe_mask(series)
# how to make sure ax.clear() flows through?
if not hasattr(ax, "_plot_data"):
ax._plot_data = []
ax._plot_data.append((series, kwargs))
# styles
style = kwargs.pop("style", None)
if style is not None:
args.append(style)
lines = plotf(ax, *args, **kwargs)
label = kwargs.get("label", None)
# set date formatter, locators and rescale limits
format_dateaxis(ax, ax.freq)
left, right = _get_xlim(ax.get_lines())
ax.set_xlim(left, right)
return lines
|
def tsplot(series, plotf, **kwargs):
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
"""
# Used inferred freq is possible, need a test case for inferred
if "ax" in kwargs:
ax = kwargs.pop("ax")
else:
import matplotlib.pyplot as plt
ax = plt.gca()
freq = _get_freq(ax, series)
# resample against axes freq if necessary
if freq is None: # pragma: no cover
raise ValueError("Cannot use dynamic axis without frequency info")
else:
ax_freq = getattr(ax, "freq", None)
if (ax_freq is not None) and (freq != ax_freq):
if frequencies.is_subperiod(freq, ax_freq): # downsample
how = kwargs.pop("how", "last")
series = series.resample(ax_freq, how=how)
elif frequencies.is_superperiod(freq, ax_freq):
series = series.resample(ax_freq)
else: # one freq is weekly
how = kwargs.pop("how", "last")
series = series.resample("D", how=how, fill_method="pad")
series = series.resample(ax_freq, how=how, fill_method="pad")
freq = ax_freq
# Convert DatetimeIndex to PeriodIndex
if isinstance(series.index, DatetimeIndex):
series = series.to_period(freq=freq)
style = kwargs.pop("style", None)
# Specialized ts plotting attributes for Axes
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
ax.legendlabels = [kwargs.get("label", None)]
ax.view_interval = None
ax.date_axis_info = None
# format args and lot
args = _maybe_mask(series)
if style is not None:
args.append(style)
plotf(ax, *args, **kwargs)
format_dateaxis(ax, ax.freq)
left, right = _get_xlim(ax.get_lines())
ax.set_xlim(left, right)
return ax
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def initialize_options(self):
self.all = True
self._clean_me = []
self._clean_trees = []
self._clean_exclude = ["np_datetime.c", "np_datetime_strings.c", "period.c"]
for root, dirs, files in list(os.walk("pandas")):
for f in files:
if f in self._clean_exclude:
continue
if os.path.splitext(f)[-1] in (".pyc", ".so", ".o", ".pyd", ".c", ".orig"):
self._clean_me.append(pjoin(root, f))
for d in dirs:
if d == "__pycache__":
self._clean_trees.append(pjoin(root, d))
for d in ("build",):
if os.path.exists(d):
self._clean_trees.append(d)
|
def initialize_options(self):
self.all = True
self._clean_me = []
self._clean_trees = []
self._clean_exclude = ["np_datetime.c", "np_datetime_strings.c", "period.c"]
for root, dirs, files in list(os.walk("pandas")):
for f in files:
if f in self._clean_exclude:
continue
if os.path.splitext(f)[-1] in (".pyc", ".so", ".o", ".pyd", ".c"):
self._clean_me.append(pjoin(root, f))
for d in dirs:
if d == "__pycache__":
self._clean_trees.append(pjoin(root, d))
for d in ("build",):
if os.path.exists(d):
self._clean_trees.append(d)
|
https://github.com/pandas-dev/pandas/issues/1628
|
In [1]: from pandas import *
In [2]: a = DataFrame(columns=['column1'], data=[1])
In [3]: b = DataFrame(columns=['column1'])
In [4]: a.merge(b, on=['column1'], how='left')
IndexError Traceback (most recent call last)
IndexError: index out of range for array
|
IndexError
|
def _dt_index_cmp(opname):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
def wrapper(self, other):
func = getattr(super(DatetimeIndex, self), opname)
if isinstance(other, datetime):
func = getattr(self, opname)
other = _to_m8(other)
elif isinstance(other, list):
other = DatetimeIndex(other)
elif not isinstance(other, np.ndarray):
other = _ensure_datetime64(other)
result = func(other)
try:
return result.view(np.ndarray)
except:
return result
return wrapper
|
def _dt_index_cmp(opname):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
def wrapper(self, other):
if isinstance(other, datetime):
func = getattr(self, opname)
result = func(_to_m8(other))
elif isinstance(other, np.ndarray):
func = getattr(super(DatetimeIndex, self), opname)
result = func(other)
else:
other = _ensure_datetime64(other)
func = getattr(super(DatetimeIndex, self), opname)
result = func(other)
try:
return result.view(np.ndarray)
except:
return result
return wrapper
|
https://github.com/pandas-dev/pandas/issues/1430
|
from pandas import *
import numpy as np
periods = 1000
ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods)
df = DataFrame({'high': np.arange(periods), 'low': np.arange(periods)}, index=ind)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
In [9]: grouped.groups
ValueError Traceback (most recent call last)
<ipython-input-9-aec229cf123a> in <module>()
----> 1 grouped.groups
~/pandas/pandas/core/groupby.pyc in groups(self)
150 @property
151 def groups(self):
--> 152 return self.grouper.groups
153
154 @property
~/pandas/pandas/core/index.pyc in groupby(self, to_groupby)
740
741 def groupby(self, to_groupby):
--> 742 return self._groupby(self.values, to_groupby)
743
744 def map(self, mapper):
...
...
...
~/pandas/pandas/lib.so in pandas.lib.groupby_arrays (pandas/src/tseries.c:67246)()
ValueError: Buffer dtype mismatch, expected 'int64_t' but got Python object
|
ValueError
|
def wrapper(self, other):
func = getattr(super(DatetimeIndex, self), opname)
if isinstance(other, datetime):
func = getattr(self, opname)
other = _to_m8(other)
elif isinstance(other, list):
other = DatetimeIndex(other)
elif not isinstance(other, np.ndarray):
other = _ensure_datetime64(other)
result = func(other)
try:
return result.view(np.ndarray)
except:
return result
|
def wrapper(self, other):
if isinstance(other, datetime):
func = getattr(self, opname)
result = func(_to_m8(other))
elif isinstance(other, np.ndarray):
func = getattr(super(DatetimeIndex, self), opname)
result = func(other)
else:
other = _ensure_datetime64(other)
func = getattr(super(DatetimeIndex, self), opname)
result = func(other)
try:
return result.view(np.ndarray)
except:
return result
|
https://github.com/pandas-dev/pandas/issues/1430
|
from pandas import *
import numpy as np
periods = 1000
ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods)
df = DataFrame({'high': np.arange(periods), 'low': np.arange(periods)}, index=ind)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
In [9]: grouped.groups
ValueError Traceback (most recent call last)
<ipython-input-9-aec229cf123a> in <module>()
----> 1 grouped.groups
~/pandas/pandas/core/groupby.pyc in groups(self)
150 @property
151 def groups(self):
--> 152 return self.grouper.groups
153
154 @property
~/pandas/pandas/core/index.pyc in groupby(self, to_groupby)
740
741 def groupby(self, to_groupby):
--> 742 return self._groupby(self.values, to_groupby)
743
744 def map(self, mapper):
...
...
...
~/pandas/pandas/lib.so in pandas.lib.groupby_arrays (pandas/src/tseries.c:67246)()
ValueError: Buffer dtype mismatch, expected 'int64_t' but got Python object
|
ValueError
|
def tsplot(series, plotf, *args, **kwargs):
"""
Plots a Series on the given Matplotlib axes object
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same args and kwargs as Axes.plot
"""
# Used inferred freq is possible, need a test case for inferred
freq = getattr(series.index, "freq", None)
if freq is None and hasattr(series.index, "inferred_freq"):
freq = series.index.inferred_freq
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = frequencies.get_base_alias(freq)
freq = frequencies.to_calendar_freq(freq)
# Convert DatetimeIndex to PeriodIndex
if isinstance(series.index, DatetimeIndex):
idx = series.index.to_period(freq=freq)
series = Series(series.values, idx, name=series.name)
if not isinstance(series.index, PeriodIndex):
# try to get it to DatetimeIndex then to period
if series.index.inferred_type == "datetime":
idx = DatetimeIndex(series.index).to_period(freq=freq)
series = Series(series.values, idx, name=series.name)
else:
raise TypeError(
"series argument to tsplot must have DatetimeIndex or PeriodIndex"
)
if freq != series.index.freq:
series = series.asfreq(freq)
series = series.dropna()
if "ax" in kwargs:
ax = kwargs.pop("ax")
else:
ax = plt.gca()
# Specialized ts plotting attributes for Axes
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
xaxis.converter = DateConverter
ax.legendlabels = [kwargs.get("label", None)]
ax.view_interval = None
ax.date_axis_info = None
# format args and lot
args = _check_plot_params(series, series.index, freq, *args)
plotted = plotf(ax, *args, **kwargs)
format_dateaxis(ax, ax.freq)
# when adding a right axis (using add_yaxis), for some reason the
# x axis limits don't get properly set. This gets around the problem
xlim = ax.get_xlim()
if xlim[0] == 0.0 and xlim[1] == 1.0:
# if xlim still at default values, autoscale the axis
ax.autoscale_view()
left = series.index[0] # get_datevalue(series.index[0], freq)
right = series.index[-1] # get_datevalue(series.index[-1], freq)
ax.set_xlim(left, right)
return plotted
|
def tsplot(series, plotf, *args, **kwargs):
"""
Plots a Series on the given Matplotlib axes object
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same args and kwargs as Axes.plot
"""
# Used inferred freq is possible, need a test case for inferred
freq = getattr(series.index, "freq", None)
if freq is None and hasattr(series.index, "inferred_freq"):
freq = series.index.inferred_freq
if isinstance(freq, DateOffset):
freq = freq.rule_code
freq = frequencies.to_calendar_freq(freq)
# Convert DatetimeIndex to PeriodIndex
if isinstance(series.index, DatetimeIndex):
idx = series.index.to_period(freq=freq)
series = Series(series.values, idx, name=series.name)
if not isinstance(series.index, PeriodIndex):
# try to get it to DatetimeIndex then to period
if series.index.inferred_type == "datetime":
idx = DatetimeIndex(series.index).to_period(freq=freq)
series = Series(series.values, idx, name=series.name)
else:
raise TypeError(
"series argument to tsplot must have DatetimeIndex or PeriodIndex"
)
if freq != series.index.freq:
series = series.asfreq(freq)
series = series.dropna()
if "ax" in kwargs:
ax = kwargs.pop("ax")
else:
ax = plt.gca()
# Specialized ts plotting attributes for Axes
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
xaxis.converter = DateConverter
ax.legendlabels = [kwargs.get("label", None)]
ax.view_interval = None
ax.date_axis_info = None
# format args and lot
args = _check_plot_params(series, series.index, freq, *args)
plotted = plotf(ax, *args, **kwargs)
format_dateaxis(ax, ax.freq)
# when adding a right axis (using add_yaxis), for some reason the
# x axis limits don't get properly set. This gets around the problem
xlim = ax.get_xlim()
if xlim[0] == 0.0 and xlim[1] == 1.0:
# if xlim still at default values, autoscale the axis
ax.autoscale_view()
left = series.index[0] # get_datevalue(series.index[0], freq)
right = series.index[-1] # get_datevalue(series.index[-1], freq)
ax.set_xlim(left, right)
return plotted
|
https://github.com/pandas-dev/pandas/issues/1357
|
In [4]: ts.plot()
Out[4]: <matplotlib.axes.AxesSubplot at 0x41cc310>
In [5]: ts.index.fr
ts.index.freq ts.index.freqstr
In [5]: ts.index.freq
Out[5]: <10 Days>
In [6]: ts.index.freq = None
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/<ipython-input-6-d89557d7f8e5> in <module>()
----> 1 ts.index.freq = None
AttributeError: can't set attribute
In [7]: ts.index.offset = None
In [8]: ts
Out[8]:
2000-01-01 2.963378
2000-01-11 -0.685998
2000-01-21 0.392812
2000-01-31 -0.143893
2000-02-10 -1.162283
2000-02-20 -0.460511
2000-03-01 -0.751741
2000-03-11 -0.567428
2000-03-21 -0.439105
2000-03-31 0.040322
2000-04-10 -0.929786
2000-04-20 -0.620005
2000-04-30 0.482502
2000-05-10 0.109013
2000-05-20 0.657817
2000-05-30 -0.668386
2000-06-09 -1.797074
2000-06-19 -0.197738
2000-06-29 -0.312481
2000-07-09 1.240477
2000-07-19 0.447583
2000-07-29 0.188429
2000-08-08 -0.117540
2000-08-18 -1.145495
2000-08-28 0.479124
2000-09-07 0.309968
2000-09-17 0.769928
2000-09-27 -0.501629
2000-10-07 0.164322
2000-10-17 -0.554113
2000-10-27 0.468588
2000-11-06 0.857458
2000-11-16 0.508256
2000-11-26 -0.452384
2000-12-06 1.732538
2000-12-16 0.695495
2000-12-26 0.441233
2001-01-05 -0.271561
In [9]: ts.index.offset = None
In [10]: ts.plot()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/wesm/Dropbox/book/svn/<ipython-input-10-132f3667ee95> in <module>()
----> 1 ts.plot()
/home/wesm/code/pandas/pandas/tools/plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, logy, **kwds)
758 legend=False, grid=grid, label=label, **kwds)
759
--> 760 plot_obj.generate()
761 plot_obj.draw()
762
/home/wesm/code/pandas/pandas/tools/plotting.pyc in generate(self)
239 self._compute_plot_data()
240 self._setup_subplots()
--> 241 self._make_plot()
242 self._post_plot_logic()
243 self._adorn_subplots()
/home/wesm/code/pandas/pandas/tools/plotting.pyc in _make_plot(self)
426 if self.use_index and self.has_ts_index:
427 data = self._maybe_convert_index(self.data)
--> 428 self._make_ts_plot(data)
429 else:
430 x = self._get_xticks()
/home/wesm/code/pandas/pandas/tools/plotting.pyc in _make_ts_plot(self, data, **kwargs)
478
479 label = com._stringify(self.label)
--> 480 tsplot(data, plotf, ax=ax, label=label, **kwargs)
481 ax.grid(self.grid)
482 else:
/home/wesm/code/pandas/pandas/tseries/plotting.pyc in tsplot(series, plotf, *args, **kwargs)
114 # format args and lot
115 args = _check_plot_params(series, series.index, freq, *args)
--> 116 plotted = plotf(ax, *args, **kwargs)
117
118 format_dateaxis(ax, ax.freq)
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in plot(self, *args, **kwargs)
3892 lines = []
3893
-> 3894 for line in self._get_lines(*args, **kwargs):
3895 self.add_line(line)
3896 lines.append(line)
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in _grab_next_args(self, *args, **kwargs)
321 return
322 if len(remaining) <= 3:
--> 323 for seg in self._plot_args(remaining, kwargs):
324 yield seg
325 return
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in _plot_args(self, tup, kwargs)
299 x = np.arange(y.shape[0], dtype=float)
300
--> 301 x, y = self._xy_from_xy(x, y)
302
303 if self.command == 'plot':
/home/wesm/code/repos/matplotlib/lib/matplotlib/axes.pyc in _xy_from_xy(self, x, y)
215 def _xy_from_xy(self, x, y):
216 if self.axes.xaxis is not None and self.axes.yaxis is not None:
--> 217 bx = self.axes.xaxis.update_units(x)
218 by = self.axes.yaxis.update_units(y)
219
/home/wesm/code/repos/matplotlib/lib/matplotlib/axis.pyc in update_units(self, data)
1276 """
1277
-> 1278 converter = munits.registry.get_converter(data)
1279 if converter is None:
1280 return False
/home/wesm/code/repos/matplotlib/lib/matplotlib/units.pyc in get_converter(self, x)
131
132 if converter is None and iterable(x):
--> 133 for thisx in x:
134 # Make sure that recursing might actually lead to a solution, if
135 # we are just going to re-examine another item of the same kind,
/home/wesm/code/pandas/pandas/tseries/period.pyc in __iter__(self)
685 def __iter__(self):
686 for val in self.values:
--> 687 yield Period(ordinal=val, freq=self.freq)
688
689 @property
/home/wesm/code/pandas/pandas/tseries/period.pyc in __init__(self, value, freq, ordinal, year, month, quarter, day, hour, minute, second)
136 base, mult = _gfc(freq)
137 if mult != 1:
--> 138 raise ValueError('Only mult == 1 supported')
139
140 if self.ordinal is None:
ValueError: Only mult == 1 supported
|
AttributeError
|
def get_loc(self, key):
"""
Get integer location for requested label
Returns
-------
loc : int
"""
try:
return self._engine.get_loc(key)
except KeyError:
try:
return self._get_string_slice(key)
except (TypeError, KeyError):
pass
if isinstance(key, time):
return self._indices_at_time(key)
stamp = Timestamp(key)
try:
return self._engine.get_loc(stamp)
except KeyError:
raise KeyError(stamp)
|
def get_loc(self, key):
"""y
Get integer location for requested label
Returns
-------
loc : int
"""
try:
return self._engine.get_loc(key)
except KeyError:
try:
return self._get_string_slice(key)
except (TypeError, KeyError):
pass
if isinstance(key, time):
return self._indices_at_time(key)
stamp = Timestamp(key)
try:
return self._engine.get_loc(stamp)
except KeyError:
raise KeyError(stamp)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def match(to_match, values, na_sentinel=-1):
"""
Compute locations of to_match into values
Parameters
----------
to_match : array-like
values to find positions of
values : array-like
Unique set of values
na_sentinel : int, default -1
Value to mark "not found"
Examples
--------
Returns
-------
match : ndarray of integers
"""
values = np.asarray(values)
if issubclass(values.dtype.type, basestring):
values = np.array(values, dtype="O")
f = lambda htype, caster: _match_generic(to_match, values, htype, caster)
return _hashtable_algo(f, values.dtype)
|
def match(values, index):
"""
Parameters
----------
Returns
-------
match : ndarray
"""
if com.is_float_dtype(index):
return _match_generic(values, index, lib.Float64HashTable, com._ensure_float64)
elif com.is_integer_dtype(index):
return _match_generic(values, index, lib.Int64HashTable, com._ensure_int64)
else:
return _match_generic(values, index, lib.PyObjectHashTable, com._ensure_object)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def unique(values):
"""
Compute unique values (not necessarily sorted) efficiently from input array
of values
Parameters
----------
values : array-like
Returns
-------
uniques
"""
f = lambda htype, caster: _unique_generic(values, htype, caster)
return _hashtable_algo(f, values.dtype)
|
def unique(values):
""" """
pass
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def count(values, uniques=None):
f = lambda htype, caster: _count_generic(values, htype, caster)
if uniques is not None:
raise NotImplementedError
else:
return _hashtable_algo(f, values.dtype)
|
def count(values, uniques=None):
if uniques is not None:
raise NotImplementedError
else:
if com.is_float_dtype(values):
return _count_generic(values, lib.Float64HashTable, com._ensure_float64)
elif com.is_integer_dtype(values):
return _count_generic(values, lib.Int64HashTable, com._ensure_int64)
else:
return _count_generic(values, lib.PyObjectHashTable, com._ensure_object)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _count_generic(values, table_type, type_caster):
from pandas.core.series import Series
values = type_caster(values)
table = table_type(len(values))
uniques, labels, counts = table.factorize(values)
return Series(counts, index=uniques)
|
def _count_generic(values, table_type, type_caster):
values = type_caster(values)
table = table_type(len(values))
uniques, labels, counts = table.factorize(values)
return Series(counts, index=uniques)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def factorize(values, sort=False, order=None, na_sentinel=-1):
"""
Encode input values as an enumerated type or categorical variable
Parameters
----------
values : sequence
sort :
order :
Returns
-------
"""
values = np.asarray(values)
is_datetime = com.is_datetime64_dtype(values)
hash_klass, values = _get_data_algo(values, _hashtables)
uniques = []
table = hash_klass(len(values))
labels, counts = table.get_labels(values, uniques, 0, na_sentinel)
labels = com._ensure_platform_int(labels)
uniques = com._asarray_tuplesafe(uniques)
if sort and len(counts) > 0:
sorter = uniques.argsort()
reverse_indexer = np.empty(len(sorter), dtype=np.int_)
reverse_indexer.put(sorter, np.arange(len(sorter)))
mask = labels < 0
labels = reverse_indexer.take(labels)
np.putmask(labels, mask, -1)
uniques = uniques.take(sorter)
counts = counts.take(sorter)
if is_datetime:
uniques = np.array(uniques, dtype="M8[ns]")
return labels, uniques, counts
|
def factorize(values, sort=False, order=None, na_sentinel=-1):
"""
Encode input values as an enumerated type or categorical variable
Parameters
----------
values : sequence
sort :
order :
Returns
-------
"""
hash_klass, values = _get_data_algo(values, _hashtables)
uniques = []
table = hash_klass(len(values))
labels, counts = table.get_labels(values, uniques, 0, na_sentinel)
uniques = com._asarray_tuplesafe(uniques)
if sort and len(counts) > 0:
sorter = uniques.argsort()
reverse_indexer = np.empty(len(sorter), dtype=np.int32)
reverse_indexer.put(sorter, np.arange(len(sorter)))
mask = labels < 0
labels = reverse_indexer.take(labels)
np.putmask(labels, mask, -1)
uniques = uniques.take(sorter)
counts = counts.take(sorter)
return labels, uniques, counts
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def value_counts(values, sort=True, ascending=False):
"""
Compute a histogram of the counts of non-null values
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
from collections import defaultdict
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
keys, counts = lib.value_count_int64(values)
result = Series(counts, index=keys)
else:
counter = defaultdict(lambda: 0)
values = values[com.notnull(values)]
for value in values:
counter[value] += 1
result = Series(counter)
if sort:
result.sort()
if not ascending:
result = result[::-1]
return result
|
def value_counts(values, sort=True, ascending=False):
"""
Compute a histogram of the counts of non-null values
Returns
-------
value_counts : Series
"""
from collections import defaultdict
if com.is_integer_dtype(values.dtype):
values = com._ensure_int64(values)
keys, counts = lib.value_count_int64(values)
result = Series(counts, index=keys)
else:
counter = defaultdict(lambda: 0)
values = values[com.notnull(values)]
for value in values:
counter[value] += 1
result = Series(counter)
if sort:
result.sort()
if not ascending:
result = result[::-1]
return result
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _get_data_algo(values, func_map):
if com.is_float_dtype(values):
f = func_map["float64"]
values = com._ensure_float64(values)
elif com.is_datetime64_dtype(values):
f = func_map["int64"]
values = values.view("i8")
elif com.is_integer_dtype(values):
f = func_map["int64"]
values = com._ensure_int64(values)
else:
f = func_map["generic"]
values = com._ensure_object(values)
return f, values
|
def _get_data_algo(values, func_map):
if com.is_float_dtype(values):
f = func_map["float64"]
values = com._ensure_float64(values)
elif com.is_integer_dtype(values):
f = func_map["int64"]
values = com._ensure_int64(values)
else:
f = func_map["generic"]
values = com._ensure_object(values)
return f, values
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def isnull(obj):
"""
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if lib.isscalar(obj):
return lib.checknull(obj)
from pandas.core.generic import PandasObject
from pandas import Series
if isinstance(obj, np.ndarray):
if obj.dtype.kind in ("O", "S"):
# Working around NumPy ticket 1542
shape = obj.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(obj.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
elif obj.dtype == np.dtype("M8[ns]"):
# this is the NaT pattern
result = np.array(obj).view("i8") == lib.iNaT
else:
result = -np.isfinite(obj)
return result
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
else:
return obj is None
|
def isnull(obj):
"""
Replacement for numpy.isnan / -numpy.isfinite which is suitable
for use on object arrays.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if np.isscalar(obj) or obj is None:
return lib.checknull(obj)
from pandas.core.generic import PandasObject
from pandas import Series
if isinstance(obj, np.ndarray):
if obj.dtype.kind in ("O", "S"):
# Working around NumPy ticket 1542
shape = obj.shape
result = np.empty(shape, dtype=bool)
vec = lib.isnullobj(obj.ravel())
result[:] = vec.reshape(shape)
if isinstance(obj, Series):
result = Series(result, index=obj.index, copy=False)
else:
result = -np.isfinite(obj)
return result
elif isinstance(obj, PandasObject):
# TODO: optimize for DataFrame, etc.
return obj.apply(isnull)
else:
return obj is None
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _get_take2d_function(dtype_str, axis=0):
if axis == 0:
return _take2d_axis0_dict[dtype_str]
elif axis == 1:
return _take2d_axis1_dict[dtype_str]
elif axis == "multi":
return _take2d_multi_dict[dtype_str]
else: # pragma: no cover
raise ValueError("bad axis: %s" % axis)
|
def _get_take2d_function(dtype_str, axis=0):
if axis == 0:
return _take2d_axis0_dict[dtype_str]
else:
return _take2d_axis1_dict[dtype_str]
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def take_1d(arr, indexer, out=None, fill_value=np.nan):
"""
Specialized Cython take which sets NaN values in one pass
"""
dtype_str = arr.dtype.name
n = len(indexer)
indexer = _ensure_int64(indexer)
out_passed = out is not None
take_f = _take1d_dict.get(dtype_str)
if dtype_str in ("int32", "int64", "bool"):
try:
if out is None:
out = np.empty(n, dtype=arr.dtype)
take_f(arr, _ensure_int64(indexer), out=out, fill_value=fill_value)
except ValueError:
mask = indexer == -1
if len(arr) == 0:
if not out_passed:
out = np.empty(n, dtype=arr.dtype)
else:
out = ndtake(arr, indexer, out=out)
if mask.any():
if out_passed:
raise Exception("out with dtype %s does not support NA" % out.dtype)
out = _maybe_upcast(out)
np.putmask(out, mask, fill_value)
elif dtype_str in ("float64", "object", "datetime64[ns]"):
if out is None:
out = np.empty(n, dtype=arr.dtype)
take_f(arr, _ensure_int64(indexer), out=out, fill_value=fill_value)
else:
out = ndtake(arr, indexer, out=out)
mask = indexer == -1
if mask.any():
if out_passed:
raise Exception("out with dtype %s does not support NA" % out.dtype)
out = _maybe_upcast(out)
np.putmask(out, mask, fill_value)
return out
|
def take_1d(arr, indexer, out=None, fill_value=np.nan):
"""
Specialized Cython take which sets NaN values in one pass
"""
dtype_str = arr.dtype.name
n = len(indexer)
if not isinstance(indexer, np.ndarray):
# Cython methods expects 32-bit integers
indexer = np.array(indexer, dtype=np.int32)
out_passed = out is not None
if dtype_str in ("int32", "int64", "bool"):
try:
if out is None:
out = np.empty(n, dtype=arr.dtype)
take_f = _take1d_dict[dtype_str]
take_f(arr, indexer, out=out, fill_value=fill_value)
except ValueError:
mask = indexer == -1
if len(arr) == 0:
if not out_passed:
out = np.empty(n, dtype=arr.dtype)
else:
out = arr.take(indexer, out=out)
if mask.any():
if out_passed:
raise Exception("out with dtype %s does not support NA" % out.dtype)
out = _maybe_upcast(out)
np.putmask(out, mask, fill_value)
elif dtype_str in ("float64", "object"):
if out is None:
out = np.empty(n, dtype=arr.dtype)
take_f = _take1d_dict[dtype_str]
take_f(arr, indexer, out=out, fill_value=fill_value)
else:
out = arr.take(indexer, out=out)
mask = indexer == -1
if mask.any():
if out_passed:
raise Exception("out with dtype %s does not support NA" % out.dtype)
out = _maybe_upcast(out)
np.putmask(out, mask, fill_value)
return out
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def take_2d(
arr, indexer, out=None, mask=None, needs_masking=None, axis=0, fill_value=np.nan
):
"""
Specialized Cython take which sets NaN values in one pass
"""
dtype_str = arr.dtype.name
out_shape = list(arr.shape)
out_shape[axis] = len(indexer)
out_shape = tuple(out_shape)
if not isinstance(indexer, np.ndarray):
indexer = np.array(indexer, dtype=np.int64)
if dtype_str in ("int32", "int64", "bool"):
if mask is None:
mask = indexer == -1
needs_masking = mask.any()
if needs_masking:
# upcasting may be required
result = ndtake(arr, indexer, axis=axis, out=out)
result = _maybe_mask(
result,
mask,
needs_masking,
axis=axis,
out_passed=out is not None,
fill_value=fill_value,
)
return result
else:
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis=axis)
take_f(arr, _ensure_int64(indexer), out=out, fill_value=fill_value)
return out
elif dtype_str in ("float64", "object", "datetime64[ns]"):
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis=axis)
take_f(arr, _ensure_int64(indexer), out=out, fill_value=fill_value)
return out
else:
if mask is None:
mask = indexer == -1
needs_masking = mask.any()
# GH #486
if out is not None and arr.dtype != out.dtype:
arr = arr.astype(out.dtype)
result = ndtake(arr, indexer, axis=axis, out=out)
result = _maybe_mask(
result,
mask,
needs_masking,
axis=axis,
out_passed=out is not None,
fill_value=fill_value,
)
return result
|
def take_2d(
arr, indexer, out=None, mask=None, needs_masking=None, axis=0, fill_value=np.nan
):
"""
Specialized Cython take which sets NaN values in one pass
"""
dtype_str = arr.dtype.name
out_shape = list(arr.shape)
out_shape[axis] = len(indexer)
out_shape = tuple(out_shape)
if not isinstance(indexer, np.ndarray):
# Cython methods expects 32-bit integers
indexer = np.array(indexer, dtype=np.int32)
if dtype_str in ("int32", "int64", "bool"):
if mask is None:
mask = indexer == -1
needs_masking = mask.any()
if needs_masking:
# upcasting may be required
result = arr.take(indexer, axis=axis, out=out)
result = _maybe_mask(
result,
mask,
needs_masking,
axis=axis,
out_passed=out is not None,
fill_value=fill_value,
)
return result
else:
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis=axis)
take_f(arr, indexer, out=out, fill_value=fill_value)
return out
elif dtype_str in ("float64", "object"):
if out is None:
out = np.empty(out_shape, dtype=arr.dtype)
take_f = _get_take2d_function(dtype_str, axis=axis)
take_f(arr, indexer, out=out, fill_value=fill_value)
return out
else:
if mask is None:
mask = indexer == -1
needs_masking = mask.any()
# GH #486
if out is not None and arr.dtype != out.dtype:
arr = arr.astype(out.dtype)
result = arr.take(indexer, axis=axis, out=out)
result = _maybe_mask(
result,
mask,
needs_masking,
axis=axis,
out_passed=out is not None,
fill_value=fill_value,
)
return result
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def take_fast(arr, indexer, mask, needs_masking, axis=0, out=None, fill_value=np.nan):
if arr.ndim == 2:
return take_2d(
arr,
indexer,
out=out,
mask=mask,
needs_masking=needs_masking,
axis=axis,
fill_value=fill_value,
)
indexer = _ensure_platform_int(indexer)
result = ndtake(arr, indexer, axis=axis, out=out)
result = _maybe_mask(
result,
mask,
needs_masking,
axis=axis,
out_passed=out is not None,
fill_value=fill_value,
)
return result
|
def take_fast(arr, indexer, mask, needs_masking, axis=0, out=None, fill_value=np.nan):
if arr.ndim == 2:
return take_2d(
arr,
indexer,
out=out,
mask=mask,
needs_masking=needs_masking,
axis=axis,
fill_value=fill_value,
)
result = arr.take(indexer, axis=axis, out=out)
result = _maybe_mask(
result,
mask,
needs_masking,
axis=axis,
out_passed=out is not None,
fill_value=fill_value,
)
return result
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def iterpairs(seq):
"""
Parameters
----------
seq: sequence
Returns
-------
iterator returning overlapping pairs of elements
Example
-------
>>> iterpairs([1, 2, 3, 4])
[(1, 2), (2, 3), (3, 4)
"""
# input may not be sliceable
seq_it = iter(seq)
seq_it_next = iter(seq)
_ = next(seq_it_next)
return itertools.izip(seq_it, seq_it_next)
|
def iterpairs(seq):
"""
Parameters
----------
seq: sequence
Returns
-------
iterator returning overlapping pairs of elements
Example
-------
>>> iterpairs([1, 2, 3, 4])
[(1, 2), (2, 3), (3, 4)
"""
# input may not be sliceable
seq_it = iter(seq)
seq_it_next = iter(seq)
_ = seq_it_next.next()
return itertools.izip(seq_it, seq_it_next)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def is_integer_dtype(arr_or_dtype):
if isinstance(arr_or_dtype, np.dtype):
tipo = arr_or_dtype.type
else:
tipo = arr_or_dtype.dtype.type
return issubclass(tipo, np.integer) and not issubclass(tipo, np.datetime64)
|
def is_integer_dtype(arr_or_dtype):
if isinstance(arr_or_dtype, np.dtype):
tipo = arr_or_dtype.type
else:
tipo = arr_or_dtype.dtype.type
return issubclass(tipo, np.integer)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _astype_nansafe(arr, dtype):
if isinstance(dtype, basestring):
dtype = np.dtype(dtype)
if issubclass(arr.dtype.type, np.datetime64):
if dtype == object:
return lib.ints_to_pydatetime(arr.view(np.int64))
elif np.issubdtype(arr.dtype, np.floating) and np.issubdtype(dtype, np.integer):
if np.isnan(arr).any():
raise ValueError("Cannot convert NA to integer")
return arr.astype(dtype)
|
def _astype_nansafe(arr, dtype):
if np.issubdtype(arr.dtype, np.floating) and np.issubdtype(dtype, np.integer):
if np.isnan(arr).any():
raise ValueError("Cannot convert NA to integer")
return arr.astype(dtype)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def __new__(
cls,
start=None,
end=None,
periods=None,
offset=datetools.bday,
time_rule=None,
tzinfo=None,
name=None,
**kwds,
):
import warnings
warnings.warn("DateRange is deprecated, use DatetimeIndex instead", FutureWarning)
if time_rule is None:
time_rule = kwds.get("timeRule")
if time_rule is not None:
offset = datetools.get_offset(time_rule)
return DatetimeIndex(
start=start,
end=end,
periods=periods,
freq=offset,
tzinfo=tzinfo,
name=name,
**kwds,
)
|
def __new__(
cls,
start=None,
end=None,
periods=None,
offset=datetools.bday,
time_rule=None,
tzinfo=None,
name=None,
**kwds,
):
time_rule = kwds.get("timeRule", time_rule)
if time_rule is not None:
offset = datetools.getOffset(time_rule)
if time_rule is None:
if offset in datetools._offsetNames:
time_rule = datetools._offsetNames[offset]
# Cachable
if not start:
start = kwds.get("begin")
if not periods:
periods = kwds.get("nPeriods")
start = datetools.to_datetime(start)
end = datetools.to_datetime(end)
if start is not None and not isinstance(start, datetime):
raise ValueError("Failed to convert %s to datetime" % start)
if end is not None and not isinstance(end, datetime):
raise ValueError("Failed to convert %s to datetime" % end)
# inside cache range. Handle UTC case
useCache = _will_use_cache(offset)
start, end, tzinfo = _figure_out_timezone(start, end, tzinfo)
useCache = useCache and _naive_in_cache_range(start, end)
if useCache:
index = cls._cached_range(
start, end, periods=periods, offset=offset, time_rule=time_rule, name=name
)
if tzinfo is None:
return index
else:
xdr = generate_range(
start=start, end=end, periods=periods, offset=offset, time_rule=time_rule
)
index = list(xdr)
if tzinfo is not None:
index = [d.replace(tzinfo=tzinfo) for d in index]
index = np.array(index, dtype=object, copy=False)
index = index.view(cls)
index.name = name
index.offset = offset
index.tzinfo = tzinfo
return index
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _expand_axes(self, key):
new_axes = []
for k, ax in zip(key, self.axes):
if k not in ax:
if type(k) != ax.dtype.type:
ax = ax.astype("O")
new_axes.append(ax.insert(len(ax), k))
else:
new_axes.append(ax)
return new_axes
|
def _expand_axes(self, key):
new_axes = []
for k, ax in zip(key, self.axes):
if k not in ax:
new_axes.append(ax.insert(len(ax), k))
else:
new_axes.append(ax)
return new_axes
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def truncate(self, before=None, after=None, copy=True):
"""Function truncate a sorted DataFrame / Series before and/or after
some particular dates.
Parameters
----------
before : date
Truncate before date
after : date
Truncate after date
Returns
-------
truncated : type of caller
"""
from pandas.tseries.tools import to_datetime
before = to_datetime(before)
after = to_datetime(after)
if before is not None and after is not None:
assert before <= after
result = self.ix[before:after]
if isinstance(self.index, MultiIndex):
result.index = self.index.truncate(before, after)
if copy:
result = result.copy()
return result
|
def truncate(self, before=None, after=None, copy=True):
"""Function truncate a sorted DataFrame / Series before and/or after
some particular dates.
Parameters
----------
before : date
Truncate before date
after : date
Truncate after date
Returns
-------
truncated : type of caller
"""
before = datetools.to_datetime(before)
after = datetools.to_datetime(after)
if before is not None and after is not None:
assert before <= after
result = self.ix[before:after]
if isinstance(self.index, MultiIndex):
result.index = self.index.truncate(before, after)
if copy:
result = result.copy()
return result
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def save(self, path):
com.save(self, path)
|
def save(self, path):
save(self, path)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def load(cls, path):
return com.load(path)
|
def load(cls, path):
return load(path)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def __getitem__(self, key):
if type(key) is tuple:
try:
return self.obj.get_value(*key)
except Exception:
pass
return self._getitem_tuple(key)
else:
return self._getitem_axis(key, axis=0)
|
def __getitem__(self, key):
if isinstance(key, tuple):
try:
return self.obj.get_value(*key)
except Exception:
pass
return self._getitem_tuple(key)
else:
return self._getitem_axis(key, axis=0)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _setitem_with_indexer(self, indexer, value):
# also has the side effect of consolidating in-place
if self.obj._is_mixed_type:
if not isinstance(indexer, tuple):
indexer = self._tuplify(indexer)
het_axis = self.obj._het_axis
het_idx = indexer[het_axis]
if isinstance(het_idx, (int, long)):
het_idx = [het_idx]
plane_indexer = indexer[:het_axis] + indexer[het_axis + 1 :]
item_labels = self.obj._get_axis(het_axis)
for item in item_labels[het_idx]:
data = self.obj[item]
data.values[plane_indexer] = value
else:
if isinstance(indexer, tuple):
indexer = _maybe_convert_ix(*indexer)
self.obj.values[indexer] = value
|
def _setitem_with_indexer(self, indexer, value):
# also has the side effect of consolidating in-place
if self.obj._is_mixed_type:
if not isinstance(indexer, tuple):
indexer = self._tuplify(indexer)
het_axis = self.obj._het_axis
het_idx = indexer[het_axis]
if isinstance(het_idx, (int, long)):
het_idx = [het_idx]
if not np.isscalar(value):
raise IndexingError(
"setting on mixed-type frames only allowed with scalar values"
)
plane_indexer = indexer[:het_axis] + indexer[het_axis + 1 :]
item_labels = self.obj._get_axis(het_axis)
for item in item_labels[het_idx]:
data = self.obj[item]
data.values[plane_indexer] = value
else:
if isinstance(indexer, tuple):
indexer = _maybe_convert_ix(*indexer)
self.obj.values[indexer] = value
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _getitem_tuple(self, tup):
try:
return self._getitem_lowerdim(tup)
except IndexingError:
pass
# ugly hack for GH #836
if self._multi_take_opportunity(tup):
return self._multi_take(tup)
# no shortcut needed
retval = self.obj
for i, key in enumerate(tup):
if i >= self.obj.ndim:
raise IndexingError("Too many indexers")
if _is_null_slice(key):
continue
retval = retval.ix._getitem_axis(key, axis=i)
return retval
|
def _getitem_tuple(self, tup):
try:
return self._getitem_lowerdim(tup)
except IndexingError:
pass
# no shortcut needed
retval = self.obj
for i, key in enumerate(tup):
if i >= self.obj.ndim:
raise IndexingError("Too many indexers")
if _is_null_slice(key):
continue
retval = retval.ix._getitem_axis(key, axis=i)
return retval
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _getitem_lowerdim(self, tup):
from pandas.core.frame import DataFrame
ax0 = self.obj._get_axis(0)
# a bit kludgy
if isinstance(ax0, MultiIndex):
try:
return self._get_label(tup, axis=0)
except TypeError:
# slices are unhashable
pass
except Exception:
if isinstance(tup[0], slice):
raise IndexingError
if tup[0] not in ax0: # and tup[0] not in ax0.levels[0]:
raise
# to avoid wasted computation
# df.ix[d1:d2, 0] -> columns first (True)
# df.ix[0, ['C', 'B', A']] -> rows first (False)
for i, key in enumerate(tup):
if _is_label_like(key) or isinstance(key, tuple):
section = self._getitem_axis(key, axis=i)
# might have been a MultiIndex
if section.ndim == self.ndim:
new_key = tup[:i] + (_NS,) + tup[i + 1 :]
# new_key = tup[:i] + tup[i+1:]
else:
new_key = tup[:i] + tup[i + 1 :]
# unfortunately need an odious kludge here because of
# DataFrame transposing convention
if isinstance(section, DataFrame) and i > 0 and len(new_key) == 2:
a, b = new_key
new_key = b, a
if len(new_key) == 1:
(new_key,) = new_key
return section.ix[new_key]
raise IndexingError("not applicable")
|
def _getitem_lowerdim(self, tup):
from pandas.core.frame import DataFrame
ax0 = self.obj._get_axis(0)
# a bit kludgy
if isinstance(ax0, MultiIndex):
try:
return self._get_label(tup, axis=0)
except TypeError:
# slices are unhashable
pass
except Exception:
if isinstance(tup[0], slice):
raise IndexingError
if tup[0] not in ax0:
raise
# to avoid wasted computation
# df.ix[d1:d2, 0] -> columns first (True)
# df.ix[0, ['C', 'B', A']] -> rows first (False)
for i, key in enumerate(tup):
if _is_label_like(key) or isinstance(key, tuple):
section = self._getitem_axis(key, axis=i)
# might have been a MultiIndex
if section.ndim == self.ndim:
new_key = tup[:i] + (_NS,) + tup[i + 1 :]
# new_key = tup[:i] + tup[i+1:]
else:
new_key = tup[:i] + tup[i + 1 :]
# unfortunately need an odious kludge here because of
# DataFrame transposing convention
if isinstance(section, DataFrame) and i > 0 and len(new_key) == 2:
a, b = new_key
new_key = b, a
if len(new_key) == 1:
(new_key,) = new_key
return section.ix[new_key]
raise IndexingError("not applicable")
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _getitem_iterable(self, key, axis=0):
labels = self.obj._get_axis(axis)
def _reindex(keys, level=None):
try:
return self.obj.reindex_axis(keys, axis=axis, level=level)
except AttributeError:
# Series
assert axis == 0
return self.obj.reindex(keys, level=level)
if com._is_bool_indexer(key):
key = _check_bool_indexer(labels, key)
return _reindex(labels[np.asarray(key)])
else:
if isinstance(key, Index):
# want Index objects to pass through untouched
keyarr = key
else:
# asarray can be unsafe, NumPy strings are weird
keyarr = _asarray_tuplesafe(key)
if _is_integer_dtype(keyarr) and not _is_integer_index(labels):
return self.obj.take(keyarr, axis=axis)
# this is not the most robust, but...
if isinstance(labels, MultiIndex) and not isinstance(keyarr[0], tuple):
level = 0
else:
level = None
return _reindex(keyarr, level=level)
|
def _getitem_iterable(self, key, axis=0):
labels = self.obj._get_axis(axis)
def _reindex(keys, level=None):
try:
return self.obj.reindex_axis(keys, axis=axis, level=level)
except AttributeError:
# Series
assert axis == 0
return self.obj.reindex(keys, level=level)
if com._is_bool_indexer(key):
key = _check_bool_indexer(labels, key)
return _reindex(labels[np.asarray(key)])
else:
if isinstance(key, Index):
# want Index objects to pass through untouched
keyarr = key
else:
# asarray can be unsafe, NumPy strings are weird
keyarr = _asarray_tuplesafe(key)
if _is_integer_dtype(keyarr) and not _is_integer_index(labels):
keyarr = labels.take(keyarr)
# this is not the most robust, but...
if isinstance(labels, MultiIndex) and not isinstance(keyarr[0], tuple):
level = 0
else:
level = None
return _reindex(keyarr, level=level)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _convert_to_indexer(self, obj, axis=0):
"""
Convert indexing key into something we can use to do actual fancy
indexing on an ndarray
Examples
ix[:5] -> slice(0, 5)
ix[[1,2,3]] -> [1,2,3]
ix[['foo', 'bar', 'baz']] -> [i, j, k] (indices of foo, bar, baz)
Going by Zen of Python?
"In the face of ambiguity, refuse the temptation to guess."
raise AmbiguousIndexError with integer labels?
- No, prefer label-based indexing
"""
labels = self.obj._get_axis(axis)
is_int_index = _is_integer_index(labels)
if com.is_integer(obj) and not is_int_index:
return obj
try:
return labels.get_loc(obj)
except (KeyError, TypeError):
pass
if isinstance(obj, slice):
ltype = labels.inferred_type
if ltype == "floating":
int_slice = _is_int_slice(obj)
else:
# floats that are within tolerance of int used
int_slice = _is_index_slice(obj)
null_slice = obj.start is None and obj.stop is None
# could have integers in the first level of the MultiIndex
position_slice = (
int_slice and not ltype == "integer" and not isinstance(labels, MultiIndex)
)
start, stop = obj.start, obj.stop
# last ditch effort: if we are mixed and have integers
try:
if "mixed" in ltype and int_slice:
if start is not None:
i = labels.get_loc(start)
if stop is not None:
j = labels.get_loc(stop)
position_slice = False
except KeyError:
if ltype == "mixed-integer":
raise
if null_slice or position_slice:
slicer = obj
else:
try:
i, j = labels.slice_locs(start, stop)
slicer = slice(i, j, obj.step)
except Exception:
if _is_index_slice(obj):
if labels.inferred_type == "integer":
raise
slicer = obj
else:
raise
return slicer
elif _is_list_like(obj):
if com._is_bool_indexer(obj):
objarr = _check_bool_indexer(labels, obj)
return objarr
else:
objarr = _asarray_tuplesafe(obj)
# If have integer labels, defer to label-based indexing
if _is_integer_dtype(objarr) and not is_int_index:
return objarr
# this is not the most robust, but...
if isinstance(labels, MultiIndex) and not isinstance(objarr[0], tuple):
level = 0
_, indexer = labels.reindex(objarr, level=level)
check = labels.levels[0].get_indexer(objarr)
else:
level = None
indexer = check = labels.get_indexer(objarr)
mask = check == -1
if mask.any():
raise KeyError("%s not in index" % objarr[mask])
return indexer
else:
return labels.get_loc(obj)
|
def _convert_to_indexer(self, obj, axis=0):
"""
Convert indexing key into something we can use to do actual fancy
indexing on an ndarray
Examples
ix[:5] -> slice(0, 5)
ix[[1,2,3]] -> [1,2,3]
ix[['foo', 'bar', 'baz']] -> [i, j, k] (indices of foo, bar, baz)
Going by Zen of Python?
"In the face of ambiguity, refuse the temptation to guess."
raise AmbiguousIndexError with integer labels?
- No, prefer label-based indexing
"""
labels = self.obj._get_axis(axis)
is_int_index = _is_integer_index(labels)
if com.is_integer(obj) and not is_int_index:
return obj
try:
return labels.get_loc(obj)
except (KeyError, TypeError):
pass
if isinstance(obj, slice):
int_slice = _is_index_slice(obj)
null_slice = obj.start is None and obj.stop is None
# could have integers in the first level of the MultiIndex
position_slice = (
int_slice
and not labels.inferred_type == "integer"
and not isinstance(labels, MultiIndex)
)
start, stop = obj.start, obj.stop
# last ditch effort: if we are mixed and have integers
try:
if "mixed" in labels.inferred_type and int_slice:
if start is not None:
i = labels.get_loc(start)
if stop is not None:
j = labels.get_loc(stop)
position_slice = False
except KeyError:
if labels.inferred_type == "mixed-integer":
raise
if null_slice or position_slice:
slicer = obj
else:
try:
i, j = labels.slice_locs(start, stop)
slicer = slice(i, j, obj.step)
except Exception:
if _is_index_slice(obj):
if labels.inferred_type == "integer":
raise
slicer = obj
else:
raise
return slicer
elif _is_list_like(obj):
if com._is_bool_indexer(obj):
objarr = _check_bool_indexer(labels, obj)
return objarr
else:
objarr = _asarray_tuplesafe(obj)
# If have integer labels, defer to label-based indexing
if _is_integer_dtype(objarr) and not is_int_index:
return objarr
# this is not the most robust, but...
if isinstance(labels, MultiIndex) and not isinstance(objarr[0], tuple):
level = 0
_, indexer = labels.reindex(objarr, level=level)
check = labels.levels[0].get_indexer(objarr)
else:
level = None
indexer = check = labels.get_indexer(objarr)
mask = check == -1
if mask.any():
raise KeyError("%s not in index" % objarr[mask])
return indexer
else:
return labels.get_loc(obj)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _get_slice_axis(self, slice_obj, axis=0):
obj = self.obj
axis_name = obj._get_axis_name(axis)
labels = getattr(obj, axis_name)
int_slice = _is_index_slice(slice_obj)
start = slice_obj.start
stop = slice_obj.stop
# in case of providing all floats, use label-based indexing
float_slice = labels.inferred_type == "floating" and _is_float_slice(slice_obj)
null_slice = slice_obj.start is None and slice_obj.stop is None
# could have integers in the first level of the MultiIndex, in which
# case we wouldn't want to do position-based slicing
position_slice = (
int_slice
and labels.inferred_type != "integer"
and not isinstance(labels, MultiIndex)
and not float_slice
)
# last ditch effort: if we are mixed and have integers
try:
if "mixed" in labels.inferred_type and int_slice:
if start is not None:
i = labels.get_loc(start)
if stop is not None:
j = labels.get_loc(stop)
position_slice = False
except KeyError:
if labels.inferred_type == "mixed-integer":
raise
if null_slice or position_slice:
slicer = slice_obj
else:
try:
i, j = labels.slice_locs(start, stop)
slicer = slice(i, j, slice_obj.step)
except Exception:
if _is_index_slice(slice_obj):
if labels.inferred_type == "integer":
raise
slicer = slice_obj
else:
raise
if not _need_slice(slice_obj):
return obj
return self._slice(slicer, axis=axis)
|
def _get_slice_axis(self, slice_obj, axis=0):
obj = self.obj
axis_name = obj._get_axis_name(axis)
labels = getattr(obj, axis_name)
int_slice = _is_index_slice(slice_obj)
start = slice_obj.start
stop = slice_obj.stop
# in case of providing all floats, use label-based indexing
float_slice = (
labels.inferred_type == "floating"
and (type(start) == float or start is None)
and (type(stop) == float or stop is None)
)
null_slice = slice_obj.start is None and slice_obj.stop is None
# could have integers in the first level of the MultiIndex, in which
# case we wouldn't want to do position-based slicing
position_slice = (
int_slice
and labels.inferred_type != "integer"
and not isinstance(labels, MultiIndex)
and not float_slice
)
# last ditch effort: if we are mixed and have integers
try:
if "mixed" in labels.inferred_type and int_slice:
if start is not None:
i = labels.get_loc(start)
if stop is not None:
j = labels.get_loc(stop)
position_slice = False
except KeyError:
if labels.inferred_type == "mixed-integer":
raise
if null_slice or position_slice:
slicer = slice_obj
else:
try:
i, j = labels.slice_locs(start, stop)
slicer = slice(i, j, slice_obj.step)
except Exception:
if _is_index_slice(slice_obj):
if labels.inferred_type == "integer":
raise
slicer = slice_obj
else:
raise
if not _need_slice(slice_obj):
return obj
return self._slice(slicer, axis=axis)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _is_integer_dtype(arr):
return (
issubclass(arr.dtype.type, np.integer) and not arr.dtype.type == np.datetime64
)
|
def _is_integer_dtype(arr):
return issubclass(arr.dtype.type, np.integer)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _is_list_like(obj):
# Consider namedtuples to be not list like as they are useful as indices
return (
np.iterable(obj)
and not isinstance(obj, basestring)
and not (isinstance(obj, tuple) and type(obj) is not tuple)
)
|
def _is_list_like(obj):
return np.iterable(obj) and not isinstance(obj, basestring)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _is_valid_index(x):
return com.is_float(x)
|
def _is_valid_index(x):
return (
com.is_integer(x)
or com.is_float(x)
and np.allclose(x, int(x), rtol=_eps, atol=0)
)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def ref_locs(self):
if self._ref_locs is None:
indexer = self.ref_items.get_indexer(self.items)
indexer = com._ensure_platform_int(indexer)
assert (indexer != -1).all()
self._ref_locs = indexer
return self._ref_locs
|
def ref_locs(self):
if self._ref_locs is None:
indexer = self.ref_items.get_indexer(self.items)
assert (indexer != -1).all()
self._ref_locs = indexer
return self._ref_locs
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def fillna(self, value, inplace=False):
new_values = self.values if inplace else self.values.copy()
mask = com.isnull(new_values)
np.putmask(new_values, mask, value)
if inplace:
return self
else:
return make_block(new_values, self.items, self.ref_items)
|
def fillna(self, value, inplace=False):
new_values = self.values if inplace else self.values.copy()
mask = com.isnull(new_values.ravel())
new_values.flat[mask] = value
if inplace:
return self
else:
return make_block(new_values, self.items, self.ref_items)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def interpolate(self, method="pad", axis=0, inplace=False, limit=None, missing=None):
values = self.values if inplace else self.values.copy()
if values.ndim != 2:
raise NotImplementedError
transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
if missing is None:
mask = None
else: # todo create faster fill func without masking
mask = _mask_missing(transf(values), missing)
if method == "pad":
com.pad_2d(transf(values), limit=limit, mask=mask)
else:
com.backfill_2d(transf(values), limit=limit, mask=mask)
return make_block(values, self.items, self.ref_items)
|
def interpolate(self, method="pad", axis=0, inplace=False):
values = self.values if inplace else self.values.copy()
if values.ndim != 2:
raise NotImplementedError
transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
if method == "pad":
_pad(transf(values))
else:
_backfill(transf(values))
return make_block(values, self.items, self.ref_items)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def should_store(self, value):
return not issubclass(
value.dtype.type, (np.integer, np.floating, np.complexfloating, np.bool_)
)
|
def should_store(self, value):
return not issubclass(value.dtype.type, (np.integer, np.floating, np.bool_))
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def make_block(values, items, ref_items, do_integrity_check=False):
dtype = values.dtype
vtype = dtype.type
if issubclass(vtype, np.floating):
klass = FloatBlock
elif issubclass(vtype, np.complexfloating):
klass = ComplexBlock
elif issubclass(vtype, np.datetime64):
klass = DatetimeBlock
elif issubclass(vtype, np.integer):
if vtype != np.int64:
values = values.astype("i8")
klass = IntBlock
elif dtype == np.bool_:
klass = BoolBlock
else:
klass = ObjectBlock
return klass(
values,
items,
ref_items,
ndim=values.ndim,
do_integrity_check=do_integrity_check,
)
|
def make_block(values, items, ref_items, do_integrity_check=False):
dtype = values.dtype
vtype = dtype.type
if issubclass(vtype, np.floating):
klass = FloatBlock
elif issubclass(vtype, np.integer):
if vtype != np.int64:
values = values.astype("i8")
klass = IntBlock
elif dtype == np.bool_:
klass = BoolBlock
else:
klass = ObjectBlock
return klass(
values,
items,
ref_items,
ndim=values.ndim,
do_integrity_check=do_integrity_check,
)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def __setstate__(self, state):
# discard anything after 3rd, support beta pickling format for a little
# while longer
ax_arrays, bvalues, bitems = state[:3]
self.axes = [_ensure_index(ax) for ax in ax_arrays]
self.axes = _handle_legacy_indexes(self.axes)
blocks = []
for values, items in zip(bvalues, bitems):
blk = make_block(values, items, self.axes[0], do_integrity_check=True)
blocks.append(blk)
self.blocks = blocks
|
def __setstate__(self, state):
# discard anything after 3rd, support beta pickling format for a little
# while longer
ax_arrays, bvalues, bitems = state[:3]
self.axes = [_ensure_index(ax) for ax in ax_arrays]
blocks = []
for values, items in zip(bvalues, bitems):
blk = make_block(values, items, self.axes[0], do_integrity_check=True)
blocks.append(blk)
self.blocks = blocks
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _verify_integrity(self):
_union_block_items(self.blocks)
mgr_shape = self.shape
for block in self.blocks:
assert block.ref_items is self.items
assert block.values.shape[1:] == mgr_shape[1:]
tot_items = sum(len(x.items) for x in self.blocks)
assert len(self.items) == tot_items
|
def _verify_integrity(self):
_union_block_items(self.blocks)
mgr_shape = self.shape
for block in self.blocks:
assert block.values.shape[1:] == mgr_shape[1:]
tot_items = sum(len(x.items) for x in self.blocks)
assert len(self.items) == tot_items
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def get_numeric_data(self, copy=False):
num_blocks = [
b
for b in self.blocks
if (
isinstance(b, (IntBlock, FloatBlock, ComplexBlock))
and not isinstance(b, DatetimeBlock)
)
]
indexer = np.sort(np.concatenate([b.ref_locs for b in num_blocks]))
new_items = self.items.take(indexer)
new_blocks = []
for b in num_blocks:
b = b.copy(deep=False)
b.ref_items = new_items
new_blocks.append(b)
new_axes = list(self.axes)
new_axes[0] = new_items
return BlockManager(new_blocks, new_axes, do_integrity_check=False)
|
def get_numeric_data(self, copy=False):
num_blocks = [b for b in self.blocks if isinstance(b, (IntBlock, FloatBlock))]
indexer = np.sort(np.concatenate([b.ref_locs for b in num_blocks]))
new_items = self.items.take(indexer)
new_blocks = []
for b in num_blocks:
b = b.copy(deep=False)
b.ref_items = new_items
new_blocks.append(b)
new_axes = list(self.axes)
new_axes[0] = new_items
return BlockManager(new_blocks, new_axes, do_integrity_check=False)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def from_blocks(cls, blocks, index):
# also checks for overlap
items = _union_block_items(blocks)
for blk in blocks:
blk.ref_items = items
return BlockManager(blocks, [items, index])
|
def from_blocks(cls, blocks, index):
# also checks for overlap
items = _union_block_items(blocks)
return BlockManager(blocks, [items, index])
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
for block in self.blocks:
indexer = items.get_indexer(block.items)
assert (indexer != -1).all()
result[indexer] = block.get_values(dtype)
itemmask[indexer] = 1
assert itemmask.all()
return result
|
def _interleave(self, items):
"""
Return ndarray from blocks with specified item order
Items must be contained in the blocks
"""
dtype = _interleaved_dtype(self.blocks)
items = _ensure_index(items)
result = np.empty(self.shape, dtype=dtype)
itemmask = np.zeros(len(items), dtype=bool)
# By construction, all of the item should be covered by one of the
# blocks
for block in self.blocks:
indexer = items.get_indexer(block.items)
assert (indexer != -1).all()
result[indexer] = block.values
itemmask[indexer] = 1
assert itemmask.all()
return result
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def reindex_items(self, new_items, copy=True, fill_value=np.nan):
""" """
new_items = _ensure_index(new_items)
data = self
if not data.is_consolidated():
data = data.consolidate()
return data.reindex_items(new_items)
# TODO: this part could be faster (!)
new_items, indexer = self.items.reindex(new_items)
# could have some pathological (MultiIndex) issues here
new_blocks = []
if indexer is None:
for blk in self.blocks:
if copy:
new_blocks.append(blk.reindex_items_from(new_items))
else:
blk.ref_items = new_items
new_blocks.append(blk)
else:
for block in self.blocks:
newb = block.reindex_items_from(new_items, copy=copy)
if len(newb.items) > 0:
new_blocks.append(newb)
mask = indexer == -1
if mask.any():
extra_items = new_items[mask]
na_block = self._make_na_block(
extra_items, new_items, fill_value=fill_value
)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return BlockManager(new_blocks, [new_items] + self.axes[1:])
|
def reindex_items(self, new_items, copy=True, fill_value=np.nan):
""" """
new_items = _ensure_index(new_items)
data = self
if not data.is_consolidated():
data = data.consolidate()
return data.reindex_items(new_items)
# TODO: this part could be faster (!)
new_items, indexer = self.items.reindex(new_items)
# could have some pathological (MultiIndex) issues here
new_blocks = []
if indexer is None:
for blk in self.blocks:
if copy:
new_blocks.append(blk.reindex_items_from(new_items))
else:
new_blocks.append(blk)
else:
for block in self.blocks:
newb = block.reindex_items_from(new_items, copy=copy)
if len(newb.items) > 0:
new_blocks.append(newb)
mask = indexer == -1
if mask.any():
extra_items = new_items[mask]
na_block = self._make_na_block(
extra_items, new_items, fill_value=fill_value
)
new_blocks.append(na_block)
new_blocks = _consolidate(new_blocks, new_items)
return BlockManager(new_blocks, [new_items] + self.axes[1:])
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def rename_axis(self, mapper, axis=1):
new_axis = Index([mapper(x) for x in self.axes[axis]])
assert new_axis.is_unique
new_axes = list(self.axes)
new_axes[axis] = new_axis
return BlockManager(self.blocks, new_axes)
|
def rename_axis(self, mapper, axis=1):
new_axis = Index([mapper(x) for x in self.axes[axis]])
new_axis._verify_integrity()
new_axes = list(self.axes)
new_axes[axis] = new_axis
return BlockManager(self.blocks, new_axes)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def rename_items(self, mapper, copydata=True):
new_items = Index([mapper(x) for x in self.items])
new_items.is_unique
new_blocks = []
for block in self.blocks:
newb = block.copy(deep=copydata)
newb.set_ref_items(new_items, maybe_rename=True)
new_blocks.append(newb)
new_axes = list(self.axes)
new_axes[0] = new_items
return BlockManager(new_blocks, new_axes)
|
def rename_items(self, mapper, copydata=True):
new_items = Index([mapper(x) for x in self.items])
new_items._verify_integrity()
new_blocks = []
for block in self.blocks:
newb = block.copy(deep=copydata)
newb.set_ref_items(new_items, maybe_rename=True)
new_blocks.append(newb)
new_axes = list(self.axes)
new_axes[0] = new_items
return BlockManager(new_blocks, new_axes)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def fillna(self, value, inplace=False):
new_blocks = [
b.fillna(value, inplace=inplace) if b._can_hold_na else b for b in self.blocks
]
if inplace:
return self
return BlockManager(new_blocks, self.axes)
|
def fillna(self, value, inplace=False):
""" """
new_blocks = [
b.fillna(value, inplace=inplace) if b._can_hold_na else b for b in self.blocks
]
if inplace:
return self
return BlockManager(new_blocks, self.axes)
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def form_blocks(data, axes):
# pre-filter out items if we passed it
items = axes[0]
if len(data) < len(items):
extra_items = items - Index(data.keys())
else:
extra_items = []
# put "leftover" items in float bucket, where else?
# generalize?
float_dict = {}
complex_dict = {}
int_dict = {}
bool_dict = {}
object_dict = {}
datetime_dict = {}
for k, v in data.iteritems():
if issubclass(v.dtype.type, np.floating):
float_dict[k] = v
elif issubclass(v.dtype.type, np.complexfloating):
complex_dict[k] = v
elif issubclass(v.dtype.type, np.datetime64):
datetime_dict[k] = v
elif issubclass(v.dtype.type, np.integer):
int_dict[k] = v
elif v.dtype == np.bool_:
bool_dict[k] = v
else:
object_dict[k] = v
blocks = []
if len(float_dict):
float_block = _simple_blockify(float_dict, items, np.float64)
blocks.append(float_block)
if len(complex_dict):
complex_block = _simple_blockify(complex_dict, items, np.complex128)
blocks.append(complex_block)
if len(int_dict):
int_block = _simple_blockify(int_dict, items, np.int64)
blocks.append(int_block)
if len(datetime_dict):
datetime_block = _simple_blockify(datetime_dict, items, np.dtype("M8[ns]"))
blocks.append(datetime_block)
if len(bool_dict):
bool_block = _simple_blockify(bool_dict, items, np.bool_)
blocks.append(bool_block)
if len(object_dict) > 0:
object_block = _simple_blockify(object_dict, items, np.object_)
blocks.append(object_block)
if len(extra_items):
shape = (len(extra_items),) + tuple(len(x) for x in axes[1:])
block_values = np.empty(shape, dtype=float)
block_values.fill(nan)
na_block = make_block(block_values, extra_items, items, do_integrity_check=True)
blocks.append(na_block)
blocks = _consolidate(blocks, items)
return blocks
|
def form_blocks(data, axes):
# pre-filter out items if we passed it
items = axes[0]
if len(data) < len(items):
extra_items = items - Index(data.keys())
else:
extra_items = []
# put "leftover" items in float bucket, where else?
# generalize?
float_dict = {}
int_dict = {}
bool_dict = {}
object_dict = {}
for k, v in data.iteritems():
if issubclass(v.dtype.type, np.floating):
float_dict[k] = v
elif issubclass(v.dtype.type, np.integer):
int_dict[k] = v
elif v.dtype == np.bool_:
bool_dict[k] = v
else:
object_dict[k] = v
blocks = []
if len(float_dict):
float_block = _simple_blockify(float_dict, items, np.float64)
blocks.append(float_block)
if len(int_dict):
int_block = _simple_blockify(int_dict, items, np.int64)
blocks.append(int_block)
if len(bool_dict):
bool_block = _simple_blockify(bool_dict, items, np.bool_)
blocks.append(bool_block)
if len(object_dict) > 0:
object_block = _simple_blockify(object_dict, items, np.object_)
blocks.append(object_block)
if len(extra_items):
shape = (len(extra_items),) + tuple(len(x) for x in axes[1:])
block_values = np.empty(shape, dtype=float)
block_values.fill(nan)
na_block = make_block(block_values, extra_items, items, do_integrity_check=True)
blocks.append(na_block)
blocks = _consolidate(blocks, items)
return blocks
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _stack_dict(dct, ref_items, dtype):
from pandas.core.series import Series
# fml
def _asarray_compat(x):
# asarray shouldn't be called on SparseSeries
if isinstance(x, Series):
return x.values
else:
return np.asarray(x)
def _shape_compat(x):
# sparseseries
if isinstance(x, Series):
return (len(x),)
else:
return x.shape
# index may box values
items = ref_items[[x in dct for x in ref_items]]
first = dct[items[0]]
shape = (len(dct),) + _shape_compat(first)
stacked = np.empty(shape, dtype=dtype)
for i, item in enumerate(items):
stacked[i] = _asarray_compat(dct[item])
# stacked = np.vstack([_asarray_compat(dct[k]) for k in items])
return items, stacked
|
def _stack_dict(dct, ref_items, dtype):
from pandas.core.series import Series
# fml
def _asarray_compat(x):
# asarray shouldn't be called on SparseSeries
if isinstance(x, Series):
return x.values
else:
return np.asarray(x)
def _shape_compat(x):
# sparseseries
if isinstance(x, Series):
return (len(x),)
else:
return x.shape
items = [x for x in ref_items if x in dct]
first = dct[items[0]]
shape = (len(dct),) + _shape_compat(first)
stacked = np.empty(shape, dtype=dtype)
for i, item in enumerate(items):
stacked[i] = _asarray_compat(dct[item])
# stacked = np.vstack([_asarray_compat(dct[k]) for k in items])
return items, stacked
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
def _interleaved_dtype(blocks):
from collections import defaultdict
counts = defaultdict(lambda: 0)
for x in blocks:
counts[type(x)] += 1
have_int = counts[IntBlock] > 0
have_bool = counts[BoolBlock] > 0
have_object = counts[ObjectBlock] > 0
have_float = counts[FloatBlock] > 0
have_complex = counts[ComplexBlock] > 0
have_dt64 = counts[DatetimeBlock] > 0
have_numeric = have_float or have_complex or have_int
if have_object or (have_bool and have_numeric) or (have_numeric and have_dt64):
return np.dtype(object)
elif have_bool:
return np.dtype(bool)
elif have_int and not have_float and not have_complex:
return np.dtype("i8")
elif have_dt64 and not have_float and not have_complex:
return np.dtype("M8[ns]")
elif have_complex:
return np.dtype("c16")
else:
return np.dtype("f8")
|
def _interleaved_dtype(blocks):
from collections import defaultdict
counts = defaultdict(lambda: 0)
for x in blocks:
counts[type(x)] += 1
have_int = counts[IntBlock] > 0
have_bool = counts[BoolBlock] > 0
have_object = counts[ObjectBlock] > 0
have_float = counts[FloatBlock] > 0
have_numeric = have_float or have_int
if have_object:
return np.object_
elif have_bool and have_numeric:
return np.object_
elif have_bool:
return np.bool_
elif have_int and not have_float:
return np.int64
else:
return np.float64
|
https://github.com/pandas-dev/pandas/issues/1328
|
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209 """
1210 return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211 freq=freq, tz=tz, normalize=normalize)
1212
1213
/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203 if data is None and offset is None:
--> 204 raise ValueError("Must provide freq argument if no data is "
205 "supplied")
206
ValueError: Must provide freq argument if no data is supplied
|
ValueError
|
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