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} | 5 | 2019-07-16T04:10:14Z | 2019-07-20T20:08:01Z | 2019-07-20T20:06:05Z | MEMBER | null | 2 goals here.
First is to end up with a single implementation for our arithmetic ops. By implementing the missing methods directly on DTA/PA, we make it so we don't need to special-case inside the `Index.__foo__` methods, so can dispatch to Series more directly.
Second is to get the wrapper defined in_arith_method_SERIES to the point where we can use it block-wise instead of column-wise, to address the recent perf issues. This makes progress towards that goal by tightening up the allowed types in masked_arith_op
Oh, and we also get rid of a usage of values_from_object, which is adjacent to #27165 and #27167 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27414 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27414/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27414/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27414/events | https://github.com/pandas-dev/pandas/issues/27414 | 468,524,860 | MDU6SXNzdWU0Njg1MjQ4NjA= | 27,414 | read_csv memory leak | {
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] | closed | false | null | [] | null | 3 | 2019-07-16T08:44:51Z | 2020-01-21T04:15:08Z | 2020-01-21T04:15:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import sys
import pandas as pd
_dir = r'D:\Python\data'
file_name = pd.read_csv(_dir + r'\dblist.csv', index_col=0)
file_name.loc[:, 'code'] = [x.upper() for x in file_name['code']]
file_name.loc[:, 'name'] = [x.upper() for x in file_name['name']]
file_name.loc[:, 'date'] = [str(x) for x in file_name['date']]
file_name11 = pd.read_csv(_dir + r'\dblist.csv', index_col=0)
file_name11.loc[:, 'code'] = [x.upper() for x in file_name11['code']]
file_name11.loc[:, 'name'] = [x.upper() for x in file_name11['name']]
file_name11.loc[:, 'date'] = [str(x) for x in file_name11['date']]
from copy import deepcopy
open_file1 = file_name.loc[file_name['name'] == 'IC1601', ['path', 'date']]
open_file11 = open_file1.copy()
for i2, fi in enumerate(open_file11['path']):
# fi0 = r'D:\Python\data\Full\2016\f_c201601dn\zc\20160104\IC1601_20160104.csv'
print('file_name0:', sys.getsizeof(file_name))
data = pd.read_csv(fi)# (fi = r'D:\Python\data\Full\2016\f_c201601dn\zc\20160104\IC1601_20160104.csv',type:str)
print('file_name1:', sys.getsizeof(file_name))
del open_file2
print('file_name2:', sys.getsizeof(file_name))
del open_file1
```
#### Problem description
I found a memory leak issue when I use pd.read_csv to read a csv file.
As the code shows,when I pd.read_csv(**fi**) the **file_name** add 104 byte memory everytimes.And the **file_name**'s shape is not change.The situation is not change when I write enumerate(open_file1).But when I use a static const str fi0,same content and type with fi,just pd.read_csv(**fi0**)memory is normal.I'm not sure it's a read_csv memory leak or not.My English is poor,I hope that I have already expressed my meaning.
output:
file_name0: 141473975
file_name1: 141474079
file_name2: 141474079
file_name0: 141474079
file_name1: 141474183
file_name2: 141474183
file_name0: 141474183
file_name1: 141474287
file_name2: 141474287
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 15 Stepping 11, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.3
pymysql: 0.9.3
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27415 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27415/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27415/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27415/events | https://github.com/pandas-dev/pandas/issues/27415 | 468,568,163 | MDU6SXNzdWU0Njg1NjgxNjM= | 27,415 | Using a `CategoricalIndex` with interval categories as column index breaks operations on a `DataFrame` | {
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} | 2 | 2019-07-16T10:13:16Z | 2019-07-23T22:06:16Z | 2019-07-23T22:06:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> a = pd.DataFrame([[1, 2]], columns=pd.CategoricalIndex(pd.interval_range(start=0.0, end=2.0)))
>>> a.T * 100
0
(0.0, 1.0] 100
(1.0, 2.0] 200
>>> a * 100
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-166-9b495e0b7c52> in <module>()
----> 1 a * 100
~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/ops.py in f(self, other, axis, level, fill_value)
2034
2035 assert np.ndim(other) == 0
-> 2036 return self._combine_const(other, op)
2037
2038 f.__name__ = op_name
...
TypeError: Cannot cast array data from dtype('float64') to dtype('<U32') according to the rule 'safe'
```
#### Problem description
When the columns index is a `CategoricalIndex` with interval categories, basic operations like multiplication by a scalar are broken.
The minimal example is a contrived case, but the situation can arise in common data analysis workflows:
1) Use `pd.cut` to bin floating point values
2) Perform a `pivot_table` operation with the binned values as columns
For instance:
```python
df = pd.DataFrame({'values': [10.0, 21.0, 32.0, 43.0],
'strings': ['A', 'B', 'C', 'D']})
df['intervals'] = pd.cut(df['values'], [0, 20, 40, 60])
p = df.pivot_table(index='strings', columns='intervals', values='values', aggfunc='sum')
```
A DataFrame with a column index of this kind is brokwn in other ways, for example it does not display as an HTML table in a Jupyter notebook, etc.
#### Expected Output
```
(0.0, 1.0] (1.0, 2.0]
0 100 200
```
#### Workaround
`df.columns = df.columns.categories` seems to make the DataFrame behave correctly.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.123-86.109.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.4
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.14.0
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 3.0.3
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.1.5
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 12 | 2019-07-16T12:25:23Z | 2019-07-16T19:19:30Z | 2019-07-16T19:19:30Z | CONTRIBUTOR | null | closes #27406 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27417 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27417/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27417/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27417/events | https://github.com/pandas-dev/pandas/issues/27417 | 468,703,014 | MDU6SXNzdWU0Njg3MDMwMTQ= | 27,417 | PERF: maybe_convert_objects rethink. | {
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] | closed | false | null | [] | null | 37 | 2019-07-16T14:54:35Z | 2019-07-27T14:24:27Z | 2019-07-27T14:24:26Z | CONTRIBUTOR | null | While exploring the `maybe_convert_objects` I've got quite a few proposals/questions but in the end I've come to a conclusion that the logic of function itself is far from optimal.
#### Motivation
1. Main argument here is we are not checking for inconsistency of parts of already seen data. E.g. if we have seen `datetime` and `float` we will still process the other part of array. And only after have seen all the elements we are going to checking of the `Seen()` object state in order to determine result dtype. But in given example one may already conclude that the result dtype must be left as `object`.
Other words there is a need to do result type checking on the fly if we want to preserve the main course of the written function.
2. Even if data is consistent we are still using redundant bunch of checks. E.g. if the dtype is `datetime` for all seen values we are still will try to check for `float`, `bool` etc. But all this checks may be left in single `if is_datetime(val)... else return object`.
I.e. for each seen dtype there should be it's own checking algorithm for other array elements.
These are only main points. So instead of trying to bloat current realisation with (maybe redundant) checks I am suggesting to rewrite `maybe_convert_objects`.
#### Realization
Main idea of following code is perform `type probing` on the first element of array and then pass processing to the corresponding function, during execution which processing may be passed to another (more 'narrow' with checks) function if new dtype has been discovered.
*NOTE* this is working copy so I left questions arisen in code comments (marked with `# QUEST:`)
```python
# First declare checking functions for corresponding dtype got in probing
# QUEST: What is the correct way to wrap these functions? Maybe another .pxd?
# QUEST: Can here and in other cdefs 'nogil' param be used?
cdef str check_complex(bint safe,
object[:] objects,
complex128_t[:] complexes,
int n = 0):
cdef:
float64_t fnan = np.nan
Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if util.is_complex_object(val) or util.is_float_object(val):
complexes[i] = val
elif val is None:
complexes[i] = fnan
elif util.is_integer_object(val):
if is_timedelta(val):
return 'object'
if not safe and oINT64_MIN <= val and val <= oUINT64_MAX:
complexes[i] = <complex128_t>val
else:
return 'object'
else:
return 'object'
return 'complex'
cdef str check_float(bint safe,
object[:] objects,
float64_t[:] floats,
complex128_t[:] complexes,
int n = 0):
cdef:
float64_t fnan = np.nan
Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if util.is_float_object(val):
floats[i] = complexes[i] = val
elif val is None:
floats[i] = complexes[i] = fnan
elif util.is_integer_object(val):
if util.is_timedelta64_object(val):
return 'object'
if not safe and oINT64_MIN <= val and val <= oUINT64_MAX:
floats[i] = <float64_t>val
complexes[i] = <complex128_t>val
else:
return 'object'
elif util.is_complex_object(val):
return check_complex(safe, objects, complexes, i)
else:
return 'object'
return 'float'
cdef str check_integer(bint safe,
object[:] objects,
int64_t[:] ints,
uint64_t[:] uints,
float64_t[:] floats,
complex128_t[:] complexes,
int n = 0):
cdef:
Seen seen = Seen()
Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if util.is_integer_object(val):
# is_integer hits timedelta64
# QUEST: maybe check timedelta64 in 'is_integer_object' directly?
# if so - remove from here and from check_float/complex
if util.is_timedelta64_object(val):
return 'object'
if not safe:
floats[i] = <float64_t>val
complexes[i] = <complex128_t>val
val = int(val)
seen.saw_int(val)
# QUEST: do val not in [in64.min, uint64.max] range should be
# dropped to object immediatelly?
if ((seen.uint_ and seen.sint_) or
val > oUINT64_MAX or val < oINT64_MIN):
return 'object'
if seen.uint_:
uints[i] = val
elif seen.sint_:
ints[i] = val
else:
uints[i] = val
ints[i] = val
elif safe:
return 'object'
elif util.is_float_object(val):
return check_float(safe, objects, floats, complexes, i)
elif val is None:
return check_float(safe, objects, floats, complexes, i)
elif util.is_complex_object(val):
return check_complex(safe, objects, complexes, i)
else:
return 'object'
if seen.uint_ and seen.sint_:
# Default for intersected sint uint data
# QUEST: should be 'sint'?
return 'sint'
elif seen.uint_:
return 'uint'
else:
return 'sint'
cdef str check_bool(object[:] objects,
uint8_t[:] bools,
int n = 0):
cdef Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if util.is_bool_object(val):
bools[i] = val
else:
return 'object'
return 'bool'
cdef str check_datetime(object[:] objects,
int64_t[:] idatetimes,
int n = 0):
cdef Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if is_datetime(val):
# QUEST: If one val has attr 'tzinfo' - must others have one?
# Or if other have not - 'object' should be returned direcrtly?
if getattr(val, 'tzinfo', None) is not None:
return 'datetime_tz'
idatetimes[i] = convert_to_tsobject(
val, None, None, 0, 0).value
elif val is NaT:
idatetimes[i] = NPY_NAT
else:
return 'object'
return 'datetime'
cdef str check_timedelta(object[:] objects,
int64_t[:] itimedeltas,
int n = 0):
cdef Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if is_timedelta(val):
itimedeltas[i] = convert_to_timedelta64(val, 'ns')
elif val is NaT:
itimedeltas[i] = NPY_NAT
else:
return 'object'
return 'timedelta'
cdef str check_NaT(object[:] objects,
int64_t[:] idatetimes,
int64_t[:] itimedeltas,
int n = 0):
cdef Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if val is NaT:
idatetimes[i] = itimedeltas[i] = NPY_NAT
elif is_datetime(val):
return check_datetime(objects, idatetimes, i)
elif is_timedelta(val):
return check_timedelta(objects, itimedeltas, i)
else:
return 'object'
# Default for arrays of NaT will be datetime64?
return 'datetime'
cdef str check_None(bint safe,
object[:] objects,
float64_t[:] floats,
complex128_t[:] complexes,
int n = 0):
cdef:
float64_t fnan = np.nan
Py_ssize_t i
for i in range(n, objects.shape[0]):
val = objects[i]
if val is None:
floats[i] = complexes[i] = fnan
elif util.is_float_object(val):
return check_float(safe, objects, floats, complexes, i)
elif util.is_complex_object(val):
return check_complex(safe, objects, complexes, i)
else:
return 'object'
# QUEST: array full of None will be left as object - correct?
# QUEST: at the same time [None, np.nan] converted to float.
# How about [None, NaT] then? or [None, bool]. Need consistency here.
# Same - [None, 1] is *float* now..
return 'object'
# QUEST: Disable directives for now caues unstable. Enable?
# @cython.boundscheck(False)
# @cython.wraparound(False)
def maybe_convert_objects(ndarray[object] objects, bint try_float=0,
bint safe=0, bint convert_datetime=0,
bint convert_timedelta=0):
"""
Type inference function-- convert object array to proper dtype
"""
# Declaring output templates
# QUEST: doesn't it use excessing memory for unnecessary types?
# so it should be initialized after dtype probing?
# QUEST: do this arrs really needed? Why refilling even one of them -
# maybe use objects.view or something as result after checkings?
cdef:
int n = objects.shape[0]
ndarray[uint8_t] bools = np.empty(n, dtype = np.uint8)
ndarray[int64_t] ints = np.empty(n, dtype = np.int64)
ndarray[uint64_t] uints = np.empty(n, dtype = np.uint64)
ndarray[float64_t] floats = np.empty(n, dtype = np.float64)
ndarray[complex128_t] complexes = np.empty(n, dtype = np.complex128)
if convert_datetime:
datetimes = np.empty(n, dtype='M8[ns]')
idatetimes = datetimes.view(np.int64)
if convert_timedelta:
timedeltas = np.empty(n, dtype='m8[ns]')
itimedeltas = timedeltas.view(np.int64)
# Ensure we are able do probing at all
if n == 0:
return objects
# Type probing - determine type of first element
# and call corresponding check
cdef:
str convert_dtype = 'object'
object val = objects[0]
if val is None:
# QUEST: Or should here 'object' be returned immediatelly?
# cause floats should use np.nan instead
convert_dtype = check_None(safe, objects, floats, complexes)
elif val is NaT:
if convert_datetime and convert_timedelta:
convert_dtype = check_NaT(objects, idatetimes, itimedeltas)
elif convert_datetime:
convert_dtype = check_datetime(objects, idatetimes)
elif convert_timedelta:
convert_dtype = check_timedelta(objects, itimedeltas)
elif is_timedelta(val):
if convert_timedelta:
convert_dtype = check_timedelta(objects, itimedeltas)
elif is_datetime(val):
if convert_datetime:
convert_dtype = check_datetime(objects, idatetimes)
elif util.is_bool_object(val):
convert_dtype = check_bool(objects, bools)
elif util.is_integer_object(val):
convert_dtype = check_integer(safe, objects,
ints, uints, floats, complexes)
elif util.is_float_object(val):
convert_dtype = check_float(safe, objects, floats, complexes)
elif util.is_complex_object(val):
convert_dtype = check_complex(safe, objects, complexes)
# QUEST: Why 'try_float' - not 'try_decimal' as e.g. try_timedelta?
elif try_float and not isinstance(val, str):
pass
# TODO: not done yet. It is not "safe" way to just try float(val)
# # this will convert Decimal objects
# try:
# convert_dtype = check_float(safe, objects, floats, complexes)
# except Exception:
# convert_dtype = 'object'
# OUTPUT
if convert_dtype == 'bool':
# QUEST: is view here needed? need to recheck this
return bools.view(np.bool_)
elif convert_dtype == 'sint':
return ints
elif convert_dtype == 'uint':
return uints
elif convert_dtype == 'float':
return floats
elif convert_dtype == 'complex':
return complexes
elif convert_dtype == 'datetime':
return datetimes
elif convert_dtype == 'timedelta':
return timedeltas
elif convert_dtype == 'datetime_tz':
# we try to coerce datetime w/tz but must all have the same tz
# QUEST: havn't check speed of this - can it be done faster?
# QUEST: DateTimeIndex is returned instead np.ndarray.
if is_datetime_with_singletz_array(objects):
from pandas import DatetimeIndex
return DatetimeIndex(objects)
return objects
```
#### Code logic tests
I've done initial testing with pytest in order to compare outputs of the _old_ and _new_ functions with this test suite:
```python
import pytest
import itertools
import numpy as np
import pandas as pd
import pandas._libs.lib as lib
import pandas.util.testing as tm
from decimal import Decimal
from datetime import datetime, timezone, timedelta
def data_gen(types):
N = 2
data = np.array([0]*N, dtype='object')
for perm in itertools.product(list(range(len(types))), repeat=N):
for i in range(data.shape[0]):
data[i] = types[perm[i]]
yield data.copy()
check_types = [None, np.nan, pd.NaT,
True,
np.iinfo(np.int64).min-1, -1, 1, np.iinfo(np.int64).max+1,
np.iinfo(np.uint64).max, np.iinfo(np.uint64).max+1,
1.1, np.float_(1.1),
1j, np.complex_(1j),
datetime(2000, 1, 1),
datetime(2000, 1, 1, 0, 0, 0, 0, timezone.utc),
np.datetime64('2000-01-01'),
timedelta(365),
np.timedelta64(1, 's'),
# Decimal(1.1),
'object'
]
datas = list(data_gen(check_types))
class Tests:
@pytest.mark.parametrize("data", datas)
@pytest.mark.parametrize("safe", [0, 1])
def test_maybe_convert_objects(self, data, safe):
old = lib.maybe_convert_objects_OLD(data, 1, safe, 1, 1)
new = lib.maybe_convert_objects(data, 1, safe, 1, 1)
print('data:', data, data.dtype,
'\nold:', old, old.dtype,
'\nnew:', new, new.dtype)
if isinstance(old, np.ndarray):
tm.assert_numpy_array_equal(old, new)
else:
tm.assert_index_equal(old, new)
```
_770/800_ tests passed
For now it's simple creates array with 2 values for all the combinations from `check_dtypes`. I'm in progress of solving left 30 and then will increase `N`.
#### Performance tests
Asv benchmarks:
```python
class MaybeConvertObjects:
dtypes = {"bool": True, "sint": -1, "uint": np.iinfo(np.uint64).max,
"float": 1.1, "complex": 1j,
"datetime": datetime(2000, 1, 1), "timedelta": timedelta(365),
"datetime_tz": datetime(2000, 1, 1, 0, 0, 0, 0, timezone.utc),
# "decimal": Decimal(1.1),
"object": "object"}
dtypes_to_insert = list(dtypes.values()) + [None, pd.NaT]
datas = {k: np.array([v] * 10**3, dtype='object')
for k, v in dtypes.items()}
params = [[0, 1], [0, 1], [0, 1],
list(dtypes.keys()),
list(itertools.product(list(range(len(dtypes_to_insert))),
repeat=2))]
param_names = ["safe", "convert_datetime", "convert_timedelta",
"dtype", "insrt_inds"]
def setup(self, safe, convert_datetime, convert_timedelta,
dtype, insrt_inds):
data = self.datas[dtype]
data[0] = self.dtypes_to_insert[insrt_inds[0]]
data[len(data) // 2] = self.dtypes_to_insert[insrt_inds[1]]
# data[2 * len(data) // 3] = self.dtypes_to_insert[insrt_inds[2]]
self.data = data
def time_maybe_convert_objects(self, safe, convert_datetime,
convert_timedelta, dtype, insrt_inds):
lib.maybe_convert_objects(self.data, try_float=0, safe=safe,
convert_datetime=convert_datetime,
convert_timedelta=convert_timedelta)
```
For _4100/4400_ of tests with changed performance the performance increased (what is the correct way to paste here the output of these? or is it redundant?). I'm also in progress of resolving left 300.
In order to continue working on this I need to know if this will be even considered or not. | {
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} | 4 | 2019-07-16T15:43:54Z | 2019-07-22T11:48:22Z | 2019-07-21T01:06:45Z | MEMBER | null | typing not added to all function signatures in io/formats/format.py in this pass to reduce the diff
~~this PR also contains a refactor to simplify the addition of type annotations; `truncate_h` and `tr_col_num` are dependant variables. `truncate_h` can be `True` or `False`. if `True`, `tr_col_num` is an `int` else if `truncate_h` is `False`, `tr_col_num` is `None`~~
~~same applies for `truncate_v` and `tr_row_num`~~
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https://api.github.com/repos/pandas-dev/pandas/issues/27419 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27419/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27419/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27419/events | https://github.com/pandas-dev/pandas/issues/27419 | 468,766,812 | MDU6SXNzdWU0Njg3NjY4MTI= | 27,419 | fillna is broken for Datetime columns [regression] | {
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} | 2 | 2019-07-16T17:05:26Z | 2019-07-17T11:54:25Z | 2019-07-17T11:54:25Z | CONTRIBUTOR | null | In 823af7639b94a39609c06a2228ba9e0f31155724 a call to `block._try_coerce_args()` was incorrectly replaced with call to `block._can_hold_element()` (I think under assumption that all block types have identical implementations of `_try_coerce_args` which is not true).
For Datetime blocks this leads to blowing up with `RecursionError: maximum recursion depth exceeded in comparison`.
Prior to the commit I indicated it worked fine, so I consider this a regression.
#### Small reproducer
```python
import pandas as pd
import numpy as np
from io import StringIO as sio
d1 = pd.read_csv(sio('2010,\n,\n2000\n'),header=None,parse_dates=[0])
f1 = np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]')
d1[0].fillna(f1)
```
#### Problem description
When the reproducer is launched, it's output is a long stacktrace caused by `RecursionError`.
#### Expected Output
The sample should not raise.
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-47-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0rc0+67.g2ed1f28cf
numpy : 1.16.2
pytz : 2018.4
dateutil : 2.6.0
pip : 10.0.1
setuptools : 39.0.1.post20180504
Cython : 0.29.6
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.9.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
#### Proposed solution
Revert the changes to `fillna()` function here: https://github.com/pandas-dev/pandas/commit/823af7639b94a39609c06a2228ba9e0f31155724?diff=unified#diff-8f51ce8697bdff680b3f71b196c582eeL388 | {
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} | 3 | 2019-07-16T17:46:28Z | 2020-10-20T02:08:29Z | 2020-10-20T02:08:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
a = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12]}
df = pd.DataFrame.from_records(a, index=['A', 'B'])
df.loc[(1,6)]
```
#### Problem description
When a non existent key `.loc` raises `TypeError`. Is this the intended behavior?
#### Expected Output
Maybe should raise `KeyError`
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-25-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.0.3
setuptools: 41.0.0
Cython: 0.29.12
numpy: 1.16.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: 1.5.6
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27421 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27421/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27421/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27421/events | https://github.com/pandas-dev/pandas/issues/27421 | 468,792,216 | MDU6SXNzdWU0Njg3OTIyMTY= | 27,421 | CI: Compatibility with pytest-azurepipelines | {
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] | closed | false | null | [] | null | 3 | 2019-07-16T18:08:04Z | 2020-10-09T18:26:47Z | 2020-10-09T18:26:47Z | CONTRIBUTOR | null | https://github.com/pandas-dev/pandas/pull/27416#issuecomment-511922995
Failing on the prerelease at https://pypi.org/project/pytest-azurepipelines/1.0.0a1/ | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27422 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27422/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27422/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27422/events | https://github.com/pandas-dev/pandas/issues/27422 | 468,829,384 | MDU6SXNzdWU0Njg4MjkzODQ= | 27,422 | BUG: to_datetime with multiple timezones does not coerce if invalid string in input | {
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```python
import pandas as pd
se = pd.Series(["2019-01-01 03:00:00-0500", "abc", "2019-01-01 03:00:00-0100"])
assert type(pd.to_datetime(se, errors="coerce").iloc[0]) == str
```
Note that the type of the first element (`"2019-01-01 03:00:00-0500"`) is `str` *after* the "conversion".
```python
import pandas as pd
se1 = pd.Series(["2019-01-01 03:00:00-0500", "abc", "2019-01-01 03:00:00-0100"])
pd.to_datetime(se1, errors="coerce")
# 0 2019-01-01 03:00:00-0500
# 1 abc
# 2 2019-01-01 03:00:00-0100
# dtype: object
se2 = pd.Series(["2019-01-01 03:00:00-0500", "2019-01-01 03:00:00-0100"])
pd.to_datetime(se2, errors="coerce")
# 0 2019-01-01 03:00:00-05:00
# 1 2019-01-01 03:00:00-01:00
# dtype: object
```
#### Problem description
If the input to `to_datetime` contains both dates with multiple timezones and invalid strings *no conversion* (the input strings will be returned) will happen when running with `errors="coerce"`. This is a problem as the (documented) expected output type is a date-like type. Note, that if the incorrect string is not present the dates will convert but the overall dtype will be object.
In previous versions (< 0.24) the function would convert the dates into timezone unaware timestamps which is a far more predictable behavior (the output can easily be converted into the desired timezone).
#### Expected Output
Output in 0.23
```python
import pandas as pd
se1 = pd.Series(["2019-01-01 03:00:00-0500", "abc", "2019-01-01 03:00:00-0100"])
pd.to_datetime(se1, errors="coerce")
# 0 2019-01-01 08:00:00
# 1 NaT
# 2 2019-01-01 04:00:00
# dtype: datetime64[ns]
se2 = pd.Series(["2019-01-01 03:00:00-0500", "2019-01-01 03:00:00-0100"])
pd.to_datetime(se2, errors="coerce")
# 0 2019-01-01 08:00:00
# 1 2019-01-01 04:00:00
# dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.7.3
pip: 19.1.1
setuptools: 39.1.0
Cython: 0.29.7
numpy: 1.16.4
scipy: 1.0.0
pyarrow: 0.13.0
xarray: None
IPython: 6.2.1
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 2.2.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.4
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.3.0
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-07-16T20:30:10Z | 2019-07-20T21:39:05Z | 2019-07-20T20:36:14Z | NONE | null | ```
pandas/_libs/tslibs/c_timestamp.c: In function ‘__pyx_f_6pandas_5_libs_6tslibs_11c_timestamp_10_Timestamp__get_start_end_field’:
pandas/_libs/tslibs/c_timestamp.c:7257:25: warning: ‘__pyx_t_10’ may be used uninitialized in this function [-Wmaybe-uninitialized]
7257 | __pyx_t_5numpy_int8_t __pyx_t_10[1];
|
```
Retrying fix dropped from https://github.com/pandas-dev/pandas/pull/27371
a review [comment](https://github.com/pandas-dev/pandas/pull/27371#discussion_r303179721) there disliked reintroducing `ndarray` here because the file had been converted to using memoryviews. `get_start_end_field` returns a view of type `npy_bool` so`out` should therefore be either a memoryview or an ndarray, and bool memoryviews will only be supported in cython 0.30
(see cython/cython/pull/2676).
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} | 9 | 2019-07-16T20:33:16Z | 2019-07-24T16:43:05Z | 2019-07-24T16:42:51Z | MEMBER | null | - [X] closes #27146
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This reveals a few new typing issues so won't pass CI but submitting as draft
Here's one way I think that works to avoid import cycles. Basically any internal imports in pandas._typing need to be set in a TYPE_CHECKING block to prevent their runtime evaluation from causing import cycles.
To illustrate the effectiveness like this if you put something like this at the end of pandas._typing:
```python
def func(obj: FrameOrSeries) -> None:
reveal_type(obj)
```
It would previously give you
```sh
pandas/_typing.py:43: note: Revealed type is 'Any'
```
Whereas now gives:
```sh
pandas/_typing.py:44: note: Revealed type is 'pandas.core.series.Series*'
pandas/_typing.py:44: note: Revealed type is 'pandas.core.frame.DataFrame*'
```
@topper-123 any way to check VSCode to see if this works with code completion?
In the long run this may not be needed with PEP 563:
https://www.python.org/dev/peps/pep-0563/
But I think this hack gets us there in the meantime | {
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} | 1 | 2019-07-16T20:45:28Z | 2019-07-17T14:05:18Z | 2019-07-17T11:54:25Z | MEMBER | null | - [x] closes #27419
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-07-16T21:58:23Z | 2020-01-16T00:35:03Z | 2019-07-17T11:52:36Z | MEMBER | null | - [x] closes #27252
reverts #25623
@heckeop
@gfyoung I know you asked for a FutureWarning to be raised but I didn't want to mess around with the validation_function in place here so just did a simple revert for sake of time / effort
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} | 3 | 2019-07-16T23:14:53Z | 2019-10-11T03:40:07Z | 2019-07-17T11:45:08Z | MEMBER | null | Figure the mypy.ini is small enough now don't need a dedicated config file for it | {
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} | 4 | 2019-07-17T00:21:26Z | 2019-07-24T00:23:23Z | 2019-07-23T23:55:40Z | MEMBER | null | Sits on top of #27411
xref #16674. The broader goal is de-special-casing code in internals.
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https://api.github.com/repos/pandas-dev/pandas/issues/27429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27429/events | https://github.com/pandas-dev/pandas/issues/27429 | 469,033,129 | MDU6SXNzdWU0NjkwMzMxMjk= | 27,429 | cannot join on column with datetime when using the 'on' parameter | {
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] | closed | false | null | [] | null | 3 | 2019-07-17T07:31:20Z | 2019-07-17T18:14:09Z | 2019-07-17T17:55:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
left = pd.DataFrame({'date':['2019-01-01'], 'a': [1]})
left['date'] = pd.to_datetime(left['date'], utc=True)
right = pd.DataFrame({'date':['2019-01-01'], 'b': [1]})
right['date'] = pd.to_datetime(right['date'], utc=True)
# execption
left.join(right, on='date')
```
#### Problem description
```
ValueError: You are trying to merge on datetime64[ns, UTC] and int64 columns. If you wish to proceed you should use pd.concat
```
Both column have the same dtype when calling join, somehow the right join colum gets converted to int64. This causes the join to fail.
However, the follwing code works fine:
``` python
# init as above
left = left.set_index('date')
right = right.set_index('date')
left.join(right)
"""
a b
date
2019-01-01 00:00:00+00:00 1 1
"""
```
#### Expected Output
```
date a b
2019-01-01 00:00:00+00:00 1 1
```
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27430 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27430/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27430/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27430/events | https://github.com/pandas-dev/pandas/issues/27430 | 469,121,889 | MDU6SXNzdWU0NjkxMjE4ODk= | 27,430 | validate ='m:m' in dataframe.merge should have a meaningful purpose | {
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"default": false,
"description": "Concat, Merge/Join, Stack/Unstack, Explode",
"id": 13098779,
"name": "Reshaping",
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"de... | closed | false | null | [] | null | 5 | 2019-07-17T10:47:50Z | 2021-07-10T18:21:30Z | 2021-07-10T18:21:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Currently, in https://github.com/pandas-dev/pandas/blob/master/pandas/core/reshape/merge.py
```python
elif validate in ["many_to_many", "m:m"]:
pass
```
#### Problem description
I was expecting m:m to give me a warning/error if either the left or right merge keys are unique.
Isn't that the purpose of specifying m:m? That is to ensure it is really a many to many merge.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
Expected an Error that keys are unique in either left/right/both and it is not a many-to-many merge when validate=m:m is specified.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.8.3 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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... | closed | false | null | [] | null | 1 | 2019-07-17T14:25:16Z | 2021-07-10T18:23:32Z | 2021-07-10T18:23:32Z | NONE | null | I am trying to develop general code to access specific columns in the order they are displayed by index. However, columns.levels resorts alphabetically automatically, and doesn't seem to have an option to have it... not do that. It would be useful if there was an option to have it remember the original order, to allow better referencing capabilities. | {
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} | 1 | 2019-07-17T15:03:44Z | 2019-07-18T13:17:37Z | 2019-07-18T13:17:37Z | MEMBER | null | - [X] xref #26747
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27433 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27433/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27433/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27433/events | https://github.com/pandas-dev/pandas/issues/27433 | 469,314,773 | MDU6SXNzdWU0NjkzMTQ3NzM= | 27,433 | df.describe.min(), max(), count() etc. produce results inconsistent with describe | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-17T16:16:25Z | 2019-07-17T16:22:33Z | 2019-07-17T16:22:33Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'data': []})
print (df.describe())
print ('Dataframe mean: {}'.format(df['data'].describe().mean()))
print ('Dataframe min: {}'.format(df['data'].describe().min()))
print ('Dataframe count: {}'.format(df['data'].describe().count()))
```
#### Problem description
This problem occurred when bringing in data from a file which was filled with NaN data values. It is easy to solve using df.empty first and then checking for min/max if df.empty is false. The unexpected problem was that while df.describe() method properly reported the NaN values in the file, the methods of min, mean, count, etc. applied to the describe method produced the wrong values ( i.e., instead of NaN, the result is 0 and instead of a count of 0, the result is 1).
#### Expected Output
<details>
```print ('Dataframe mean: {}'.format(df['data'].describe().mean()))```
Dataframe mean: NaN
```print ('Dataframe min: {}'.format(df['data'].describe().min()))```
Dataframe min: NaN
```print ('Dataframe count: {}'.format(df['data'].describe().count()))```
Dataframe count: 0
#### Output of ``pd.show_versions()``
</details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/27434 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27434/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27434/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27434/events | https://github.com/pandas-dev/pandas/issues/27434 | 469,336,683 | MDU6SXNzdWU0NjkzMzY2ODM= | 27,434 | Connecting to Kaggle for data. | {
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] | closed | false | null | [] | null | 2 | 2019-07-17T16:57:54Z | 2019-08-01T03:34:26Z | 2019-07-17T17:00:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
project_data = pd.read_csv('https://www.kaggle.com/hanselhansel/donorschoose#train_data.csv')
resource_data = pd.read_csv('https://www.kaggle.com/hanselhansel/donorschoose#Resources.csv')
```
#### Problem description
Anyone one knows if I am passing the right way.
---------------------------------------------------------------------------
ParserError Traceback (most recent call last)
<ipython-input-8-f36897a1d126> in <module>
----> 1 project_data = pd.read_csv('https://www.kaggle.com/devesh8052/donors-choode-dataset#train_data.csv')
2 resource_data = pd.read_csv('https://www.kaggle.com/devesh8052/donors-choode-dataset#Resources.csv')
3 #Limit the data for testing purpose since processing takes few hours for full set..
4
5 project_data = project_data.head(20000)
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)
676 skip_blank_lines=skip_blank_lines)
677
--> 678 return _read(filepath_or_buffer, kwds)
679
680 parser_f.__name__ = name
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
444
445 try:
--> 446 data = parser.read(nrows)
447 finally:
448 parser.close()
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in read(self, nrows)
1034 raise ValueError('skipfooter not supported for iteration')
1035
-> 1036 ret = self._engine.read(nrows)
1037
1038 # May alter columns / col_dict
/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py in read(self, nrows)
1846 def read(self, nrows=None):
1847 try:
-> 1848 data = self._reader.read(nrows)
1849 except StopIteration:
1850 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()
ParserError: Error tokenizing data. C error: Expected 1 fields in line 6, saw 2
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 32 | 2019-07-17T18:09:20Z | 2020-04-03T14:57:32Z | 2019-10-22T01:24:43Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```bash
RuntimeError: Python version >= 3.5 required.
```
#### Problem description
Under this docs: https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.24.0.html
0.24.x is supposed to be backward compatible to Python 2.
```
The 0.24.x series of releases will be the last to support Python 2. Future feature releases will support Python 3 only. See Plan for dropping Python 2.7 for more details
```
#### Expected Output
0.24.2 should still work on Python 2.
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} | 1 | 2019-07-17T18:17:01Z | 2019-07-24T13:20:10Z | 2019-07-24T12:07:14Z | MEMBER | null | - [x] closes #23925
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"color": "0e... | open | false | null | [] | null | 2 | 2019-07-17T18:24:13Z | 2021-09-20T18:27:19Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
data = pd.DataFrame(dict(values=range(20), quintiles=pd.cut(range(20), 5)))
grouped = data.groupby("quintiles")["values"]
samples = grouped.agg(lambda x: list(x.sample(3)))
samples
# quintiles
# (-0.019, 3.8] [3, 2, 1]
# (3.8, 7.6] [7, 4, 6]
# (7.6, 11.4] [8, 10, 11]
# (11.4, 15.2] [15, 14, 12]
# (15.2, 19.0] [17, 16, 19]
# Name: values, dtype: object
samples[0]
# [3, 2, 1]
samples[1]
# [3, 2, 1]
samples[2.3]
# [3, 2, 1]
samples[19]
# [17, 16, 19]
samples.keys()
# CategoricalIndex([(-0.019, 3.8], (3.8, 7.6], (7.6, 11.4], (11.4, 15.2],
# (15.2, 19.0]],
# categories=[(-0.019, 3.8], (3.8, 7.6], (7.6, 11.4], (11.4, 15.2], (15.2, 19.0]], ordered=True, name='quintiles', dtype='category')
```
#### Problem description
Indexing a series object that has pd.cut() intervals as its index results in membership test of the provided index on the intervals, which I found interesting, but it was very unexpected. Is this intentional and documented somewhere? When I give the labels names (labels=list("abcde") in pd.cut), this doesn't happen anymore.
#### Expected Output
I expectived it to fall back on .iloc[].
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.12.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 4.4.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.6
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.4.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 18 | 2019-07-17T18:47:51Z | 2019-07-25T19:56:41Z | 2019-07-24T12:05:48Z | CONTRIBUTOR | null | - [X] corresponding [discussion](https://github.com/pandas-dev/pandas/issues/27417#issuecomment-512014358) 27417
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
In order to stick to `maybe_convert_objects` realization it was needed to introduce `Seen().nat_` param. Since we want to distinguish following situations: were seen `NaT` or `datetime` or `timedelta`
NOTE: in current realization array full of `NaT` will be converted to `np.datetime64`. | {
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] | closed | false | null | [] | null | 4 | 2019-07-17T20:18:34Z | 2021-07-10T18:29:57Z | 2021-07-10T18:29:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Function
def _filter(self, func):
return self[list(map(func, self))]
# Set the attribute
setattr(pd.Index, "filter", _filter)
index = pd.Index(["iris_1", "notiris_1", "iris_2", "other_thing"])
# New way
func = lambda x:x.startswith("iris")
print("New way:", index.filter(func))
# Old way
print("Old way:", index[index.map(func)])
# New way: Index(['iris_1', 'iris_2'], dtype='object')
# Old way: Index(['iris_1', 'iris_2'], dtype='object')
# Set the attribute
setattr(pd.Series, "filter", _filter)
series = pd.Series(list("imagine_more_complex_stuff_here_instead"))
# New way
func = lambda x:x in ["o", "_"]
print("New way:", series.filter(func))
# Old way
print("Old way:", series[series.map(func)])
# New way: 7 _
# 9 o
# 12 _
# 14 o
# 20 _
# 26 _
# 31 _
# dtype: object
# Old way: 7 _
# 9 o
# 12 _
# 14 o
# 20 _
# 26 _
# 31 _
# dtype: object
```
#### Problem description
II always find myself writing really verbose code to filter my pandas indices or series. I love the map method and would like to extend this concept to include `filter`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27441 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27441/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27441/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27441/events | https://github.com/pandas-dev/pandas/issues/27441 | 469,484,956 | MDU6SXNzdWU0Njk0ODQ5NTY= | 27,441 | Add 'key' keyword argument to max/min methods, akin to python's builtin max/min | {
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Specifying a `key=` function F to use indicates you want to return the `val` with the largest `F(val)`, as opposed to returning the largest val or returning the largest F(val).
The following code should work:
```
>>> pd.DataFrame([1, 3, 5, 7]).max(key=lambda e: e % 3)
5
```
For the same reason that this works:
```
>>> max([1, 3, 5, 7], key=lambda e: e % 3)
5
```
Currently, it is necessary to use workarounds such as create temporary copies of the output of the function:
```
>>> df = pd.DataFrame([1, 3, 5, 7])
>>> k = df.apply(lambda e: e % 3).idxmax()
>>> df[0][k]
5
``` | {
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... | open | false | null | [] | null | 5 | 2019-07-17T23:13:12Z | 2021-07-10T18:30:33Z | null | NONE | null | Current behavior:
```
>>> df = pd.DataFrame({'a': [1, 1, 3], 'b': [9, 8, 7]})
>>> df.groupby('a')
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fd3d5a7b470>
```
Desired behavior (example, not exact):
```
>>> df = pd.DataFrame({'a': [1, 1, 3], 'b': [9, 8, 7]})
>>> df.groupby('a')
group a=1
a b
0 1 9
1 1 8
group a=3
a b
2 3 7
``` | {
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} | 2 | 2019-07-18T01:43:53Z | 2019-07-23T22:06:19Z | 2019-07-23T22:06:16Z | CONTRIBUTOR | null | - [x] closes #27415
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-07-18T02:46:26Z | 2019-07-20T23:52:50Z | 2019-07-20T23:49:48Z | MEMBER | null | Block.replace has code for dispatching to either maybe_convert_objects or soft_convert_objects, but the former is only reached from the tests. So rip that out and use soft_convert_objects.
Both of the functions have branches that are unreachable, so rip those out, along with adding types and validation. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27445 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27445/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27445/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27445/events | https://github.com/pandas-dev/pandas/pull/27445 | 469,545,970 | MDExOlB1bGxSZXF1ZXN0Mjk4NzM3MTcz | 27,445 | BUG: avoid incorrectly inheriting result | {
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} | 5 | 2019-07-18T02:51:43Z | 2019-07-26T17:21:17Z | 2019-07-26T17:21:05Z | MEMBER | null | This has caused problems in a number of local branches, but I don't have a good test case on master. Suggestions welcome. Still though, the failure mode is pretty clear, so this fixes it. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27446 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27446/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27446/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27446/events | https://github.com/pandas-dev/pandas/issues/27446 | 469,566,261 | MDU6SXNzdWU0Njk1NjYyNjE= | 27,446 | Is the internal twosigma refactor of window.pyx planned for merging in pandas? | {
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} | [] | closed | false | null | [] | null | 4 | 2019-07-18T04:35:00Z | 2019-07-19T03:43:07Z | 2019-07-18T19:57:57Z | NONE | null | I've been thinking of improvements to pandas rolling window functionality. Namely, allowing users to supply their own indexers and allowing numba JIT'd kernels to be passed to apply/transform.
I [floated](](https://github.com/pandas-dev/pandas/pull/27403#discussion_r304165649)) the idea of a window.pyx refactor, and was told that "others are working on it". I've seen no issue for this on pandas-dev/pandas, but there's apperently an effort going privately at Twosigma to do a major redesign of this part of pandas.
Of course they're free to develop whatever they want internally, but if this is meant for inclusion in pandas, why is the design not being discussed here? and if it's an internal effort only (perfectly fine), why should their efforts influence efforts by contributors such as myself?
I'd kindly to like to have this clarified before I waste time working on my own PR.
There seems to be an intention to push [some bits](https://github.com/twosigma/pandas/pull/3#pullrequestreview-258851617) to pandas, but not others ?
> jreback: I would do this versus pandas/master. Best to push whatever we can there
> in small uncontroversial bits.
See https://github.com/pandas-dev/pandas/pull/27305, and https://github.com/twosigma/pandas/issues/4
cc @jreback , @mroeschke
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} | 1 | 2019-07-18T06:39:11Z | 2019-07-18T10:53:56Z | 2019-07-18T10:53:29Z | CONTRIBUTOR | null | Fixes a performance regression introduced in #26403 very shortly before `0.25.0rc0` was cut, which can be seen [here](https://qwhelan.github.io/pandas/#indexing.CategoricalIndexIndexing.time_getitem_slice?machine=T470&num_cpu=4&os=Linux%205.0.0-20-generic&ram=20305904)
When comparing `CategoricalDtype`s with `ordered=False` (the default), a hash is currently done that is relatively slow even for a small number of categories. If we check if the categories happen to be the same and in the same order, we see a significant speedup in the equal case. The `.equals()` function pre-exits, so overhead in the non-equal case should be minimal.
`asv` results:
```
before after ratio
[a4c19e7a] [6810ff2f]
<unsorted_cats~1> <unsorted_cats>
- 179±0.4μs 25.6±0.4μs 0.14 indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_decr')
- 179±2μs 25.2±1μs 0.14 indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_incr')
- 180±0.4μs 25.1±0.3μs 0.14 indexing.CategoricalIndexIndexing.time_getitem_slice('non_monotonic')
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27449 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27449/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27449/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27449/events | https://github.com/pandas-dev/pandas/issues/27449 | 469,661,011 | MDU6SXNzdWU0Njk2NjEwMTE= | 27,449 | Usage of .loc with output of drop_dupilcates() returned DatetimeIndex | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-18T09:21:13Z | 2019-07-18T09:26:36Z | 2019-07-18T09:26:11Z | NONE | null | Basically I want to get rid of duplicated DatetimeIndex items. I do not understand why this does not work using the output of "drop_duplicates()".
I am using pandas 0.24.2
This shows that drop_duplicates() in the loc statement does not work although it does work with the boolean approach.
```python
import pandas as pd
df1 = pd.DataFrame(index=pd.date_range("2018-01-01", periods=3))
df2 = pd.DataFrame(index=pd.date_range("2018-01-03", periods=3))
df3 = df1.append(df2)
df3 = df3.loc[df3.index.drop_duplicates(), :]
print(df3)
# Empty DataFrame
# Columns: []
# Index: [2018-01-01 00:00:00, 2018-01-02 00:00:00, 2018-01-03 00:00:00, 2018-01-03 00:00:00, 2018-01-04 00:00:00, 2018-01-05 00:00:00]
print(df3.loc[~df3.index.duplicated(), :])
# Empty DataFrame
# Columns: []
# Index: [2018-01-01 00:00:00, 2018-01-02 00:00:00, 2018-01-03 00:00:00, 2018-01-04 00:00:00, 2018-01-05 00:00:00]
print(df3.index.drop_duplicates())
# DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
# '2018-01-05'],
# dtype='datetime64[ns]', freq=None)
```
drop_duplicates() returns the correct indexes, but does not work as expected in .loc.
So, first thing I thought is that I can not use an index to select rows and that I instead need to always use boolean lists/arrays/…. Then, I checked types and tried to use an index range, which works, surprisingly:
```python
print(type(df3.index.drop_duplicates()))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
```
```python
print(type(df3.index[2:4]))
# <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
```
```python
print(df3.loc[df3.index[1:3],:])
# Empty DataFrame
# Columns: []
# Index: [2018-01-02 00:00:00, 2018-01-03 00:00:00, 2018-01-03 00:00:00]
```
So, I can use .loc to get certain indexes, but although the type is the same, I can not use the output of drop_duplicates(), which is not what I expected.
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.4.1
pip: 19.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"color": "5319e7",
"default": false,
"description": "SAS: read_sas",
"id": 258745163,
"name": "IO SAS",
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"description"... | closed | false | null | [] | null | 12 | 2019-07-18T10:15:02Z | 2019-07-23T18:16:20Z | 2019-07-23T16:48:34Z | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.read_sas('test.sas7bdat', format='sas7bdat', encoding='utf-8')
```
#### Problem description
I am trying to read a `.sas7bdat` file using `read_sas` and I am having a hard time because the module is converting strings values that look like a number into `float`.
For example, if I have a telephone number like `'348386789'` and I read it with the code I posted the output obtained is `348386789.0`.
I noticed the same problem in the `read_csv` function but there you can use the argument `dtype` to manually set the type for each column (es. dtype={'number': str)}).
#### Expected Output
`'348386789'`
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.0
Cython: None
numpy: 1.16.3
scipy: None
pyarrow: 0.13.0
xarray: None
IPython: 7.4.0
sphinx: 2.1.2
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.9
feather: 0.4.0
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.4
bs4: None
html5lib: None
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 2 | 2019-07-18T10:55:17Z | 2019-11-08T01:03:24Z | 2019-11-08T01:03:24Z | NONE | null | Minor/trivial bug:
https://github.com/pandas-dev/pandas/blob/master/pandas/core/generic.py#L4960
Shouldn't `sample()` check that `frac` is between 0 and 1 somewhere?
Doc improvement suggestion:
Could anyone confirm that `replace=True|False` means allowing|disallowing sampling the same row more than once?
Methinks the docs could use an update to make what `replace` does clearer for newcomers - or at least for those who aren't familiar with `numpy`. | {
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} | 2 | 2019-07-18T11:05:24Z | 2019-07-18T11:15:32Z | 2019-07-18T11:15:08Z | NONE | null | Have a look here:
https://stackoverflow.com/q/57092373/9024698 | {
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} | 4 | 2019-07-18T11:12:41Z | 2020-03-26T12:45:38Z | 2020-03-26T12:45:38Z | NONE | null | Base by [SO question](https://stackoverflow.com/q/57092373/2901002):
Merge with right join not preserve order of columns:
columns = ['A', 'B', 'C']
data_1 = [[2, 5, 3, 5], [8, 2, 4, 1], [6, 5, 9, 1]]
data_1 = np.array(data_1).T
df_1 = pd.DataFrame(data=data_1, columns=columns)
print (df_1)
A B C
0 2 8 6
1 5 2 5
2 3 4 9
3 5 1 1
columns = ['A', 'B', 'C']
data_2 = [[2, 5, 3, 5], [7, 1, 3, 0], [np.nan, np.nan, np.nan, np.nan]]
data_2 = np.array(data_2).T
df_2 = pd.DataFrame(data=data_2, columns=columns)
print (df_2)
A B C
0 2.0 7.0 NaN
1 5.0 1.0 NaN
2 3.0 3.0 NaN
3 5.0 0.0 NaN
#right join NOT preserve order
df1 = df_1.merge(df_2[['A', 'B']], on=['A', 'B'], how='right')
print (df1)
A B C
0 5 1 1.0
1 2 7 NaN
2 3 3 NaN
3 5 0 NaN
Correct order for right join:
A B C
0 2 7 NaN
1 5 1 1
2 3 3 NaN
3 5 0 NaN
#left join wpreserve order correct
df2 = df_1.merge(df_2[['A', 'B']], on=['A', 'B'], how='left')
print (df2)
A B C
0 2 8 6
1 5 2 5
2 3 4 9
3 5 1 1
----
print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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] | closed | false | null | [] | null | 1 | 2019-07-18T12:21:11Z | 2019-07-18T16:55:53Z | 2019-07-18T16:55:48Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
str1='05-01-2019 00:37'
date1=pd.to_datetime(str1).date()
print(date1)
# output 2019-05-01
str2='15-01-2019 00:37'
date2=pd.to_datetime(str2).date()
print(date2)
# output 2019-01-15
```
#### Problem description
pd.to_datetime( ) is giving inconsistant out put, in the first piece of code where 01 is month and 05 is date after conversion these values have interchanged 01 as date and 05 as month.
but for similar code where the string value has 15 as date and 01 as month after conversion remained the same as expected.
I have tried .astype(‘datetime64’) and this also yielded the same wrong values as above code
Is this a known Issue and please provide workaround
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} | 1 | 2019-07-18T15:07:13Z | 2019-07-18T15:56:41Z | 2019-07-18T15:56:36Z | MEMBER | null | @TomAugspurger | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 13 | 2019-07-18T16:33:22Z | 2020-04-27T00:16:45Z | null | NONE | null | Scenario 1: single-level indexing, which works fine:
import pandas as pd # pandas version 0.25.0, python version: 3.6.6
idx = pd.IntervalIndex.from_arrays([1,3,1,2],
[3,4,2,4])
df = pd.DataFrame({'Value':[1,2,3,4]},index=idx)
which returns:
df =
Value
(1,3] 1
(3,4] 2
(1,2] 3
(2,4] 4
query results:
df.loc[1.5] =
Value
(1,3] 1
(1,2] 3
Scenario 2: Multi-level indexing:
idx1 = pd.MultiIndex.from_arrays([
pd.Index(['label1','label1','label2','label2']),
pd.IntervalIndex.from_arrays([1,3,1,2],
[3,4,2,4])
])
idx2 = pd.MultiIndex.from_arrays([
pd.Index(['label1','label1','label2','label2']),
pd.IntervalIndex.from_arrays([1,2,1,2],
[2,4,2,4])
])
df1 = pd.DataFrame({'Value':[1,2,3,4]},index=idx1) #with overlapping intervals
df2 = pd.DataFrame({'Value':[1,2,3,4]},index=idx2) #without overlapping intervals
which returns:
df1 =
Value
label1 (1,3] 1
label1 (3,4] 2
label2 (1,2] 3
label2 (2,4] 4
df2 =
Value
label1 (1,2] 1
label1 (2,4] 2
label2 (1,2] 3
label2 (2,4] 4
query method 1: works fine on both df1 and df2 but is slow
df1.Value.loc['label1'].loc[1.5]
1
query method 2: works only with df2, doesn't work with df1, is 10 times faster than query method 1
df2.Value.loc[('label1',1.5)]
1
df1.Value.loc[('label1',1.5)]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2889 try:
-> 2890 return self._engine.get_loc(key)
2891 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 1.5
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-12-ef83c1160165> in <module>()
11 display(df)
12 print(df.loc['label1'].loc[1.5])
---> 13 print(df.loc[('label1',1.5)])
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1402 except (KeyError, IndexError, AttributeError):
1403 pass
-> 1404 return self._getitem_tuple(key)
1405 else:
1406 # we by definition only have the 0th axis
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in _getitem_tuple(self, tup)
789 def _getitem_tuple(self, tup):
790 try:
--> 791 return self._getitem_lowerdim(tup)
792 except IndexingError:
793 pass
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in _getitem_lowerdim(self, tup)
945 return section
946 # This is an elided recursive call to iloc/loc/etc'
--> 947 return getattr(section, self.name)[new_key]
948
949 raise IndexingError("not applicable")
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1402 except (KeyError, IndexError, AttributeError):
1403 pass
-> 1404 return self._getitem_tuple(key)
1405 else:
1406 # we by definition only have the 0th axis
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in _getitem_tuple(self, tup)
789 def _getitem_tuple(self, tup):
790 try:
--> 791 return self._getitem_lowerdim(tup)
792 except IndexingError:
793 pass
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in _getitem_lowerdim(self, tup)
913 for i, key in enumerate(tup):
914 if is_label_like(key) or isinstance(key, tuple):
--> 915 section = self._getitem_axis(key, axis=i)
916
917 # we have yielded a scalar ?
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
1823 # fall thru to straight lookup
1824 self._validate_key(key, axis)
-> 1825 return self._get_label(key, axis=axis)
1826
1827
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexing.py in _get_label(self, label, axis)
155 raise IndexingError("no slices here, handle elsewhere")
156
--> 157 return self.obj._xs(label, axis=axis)
158
159 def _get_loc(self, key: int, axis: int):
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\generic.py in xs(self, key, axis, level, drop_level)
3728
3729 if axis == 1:
-> 3730 return self[key]
3731
3732 self._consolidate_inplace()
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2973 if self.columns.nlevels > 1:
2974 return self._getitem_multilevel(key)
-> 2975 indexer = self.columns.get_loc(key)
2976 if is_integer(indexer):
2977 indexer = [indexer]
C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2890 return self._engine.get_loc(key)
2891 except KeyError:
-> 2892 return self._engine.get_loc(self._maybe_cast_indexer(key))
2893 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2894 if indexer.ndim > 1 or indexer.size > 1:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 1.5
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https://api.github.com/repos/pandas-dev/pandas/issues/27457 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27457/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27457/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27457/events | https://github.com/pandas-dev/pandas/issues/27457 | 469,906,466 | MDU6SXNzdWU0Njk5MDY0NjY= | 27,457 | missing.cpython-35m-x86_64 | {
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} | [] | closed | false | null | [] | null | 5 | 2019-07-18T17:47:32Z | 2019-07-23T17:16:33Z | 2019-07-22T17:46:56Z | NONE | null | Every once in a while, one of my scripts are failing and initially I thought it was due to a memory leak somewhere. After reading the error carefully, it looks like it's from a missing dependency:
```
7fecaeb6d000-7fecaeb7f000 r-xp 00000000 08:04 3021138 /home/lib/python3.5/site-packages/pandas/_libs/missing.cpython-35m-x86_64-linux-gnu.so
7fecaeb7f000-7fecaed7f000 ---p 00012000 08:04 3021138 /home/lib/python3.5/site-packages/pandas/_libs/missing.cpython-35m-x86_64-linux-gnu.so
7fecaed7f000-7fecaed81000 rw-p 00012000 08:04 3021138 /home/lib/python3.5/site-packages/pandas/_libs/missing.cpython-35m-x86_64Aborted
```
In the library directory for pandas, in _libs, there is a file titled: missing.cpython-35m-x86_64-linux-gnu.so
I can just guess this is some low level dependency that probably didn't get installed when pandas was initially installed.
I updated pandas to ensure it's on the latest version, I believe 0.24?
At this point should I just uninstall & reinstall pandas? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27458 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27458/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27458/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27458/events | https://github.com/pandas-dev/pandas/issues/27458 | 469,935,227 | MDU6SXNzdWU0Njk5MzUyMjc= | 27,458 | Feature Request: Binomial Standard Deviation? | {
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} | [] | closed | false | null | [] | null | 4 | 2019-07-18T18:56:07Z | 2019-07-19T00:06:51Z | 2019-07-18T23:28:41Z | NONE | null | #### Problem description
We often work with large quantities of simulated data, and it would be convenient if we could get binomial standard deviation with a `.binom_std()` method (or with an optional argument to `.std()`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27459 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27459/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27459/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27459/events | https://github.com/pandas-dev/pandas/pull/27459 | 469,962,150 | MDExOlB1bGxSZXF1ZXN0Mjk5MDY5MjU3 | 27,459 | Fix #27451 (doc + validation) | {
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"description": "Incorrect or improved... | closed | false | null | [] | null | 6 | 2019-07-18T19:59:28Z | 2019-09-05T17:50:27Z | 2019-09-05T17:50:27Z | NONE | null | - [X ] closes #27451
- [ X] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27460 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27460/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27460/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27460/events | https://github.com/pandas-dev/pandas/issues/27460 | 470,041,588 | MDU6SXNzdWU0NzAwNDE1ODg= | 27,460 | REF: share code between pd.array and Series constructor | {
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"de... | open | false | null | [] | null | 0 | 2019-07-18T23:25:25Z | 2019-07-23T19:38:12Z | null | MEMBER | null | I've noticed a couple of places in the code where we do runtime imports of Series just to do `values = Series(values)._values`. It would be nice to avoid that.
This probably also means moving parts of internals.construction to somewhere like `arrays.array_`. In particular I think this would require moving sanitize_array and _try_cast. Since sanitize_array is imported by other modules, this would be nice to have non-internals anyway.
Finally there are some functions in dtype.cast that might make more sense alongside sanitize_array, e.g. construct_1d_object_array_from_listlike | {
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} | 4 | 2019-07-19T00:57:27Z | 2019-07-22T14:08:08Z | 2019-07-22T11:52:32Z | MEMBER | null | Edits to lib.is_scalar are to put slower python-space checks later so they may get short-circuited.
Everything else should be self-explanatory. | {
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> i think we need. way for EA to hook into this for an EA scalar
> eg an IPaddress from cyberpandas could register a scalar i think
Before we move on this, I think we need to clarify in which situations we care about `lib.is_scalar(x)` vs the simpler `np.ndim(x) == 0` | {
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} | 4 | 2019-07-19T01:50:26Z | 2019-07-20T20:26:12Z | 2019-07-20T19:20:06Z | CONTRIBUTOR | null | Removed ``.. versionadded:: 0.18.0`` and related text blocks. | {
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} | 1 | 2019-07-19T03:19:10Z | 2019-08-20T17:45:06Z | 2019-08-20T17:45:06Z | CONTRIBUTOR | null | I think that `.loc[0, 'A']` should be inf.
```python
In [36]: a = pd.DataFrame({"A": [0, 1, 2], 'B': [1.1, None, 1.1]})
In [37]: b = pd.DataFrame({"A": [1.1, 1.2, 1.3]}, index=[0, 2, 3])
In [38]: a.rfloordiv(b, fill_value=1)
Out[38]:
A B
0 NaN 0.0
1 1.0 NaN
2 0.0 0.0
3 1.0 NaN
```
to match
```python
In [49]: a.rfloordiv(1.1, fill_value=1)
Out[49]:
A B
0 inf 1.0
1 1.0 1.0
2 0.0 1.0
```
cc @jbrockmendel | {
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} | 17 | 2019-07-19T04:13:50Z | 2019-09-11T15:36:20Z | 2019-09-11T02:02:07Z | MEMBER | null | As a follow-up I'll take a look at either removing the now-empty .pyx file or trying to port the c file to pyx | {
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} | 6 | 2019-07-19T04:29:29Z | 2020-04-05T17:36:18Z | 2019-07-20T19:29:22Z | MEMBER | null | @jreback can you confirm my hunch that the `result.ndim == 2` block in groupby.ops was for Panel?
Tracing back the blame, row_bool_subset was introduced [here](https://github.com/pandas-dev/pandas/commit/b15ae85e1f647821755d35bedd6143ad1e9c3678) in 2012 to close #152 and it started being called only for result.ndim == 2 [here](https://github.com/pandas-dev/pandas/commit/9bfdc3c037e926143e708463d76fcdae3fb709f2) | {
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} | 6 | 2019-07-19T04:31:40Z | 2019-07-25T23:45:09Z | 2019-07-25T22:09:45Z | NONE | null | - [x] whatsnew
Related #27389 (several issues there)
closes #27486 | {
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... | open | false | null | [] | null | 0 | 2019-07-19T05:53:17Z | 2019-07-22T21:13:33Z | null | NONE | null | Adding tests for #27467 surfaced some bugs:
```python
from pandas import *
df = DataFrame(dict(a=[0, 0, 0, 1, 1, 1], b=range(6)))
g = df.groupby(np.repeat([0,1], 3))
# ok
print(g.ngroup())
#fails
g.transform('ngroup')
AttributeError: ("'Series' object has no attribute 'ngroup'", 'occurred at index a')
``` | {
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... | open | false | null | [] | null | 0 | 2019-07-19T05:54:53Z | 2019-07-23T15:02:19Z | null | NONE | null | Adding tests for #27467 surfaced some bugs:
```python
from pandas import *
df = DataFrame(dict(a=[0, 0, 0, 1, 1, 1], b=range(6)))
g = df.groupby(np.repeat([0,1], 3))
# ok
print(g.size())
#fails
g.transform('size')
TypeError: Transform function invalid for data types
``` | {
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} | 2 | 2019-07-19T05:58:13Z | 2019-08-12T17:56:47Z | 2019-08-12T17:56:47Z | NONE | null | Adding tests for #27467 surfaced some bugs:
```python
from pandas import *
df = DataFrame(dict(a=[0, 0, 0, 1, 1, 1], b=range(6)))
g = df.groupby(np.repeat([0,1], 3))
# ok
g.quantile(50)
<segfault>
```
quantile accepts floats in `[0,1]` or arrays of floats. It appears not to validate the range though. | {
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I need a print table with several columns by default for automatic test purposes while the default number of columns changed from version v0.23. I was using a simple check
```python
if 'display.max_columns' in pandas.core.config._registered_options:
pandas.set_option('display.max_columns', 20)
```
but this was broken from v0.25 since the config is missing
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'pandas.core' has no attribute 'config'
```
I have checked the [release notes](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html) but I dd not find any info about moving this property...
### Code Sample
```python
.core.config._registered_options
```
### Expected Output
Extending the columns to be shown
### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-54-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.15.4
pytz : 2018.7
dateutil : 2.7.5
pip : 19.1.1
```
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```python
from pandas import *
df = DataFrame(dict(a=[0, 0, 0, 1, 1, 1], b=range(6)))
g = df.groupby(np.repeat([0,1], 3))
# ok
g.cumcount()
# fails
g.transform('cumcount')
AttributeError: ("'Series' object has no attribute 'cumcount'", 'occurred at index a')
```
`cumcount` is a cythonized function. | {
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- [ ] waiting for #27584 to be resolved (move `check_percentiles` out of `NDFrame`)
- [ ] waiting for #27526 to be resolved (#20405 cythonized `Groupby.quantile` with functionality regression)
- [ ] whatsnew entry
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case `data is None`. Better include an if-statement to make sure this
condition actually holds.
When this if-statement is not present, failure occurs at a later point
in the code when an empty index is used, because that later code expects data
to be array-like, but we convert to a scalar here.
====
NOTE: this is a "shoot from the hip" (completely untested) PR as it stands.
I hope it can contribute towards a solution nonetheless, but it needs at least
tests
- [x] closes #26469
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-07-19T13:30:37Z | 2019-08-12T21:16:39Z | 2019-07-26T20:58:46Z | CONTRIBUTOR | null | #### Problem description
New added feature read_spss() #26537 doesn´t still have any kind of documentation about it http://pandas-docs.github.io/pandas-docs-travis/user_guide/io.html | {
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We can ask things like
* What version of pandas are you using?
* Is pandas stable enough?
* What APIs are you using (e.g. which `read_` methods)?
* What features would you like (additional readers, etc.)?
I plan to put together a form sometime in the next couple weeks. If there are questions you'd like to see included, please post them here.
See https://github.com/dask/dask/issues/4748 and https://t.co/OGrIjTLC2G for inspiration. | {
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} | 33 | 2019-07-19T15:10:58Z | 2019-08-01T20:50:46Z | 2019-08-01T18:04:12Z | CONTRIBUTOR | null | This PR adds a roadmap document. This is useful when pursing funding; we can point to a list of known items that we'd like to do if we had the person time (and funding) to tackle them.
Let's have two discussions
1. Do we want this? Roadmaps tend to go stale. How can we keep this up to date?
2. If so, what items should go on it? I've mostly picked from https://docs.google.com/document/d/151ct8jcZWwh7XStptjbLsda6h2b3C0IuiH_hfZnUA58/edit#heading=h.qm48l6dargmd plus a couple pet peeves of mine.
cc @pandas-dev/pandas-core | {
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} | 9 | 2019-07-19T16:01:42Z | 2019-08-20T14:26:50Z | 2019-08-20T14:26:49Z | CONTRIBUTOR | null | Consider
```python
import matplotlib.units as munits
import pandas as pd
pd.plotting.deregister_matplotlib_converters()
print(pd.__version__)
for key in munits.registry:
print(key, '\t', munits.registry[key])
```
`deregister_matplotlib_converters()` should only remove `pandas` converters, and not any other converters in the MPL units registry, but in pandas 0.25 it removes some of the Matplotlib converters.
#### Problem description
For pandas 0.24 this prints:
```
0.24.2
<class 'decimal.Decimal'> <matplotlib.units.DecimalConverter object at 0x1135515f8>
<class 'datetime.datetime'> <matplotlib.dates.DateConverter object at 0x11ed79cc0>
<class 'datetime.date'> <matplotlib.dates.DateConverter object at 0x11ed79c88>
<class 'numpy.datetime64'> <matplotlib.dates.DateConverter object at 0x11ed79c50>
```
But for pandas 0.25 it gives
```
0.25.0
<class 'decimal.Decimal'> <matplotlib.units.DecimalConverter object at 0x1133cd588>
```
This means `pandas.plotting.deregister_matplotlib_converters` is removing some of the built in matplotlib converters from the units registry when it shouldn't be; this is causing some of our tests to break upstream (xref https://github.com/matplotlib/matplotlib/issues/14859)
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} | 10 | 2019-07-19T18:06:00Z | 2019-08-21T16:34:46Z | 2019-08-20T14:26:50Z | CONTRIBUTOR | null | Fixes https://github.com/pandas-dev/pandas/issues/27479. Converters should only be cached to be re-instated if they are original, and not pandas converters.
I'm struggling to write a test for this; I think it requires a clean state with no pandas imports, so one can check what was in the units registry before pandas was imported. I think this means a new test file with no pandas imports at the top, but maybe someone has a better idea? | {
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} | 1 | 2019-07-19T18:48:00Z | 2019-08-20T14:00:09Z | 2019-08-20T14:00:09Z | NONE | null | ```python
import pandas as pd
x = pd.Series([1,2,3,4])
xt = type(x)
pd.isnull(xt)
## Traceback (most recent call last):
## File "<input>", line 1, in <module>
## File "/home/bbassett/venv37/lib/python3.7/site-packages/pandas/core/dtypes/missing.py", line 122, in isna
## return _isna(obj)
## File "/home/bbassett/venv37/lib/python3.7/site-packages/pandas/core/dtypes/missing.py", line 145, in _isna_new
## return _isna_ndarraylike(obj)
## File "/home/bbassett/venv37/lib/python3.7/site-packages/pandas/core/dtypes/missing.py", line 225, in _isna_ndarraylike
## dtype = values.dtype
## AttributeError: 'property' object has no attribute 'dtype'
y = pd.DataFrame({"col": [1,2,3,4]})
yt = type(y)
pd.isnull(yt)
## Traceback (most recent call last):
## File "<input>", line 1, in <module>
## File "/home/bbassett/venv37/lib/python3.7/site-packages/pandas/core/dtypes/missing.py", line 122, in isna
## return _isna(obj)
## File "/home/bbassett/venv37/lib/python3.7/site-packages/pandas/core/dtypes/missing.py", line 147, in _isna_new
## return obj._constructor(obj._data.isna(func=isna))
## AttributeError: 'NoneType' object has no attribute 'isna'
```
#### Problem description
I'm using a list comprehension and `pd.isnull()` to remove empty items from a list (`[x for x in l if not pd.isnull(x)]`). When one of my users passes a pandas `type` object to this logic, it gums up the works by raising an AttributeError. This logic works as expected with `type` objects from Python's builtins:
```python
import pandas as pd
z = "This is a test."
zt = type(z)
pd.isnull(zt)
## False
```
#### Expected Output
Both calls to `pd.isnull()` above should return `False`. The type objects are not null/`None`/`NaN`/missing.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 4 | 2019-07-19T19:53:15Z | 2019-12-31T12:34:12Z | null | CONTRIBUTOR | null | I'd like to have a document that describes how we think people should write pandas code.
This introduces a bit of friction when documenting something, since you'll need to decide "does it go in best practices or the user guide?" But I think the idea of a "best practices" document with opinionated, short examples and prose, linking back to the user guide and API docs, is valuable.
I've started a notebook at https://mybinder.org/v2/gh/TomAugspurger/pandas-best-practices/master?filepath=Best%20Practices.ipynb
Are there any sections you would add / remove?
Would you structure it differently?
(tangentially, I'd like to explore how we can incorporate binder into our documentation). | {
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} | 1 | 2019-07-19T20:55:42Z | 2019-07-25T15:58:18Z | 2019-07-25T15:58:18Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from pandas import Series
print(Series([-1j]))
```
#### Problem description
This raises the following error:
```
Traceback (most recent call last):
File "foo.py", line 3, in <module>
print(Series([-1j]))
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/core/series.py", line 1611, in __repr__
length=show_dimensions,
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/core/series.py", line 1677, in to_string
result = formatter.to_string()
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 312, in to_string
fmt_values = self._get_formatted_values()
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 299, in _get_formatted_values
values_to_format, None, float_format=self.float_format, na_rep=self.na_rep
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1032, in format_array
return fmt_obj.get_result()
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1063, in get_result
fmt_values = self._format_strings()
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1288, in _format_strings
return list(self.get_result_as_array())
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1252, in get_result_as_array
formatted_values = format_values_with(float_format)
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1234, in format_values_with
return _trim_zeros_complex(values, na_rep)
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1597, in _trim_zeros_complex
return ["".join(separate_and_trim(x, na_rep)) for x in str_complexes]
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1597, in <listcomp>
return ["".join(separate_and_trim(x, na_rep)) for x in str_complexes]
File "/home/david/.pyenv/versions/3.7.4/lib/python3.7/site-packages/pandas/io/formats/format.py", line 1594, in separate_and_trim
+ ["j"]
IndexError: list index out of range
```
#### Expected Output
This should print something like the following:
```
0 0.0-1.0j
dtype: complex128
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-17134-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-07-19T22:40:38Z | 2019-09-05T17:10:54Z | 2019-09-05T17:10:54Z | MEMBER | null | Right now we don't benchmark performance of read_excel across the various engines (xlrd, openpyxl and odfpy) so would gladly accept contributions to improve benchmark coverage on those | {
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} | 3 | 2019-07-19T23:56:22Z | 2019-07-25T22:09:45Z | 2019-07-25T22:09:45Z | NONE | null | https://github.com/pandas-dev/pandas/pull/27467#discussion_r305439508
Examples:
- `g.transform('invalid_name')`
- `g.transform('agg')`, attribute exists but isn't appropriate agg/transformation.
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] | closed | false | null | [] | null | 2 | 2019-07-20T03:19:22Z | 2019-11-05T22:10:58Z | 2019-11-05T22:10:58Z | MEMBER | null | `CategoricalDtype`, `DatetimeTZDtype`, `PeriodDtype`, and `IntervalDtype` are defined in `core.dtypes.dtypes`. `_IntegerDtype`, `PandasDtype`, and `SparseDtype` are defined in `core.arrays.*`. We should be consistent about where these are defined.
My hobby horse right now is the dependency structure. `core.dtypes` is _almost_ independent of the rest of `core`, and the code would be appreciably easier to reason about if that separation were strict. To make that work we would need to either
a) move SparseDType etc into `core.dtypes.dtypes` and deal with the handful of imports that the Dtype classes require
b) move CategoricalDType etc over to the `core.array` modules and find a way to avoid the need for these DType classes in `dtypes.common` functions.
a) sounds significantly easier to me. | {
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} | 15 | 2019-07-20T03:36:53Z | 2019-07-25T20:21:50Z | 2019-07-25T17:18:13Z | CONTRIBUTOR | null | Libraries, including pandas, register backends via entrypoints.
xref #26747
cc @datapythonista @jakevdp @philippjfr | {
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} | 2 | 2019-07-20T09:39:05Z | 2019-07-20T17:00:50Z | 2019-07-20T16:41:02Z | CONTRIBUTOR | null | I've been backfilling `asv` data and noticed the following regression in `IntervalIndexMethod.time_intersection_both_duplicate` (see [here](https://qwhelan.github.io/pandas/#index_object.IntervalIndexMethod.time_intersection_both_duplicate?machine=T470-W10&os=Linux%204.4.0-17134-Microsoft&ram=20822880&p-param1=100000&commits=83eb2428-cdc07fde)):
This regression was missed as the benchmark was added in #26711, which was after introduction in #26225.
This PR both simplifies the `IntervalIndex._intersection_non_unique` logic (now equivalent to `MultiIndex._intersection_non_unique`) and provides a `~50x` speedup:
```
before after ratio
[9bab81e0] [2848036e]
<interval_non_unique_intersection~1> <interval_non_unique_intersection>
- 12.6±0.1ms 725±30μs 0.06 index_object.IntervalIndexMethod.time_intersection_both_duplicate(1000)
- 4.96±0s 96.7±6ms 0.02 index_object.IntervalIndexMethod.time_intersection_both_duplicate(100000)
```
The new numbers are about `10x` faster than the old baseline.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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... | open | false | null | [] | null | 2 | 2019-07-20T15:05:50Z | 2021-03-09T20:46:01Z | null | CONTRIBUTOR | null | Timestamps always get returned in nanoseconds regardless of the unit I specify:
```python
import numpy as np, pandas as pd
np.datetime64("2018-01-01").astype("datetime64[s]").astype(int)
>>> 1514764800
pd.Series(np.datetime64("2018-01-01")).astype("datetime64[s]")
```
```
0 2018-01-01
dtype: datetime64[ns]
```
```python
pd.Series(np.datetime64("2018-01-01")).astype("datetime64[s]").astype(int)
```
```
0 1514764800000000000
dtype: int64
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} | 26 | 2019-07-20T17:02:30Z | 2020-01-30T12:34:23Z | 2020-01-30T12:17:42Z | CONTRIBUTOR | null | discussion for 1.0.
We are focusing all deprecation removals and we should try to release by September 2019. | {
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#27456 has been updated. closing as duplicate. | {
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- [X] closes #27329
- [ ] tests added / passed
- [X] whatsnew entry
- [ ] double check performance impact and update docs accordingly if needed. | {
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} | 1 | 2019-07-20T23:54:04Z | 2019-07-23T22:04:28Z | 2019-07-23T22:04:24Z | CONTRIBUTOR | null | The current logic for `MultiIndex.is_monotonic` relies on `np.lexsort()` on `MultiIndex._values`. While the result is cached, this is slow as it triggers the creation of `._values`, needs to perform an `O(n log(n))` sort, as well as populate the hashmap of a transient `Index`.
This PR significantly speeds up this check by directly operating on `.codes` when `.levels` are individually sorted. This means we can leverage `libalgos.is_lexsorted()` which is `O(n)` (but has the downside of needing `int64` when `MultiIndex` compacts levels).
```
before after ratio
[5bd57f90] [3320dded]
- 31.9±0.9ms 627±50μs 0.02 index_cached_properties.IndexCache.time_is_monotonic_decreasing('MultiIndex')
- 29.6±0.7ms 528±80μs 0.02 index_cached_properties.IndexCache.time_is_monotonic('MultiIndex')
- 29.9±1ms 524±90μs 0.02 index_cached_properties.IndexCache.time_is_monotonic_increasing('MultiIndex')
```
- [ ] closes #27099
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"description": "Timezone data d... | closed | false | null | [] | null | 2 | 2019-07-21T00:18:14Z | 2019-07-22T15:27:40Z | 2019-07-22T15:27:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Similar issue as #24198 in pandas 0.25.0
```python
import pandas as pd
df = pd.DataFrame({'time': [pd.Timestamp('2010-07-15 03:14:45'),
pd.Timestamp('2010-11-19 18:47:06')],
'timezone': ['Etc/GMT+4', 'US/Eastern']})
df['time_tz'] = (df.groupby(['timezone'])['time']
.transform(lambda x: x.dt.tz_localize(x.name, ambiguous='NaT')))
df['time_tz_desired'] = (df.groupby(['timezone'])['time']
.apply(lambda x: x.dt.tz_localize(x.name, ambiguous='NaT')))
print(df)
```
#### Problem description
The timezone in the transform should be preserved but it is not.
For the apply the datetime returned is an object type
`df.info()` shows
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 4 columns):
time 2 non-null datetime64[ns]
timezone 2 non-null object
time_tz 2 non-null datetime64[ns]
time_tz_desired 2 non-null object
dtypes: datetime64[ns](2), object(2)
memory usage: 192.0+ bytes
```
#### Expected Output
Should preserve timezone
```python
df.groupby(['timezone'])['time'].transform(lambda x: x.dt.tz_localize(x.name, ambiguous='NaT'))
```
And should return datetime
```python
df.groupby(['timezone'])['time'].apply(lambda x: x.dt.tz_localize(x.name, ambiguous='NaT'))
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 40.8.0
Cython : 0.29.10
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.3
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | [] | closed | false | null | [] | null | 6 | 2019-07-21T01:13:35Z | 2019-11-02T21:56:03Z | 2019-11-02T21:56:03Z | NONE | null | I have a dataframe with `DatetimeIndex`. I note the following difference of behavior in `0.24.2` against `0.25.0`.
For equivalent Timestamp indices, `0.25.0` returns an empty Series, while `0.24.2` returns one value.
```python
print(start)
Timestamp('2019-06-24 16:56:11.200999936')
print(stop)
Timestamp('2019-06-24 16:56:11.200999936')
# pandas 0.24.2
df2.loc[start:stop, ["time"]]
time
time
2019-06-24 16:56:11.201 NaT
# pandas 0.25
df2.loc[start:stop, "time"]
Series([], Name: time, dtype: datetime64[ns])
```
I expect this is actually a good thing as before, indicated above, time indexes didn't respect upper-bound exclusivity and now they do.
### Question 1
I looked through release notes for `0.25.0` and didn't find anything mentioning this particular behavior change. Is this upper-bound exclusivity expected for DatetimeIndex frame slicing?
### Question 2
Sometimes I want to ensure a slice is upper-bound inclusive. To adapt, I'm attempting to add a nanosecond to the `stop` variable above.
```python
ipdb> df2.loc[start:stop + pd.Timedelta(nanoseconds=1), ["time"]]
Empty DataFrame
Columns: [time]
Index: []
```
But this returns an empty dataframe. It doesn't produce the expected behavior until I set `nanoseconds=70`.
Is this actually expected? What is the mechanism for producing a distinct Timestamp slice? | {
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} | 0 | 2019-07-21T02:09:24Z | 2020-10-27T12:51:13Z | 2020-10-27T12:51:13Z | CONTRIBUTOR | null | Follow up for #27495, where we handle the common case of `MultiIndex` being created from helper functions that ensure the levels are sorted. In order to handle the case of `MultiIndex(codes=..., labels=...)` and labels being individually unsorted, we likely need to re-encode that level. If that is performant, it would allow deleting a non-performant base case in `MultiIndex.is_monotonic` | {
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} | 1 | 2019-07-21T04:09:01Z | 2019-07-22T21:30:50Z | 2019-07-22T21:14:54Z | MEMBER | null | Preliminary to possibly finding new homes for some of these functions that don't really fit in core.dtypes (discussed briefly at the sprint). xref #25273 | {
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} | 2 | 2019-07-21T09:05:28Z | 2019-08-06T15:45:31Z | 2019-08-06T15:45:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
pd.crosstab([df.A,df.B],df.C,margins=True,margins_name='Sub-Total',normalize=0)
```
#### Problem description
pandas.Crosstab:
As per the Documentation, For parameter normalize If margins is True, will also normalize margin values. However when I give normalize as True and margins as True with margin_names as a string then it throws following exception:
KeyError: "['Sub-Total'] not found in axis"
where Sub-Total is the margins_name String.
#### Expected Output
Normalize value of the Margin rows and columns
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 3.2.1
pip: 18.0
setuptools: 39.1.0
Cython: 0.26.1
numpy: 1.15.4
scipy: 1.0.0
pyarrow: 0.8.0
xarray: 0.10.7
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
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https://api.github.com/repos/pandas-dev/pandas/issues/27501 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27501/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27501/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27501/events | https://github.com/pandas-dev/pandas/issues/27501 | 470,763,287 | MDU6SXNzdWU0NzA3NjMyODc= | 27,501 | pd.to_csv gives '__init__() got an unexpected keyword argument 'tupleize_cols'` | {
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] | closed | false | null | [] | null | 4 | 2019-07-21T09:50:24Z | 2019-08-21T15:53:02Z | 2019-07-21T16:15:02Z | NONE | null | Hi, I have an issue which might be related to this:
I'm running the following basic code:
<code>`dfMain.to_csv('./January_filtered_International_WE.csv')` </code>
which used to run normally until yesterday. Yesterday morning I upgraded to `pandas 0.25.0` while running code and now I cannot write a 500k rows (single-level indexed) dataframe to a csv. I can mention that I left Jupyter Notebook running in order to do some processing, so this morning when I opened it I had the dataFrame already, processed.
Versions (using Windows 10)
Jupyter Notebook : 5.7.8 (Edit: 6.0.0 - upgraded with Administrator permissions - errors still persist)
Python : 3.6.7
Pandas : 0.25.0
I would like to save my DataFrame in a fast and efficient manner as I will load it several times in the future. I do not want to close the notebook as this will delete the dataFrame.
I tried:
- downgrading to `Pandas 0.24.2` (previous version used) but still getting the `__init__() got an unexpected keyword argument 'tupleize_cols'`
- use `pd.to_pickle` but got a `memoryError`
- use `pd.to_hdf` but got a `memoryError`
- using msgbox instead but apparently it does not support DataFrames (got an error)
- upgrade Jupyter notebook, but got the following error:
`ERROR: ipython 5.8.0 has requirement prompt-toolkit<2.0.0,>=1.0.4, but
you'll have prompt-toolkit 2.0.9 which is incompatible`
- so naturally I did pip install prompt-toolkit 1.0.16 but then got this message:
_`ERROR: jupyter-console 6.0.0 has requirement prompt-toolkit<2.1.0,>=2.0.0, but you'll have prompt-toolkit 1.0.16 which is incompatible.`_
Edit:
With Admin privileges it managed to upgrade Jupyter.
As an alternative I went into PyCharm and took a random `DataFrame.to_csv` and it worked. This makes me think the issue is with Jupyter Notebook.
P.S.: Any help on how to save the DataFrame (~12 GB) is also appreciated! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27502 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27502/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27502/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27502/events | https://github.com/pandas-dev/pandas/issues/27502 | 470,781,354 | MDU6SXNzdWU0NzA3ODEzNTQ= | 27,502 | MultiIndex with Period level is really slow | {
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"default"... | open | false | null | [] | null | 2 | 2019-07-21T13:23:41Z | 2019-07-22T04:43:52Z | null | NONE | null | A MultiIndex with a Period level is multiple levels of magnitude slower to join than a same-sized single Period index, or the same sized MultiIndex with Dates instead of Periods.
In this benchmark, I construct a the multiindexes and single index referred to above by making a date/period range, and adding it to a single-value dummy column to make a trivial multiindex.
Benchmark code:
```python
import pandas as pd
from datetime import date
import numpy as np
from timeit import timeit
from numpy.random import random_integers, random_sample
periods = pd.period_range(date(2019,1,1), date(2020,1,1), freq='min')
dates = pd .date_range(date(2019,1,1), date(2020,1,1), freq='min')
people = [1]
singlePeriodIndex = periods
multiPeriodIndex = pd.MultiIndex.from_product([periods, people])
multiDateIndex = pd.MultiIndex.from_product([dates, people])
print(f"singlePeriodIndex size: {len(singlePeriodIndex)}")
print(f"multiPeriodIndex size: {len(multiPeriodIndex)}")
print(f"mnultiDateIndex size: {len(multiDateIndex)}")
indexSize = len(singlePeriodIndex)
Counts = random_integers(0, 100, size=indexSize)
TotalSales = random_sample(size=indexSize) * 1000
def join_two_frames(index: pd.Index):
data = pd.DataFrame(data={'Counts': Counts}, index=index)
data2 = pd.DataFrame(data={'TotalSales': TotalSales}, index=index)
return data.join(data2)
print(f" Time for 3 × single period index: {timeit(lambda: join_two_frames(singlePeriodIndex), number=3)}s")
print(f" Time for 3 × multi period index: {timeit(lambda: join_two_frames(multiPeriodIndex), number=3)}s")
print(f" Time for 3 × multi date index: {timeit(lambda: join_two_frames(multiDateIndex), number=3)}s")
```
```
# results from my PC:
singlePeriodIndex size: 525601
multiPeriodIndex size: 525601
mnultiDateIndex size: 525601
Time for 3 × single period index: 0.07404399762210523s
Time for 3 × multi period index: 203.1650731925208s
Time for 3 × multi date index: 0.20330959270341964s
```
This is on my Windows 10 PC, but I observed similar on an Ubuntu PC running the same versions of libraries as below (my env is set up by pipenv).
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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} | 2 | 2019-07-21T14:42:02Z | 2019-08-02T14:44:29Z | 2019-07-31T14:07:09Z | NONE | null | These disabled tests now pass on 3.7. Let's see what CI says. | {
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] | closed | false | null | [] | null | 4 | 2019-07-21T15:47:56Z | 2019-08-02T14:44:05Z | 2019-07-31T14:16:13Z | NONE | null | - [X] closes #27058, #21684
- [X] whatsnew entry
- [x] append and list of levels (like `DataFrame.set_index` has)
- [ ] tests added / passed
Help users method-chain all the things. This is modeled closely on the original support for Arrays added in 2011 by Wes Mckinney in
553bacc7abe341991e4d043a5c17521902c7f127 `ENH: can pass external arrays in lieu of column names to DataFrame.set_index`
This is also how `DataFrame.set_index` behaves (though poorly documented), modulo support for column keys which don't apply here.
related discussion #24046, and end of https://github.com/pandas-dev/pandas/issues/14829#issuecomment-404884912, https://github.com/pandas-dev/pandas/issues/12392 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27505 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27505/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27505/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27505/events | https://github.com/pandas-dev/pandas/pull/27505 | 470,796,856 | MDExOlB1bGxSZXF1ZXN0Mjk5NjQ2NjUw | 27,505 | Update issue template | {
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} | 0 | 2019-07-21T16:38:49Z | 2021-04-19T13:51:57Z | 2021-04-19T13:51:57Z | MEMBER | null | Unearthed as part of #27424
```python
>>> import pandas as pd
>>> from pandas.core.dtypes.common import ensure_int_or_float
>>> ensure_int_or_float(pd.Series(range(3), dtype='Int64'))
0 0
1 1
2 2
dtype: int64
# Directly against PandasArray now
>>> ensure_int_or_float(pd.Series(range(3), dtype='Int64').array)
array([0., 1., 2.]) # returns float
```
When the underlying array gets passed to ensure_int_or_float they don't return integer as expected. The issue here is due to the fact that the `astype` signature doesn't have `casting` as a parameter like other types going through `ensure_int_or_float` do | {
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} | 1 | 2019-07-21T18:18:53Z | 2019-07-22T12:08:28Z | 2019-07-22T12:07:10Z | CONTRIBUTOR | null | - [x] xref #27463
Somehow all the changes didn't get added to #27463, so here's a follow-up.
I've also removed a reference to 0.17 in style.ipynb which also makes the styler api non-provisional. | {
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... | open | false | null | [] | null | 1 | 2019-07-21T18:55:32Z | 2021-07-10T18:39:22Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame({'a':np.arange(5)},
index=pd.DatetimeIndex(['2019-07-01 12:00:00',
'2019-07-01 17:00:00',
'2019-07-02 03:00:00',
'2019-07-02 16:00:00',
'2019-07-04 13:00:00']))
# First plot - not nice x-axis labels
df1.plot()
df2 = df1.resample('1H').ffill()
# Second plot - lovely x-axis labels
df2.plot()
```
#### Problem description
The first plot created by the code above is:
The x-axis labels here aren't formatted as nicely as they could be.
However, when the data is resampled to a regular frequency and plotted, you get the plot below:
This has lovely x-axis label formatting, with multiple levels, and is very clear.
I've had a look at the code, and it seems that it is using a major formatter of `pd.plotting._converter.TimeSeries_DateFormatter`, but this seems to require data with a `freq` (ie. regularly spaced time series).
Is there any way to add this lovely formatting for non-regular time series data? Either as a default, or as something that can be done manually by the user.
#### Expected Output
The ideal expected output is the same x-axis labels for both plots.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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