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TOC on the left hand side is pinned during navigation and also expandable/collapsible
<img width="1089" alt="image" src="https://user-images.githubusercontent.com/609873/54065608-3cc81e00-41d8-11e9-830d-124d759bf791.png">
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} | 3 | 2019-03-09T10:58:49Z | 2019-03-09T16:32:46Z | 2019-03-09T16:32:44Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-03-09T12:06:49Z | 2019-03-12T20:46:22Z | 2019-03-12T20:45:24Z | NONE | null | #### Code Sample
```python
import pandas as pd
x = {'user_id':['100000715097692381911',
'100003840837471130074'],
'item_id': [1, 2]
}
dfx = pd.DataFrame(x)
dfx['user_id'].replace(
{
'100000715097692381911': 0,
'100003840837471130074': 1
}, inplace=True)
```
#### Problem description
I got overflow error:
File "/home/grain/ml/lib/python3.6/site-packages/pandas/core/dtypes/cast.py", line 683, in astype_nansafe
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
File "pandas/_libs/lib.pyx", line 546, in pandas._libs.lib.astype_intsafe
OverflowError: Python int too large to convert to C long
*This error occurs on my laptop with pandas 0.24.1, python 3.6.7. The example code can handle the replacement correctly on another computer with an older version pandas 0.20.3.*
#### Expected Output
Since I loaded big id numbers like '100000715097692381911' as string type, the pandas.DataFrame.replace() method should replace it with the corresponding value in the dictionary. But I got overflow error. It seemed pandas took the id string as integer?
And if I modify just the first element of 'user_id' column to 's100000715097692381911', then the code won't run into overflow.
#### 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: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.6.3
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
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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] | closed | false | null | [] | null | 4 | 2019-03-09T12:25:29Z | 2019-04-29T15:04:07Z | 2019-04-29T15:04:07Z | CONTRIBUTOR | null | - [ ] closes #25120
a solution for the problem of inherited docstrings not passing docstring tests preventing errors to be added to the CI `code_checks.sh`
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} | 3 | 2019-03-09T16:35:19Z | 2019-03-09T19:19:45Z | 2019-03-09T18:52:21Z | CONTRIBUTOR | null | - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Made parameter description of the `pandas.core.filter` docstring clearer. PEP8 fix in example, see also comments in #20148.
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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I did not use an oxford comma in the error description, which is how it was written in the issue's expected result. | {
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```python
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> error_dict = pd.read_csv("error_dict.txt") #github doesn't support attach csv
>>> dict(error_dict.set_index('FIELD').squeeze())
```
This outputs a malformed dict:
{'Q89': 'category',
'Q90': 'category',
'Q91': 'category',
'Q92': 'category',
'Q93': 'category',
'Q94': 'category',
'Q95': 'category',
'Q102': 'category',
'Q103': Q103 category
Q103 category
Name: TYPE, dtype: object,
'Q104': Q104 category
Q104 category
Name: TYPE, dtype: object,
'Q105': 'category',
'Q106': 'category',
'Q107': 'category',
'Q108': 'category',
'Q109': 'category',
'Q110': 'category',
'Q111': 'category',
'Q112': 'category',
'Q113': Q113 category
Q113 category
Name: TYPE, dtype: object,
'Q41': 'category',
'Q42': 'category',
'Q101': 'category',
'Q24': 'category',
'Q39': 'category',
'Q37': 'category',
'Q38': 'category'}
#### Expected Output
{'Q89': 'category',
'Q90': 'category',
'Q91': 'category',
'Q92': 'category',
'Q93': 'category',
'Q94': 'category',
'Q95': 'category',
'Q102': 'category',
'Q103': 'category',
'Q104': 'category',
'Q105': 'category',
'Q106': 'category',
'Q107': 'category',
'Q108': 'category',
'Q109': 'category',
'Q110': 'category',
'Q111': 'category',
'Q112': 'category',
'Q113': 'category',
'Q41': 'category',
'Q42': 'category',
'Q101': 'category',
'Q24': 'category',
'Q39': 'category',
'Q37': 'category',
'Q38': 'category'}
#### 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: 4.18.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: pt_BR.UTF-8
pandas: 0.24.1
pytest: None
pip: 9.0.1
setuptools: 39.0.1
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
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: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 0.999999999
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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Not complete just pushing for now. Main goal here is to get a CI run that works for any modules existing with comments. | {
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} | 3 | 2019-03-10T01:37:28Z | 2021-05-13T23:31:33Z | 2021-05-13T23:31:29Z | NONE | null | ```python
import pandas as pd
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.to_csv('test.csv', index=False)
# Should raise ValueError, since there is no col at index 10
pd.read_csv('test.csv', header=0, usecols=[0, 10], engine='python')
print('Silently ran to completion.')
```
<hr>
Output
```python
Silently ran to completion.
```
#### Problem description
Silently passing incorrect column indices (result of some arithmetic) could potentially lead to a debugging nightmare.
A possible solution is to check if the largest value in `usecols` is larger than the number of columns at any level.
```python
# Something along the lines of
if any(max(col_indices) >= len(column) for column in columns):
raise ValueError('...')
columns = [[n for i, n in enumerate(column) if i in col_indices]
for column in columns]
self._col_indices = col_indices
```
If the idea is to accommodate different levels having different number of columns, maybe it would be better if `usecols` allowed a sequence of sequences (one for each level).
**Note**:
* A similar problem exists with the C engine, although the result is an unexpected column full of _nulls_.
* The issue seems to be handled correctly when `index` is written to file.
**Edit**:
* The issue occurs when the length of `usecols` equals the number of columns in dataframe.
#### Expected Output
``ValueError``
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 3 | 2019-03-10T01:51:41Z | 2019-03-11T13:58:13Z | 2019-03-11T13:58:13Z | CONTRIBUTOR | null | - [x] closes #25048
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} | 5 | 2019-03-10T02:24:06Z | 2019-03-11T15:28:59Z | 2019-03-11T15:26:55Z | CONTRIBUTOR | null | - [x] closes #25311
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-03-10T09:16:00Z | 2019-03-10T21:16:30Z | 2019-03-10T21:16:30Z | NONE | null | #### Code demonstrating lost of compatibility between NaNs and NaTs from pandas 0.22.0 to 0.24.1
The introduction of an early control in the merge function based on the dtype, lost the compatibility between NaN and NaT offered in previous versions. This compatibility is described in the following pandas documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html.
In pandas version 0.22.0, the merge between datetime and NaN(s) is not raising an exception, as demonstrated in the following code:
```python
import pandas as pd
import numpy as np
print(pd.__version__)
# 0.22.0
df1 = pd.DataFrame({
'some_date': [pd.datetime(2019, 3, 1), pd.datetime(2019, 3, 2)],
'id': [1, 2]
})
df2 = pd.DataFrame({
'empty_date': [np.nan],
})
print(df1.dtypes)
# id int64
# some_date datetime64[ns]
# dtype: object
print(df2.dtypes)
# empty_date float64
# dtype: object
df_merged = pd.merge(df1, df2, left_on=['some_date'], right_on=['empty_date'])
print(df_merged)
# Empty DataFrame
# Columns: [id, some_date, empty_date]
print(df_merged.dtypes)
# id int64
# some_date object
# empty_date float64
df_merged = pd.merge(df1, df2, left_on=['some_date'], right_on=['empty_date'], how='left')
print(df_merged)
# id some_date empty_date
# 0 1 2019-03-01 00:00:00 NaN
# 1 2 2019-03-02 00:00:00 NaN
```
But in pandas version 0.24.1, the merge between datetime and nan(s) is raising the exception: _ValueError: You are trying to merge on datetime64[ns] and float64 columns. If you wish to proceed you should use pd.concat, as demonstrated next:_.
One way to overcome this problem is to convert the column from object to datetime using _pd.to_datetime_
```python
import pandas as pd
import numpy as np
print(pd.__version__)
# 0.24.1
df1 = pd.DataFrame({
'some_date': [pd.datetime(2019, 3, 1), pd.datetime(2019, 3, 2)],
'id': [1, 2]
})
df2 = pd.DataFrame({
'empty_date': [np.nan],
})
print(df1.dtypes)
# some_date datetime64[ns]
# id int64
print(df2.dtypes)
# empty_date float64
# dtype: object
df_merged = pd.merge(df1, df2, left_on=['some_date'], right_on=['empty_date'])
# ValueError: You are trying to merge on datetime64[ns] and float64 columns. If you wish to proceed you should use pd.concat
# You can avoid the previous exception by converting to datetime, as follows:
df2['empty_date'] = pd.to_datetime(df2.empty_date)
print(df2.dtypes)
# empty_date datetime64[ns]
df_merged = pd.merge(df1, df2, left_on=['some_date'], right_on=['empty_date'])
print(df_merged)
# Empty DataFrame
df_merged = pd.merge(df1, df2, left_on=['some_date'], right_on=['empty_date'], how='left')
print(df_merged)
# some_date id empty_date
# 0 2019-03-01 1 NaT
# 1 2019-03-02 2 NaT
```
#### Problem description
The typical case when you end up with NaNs in columns that you expect to be datetime is:
- If load an empty data set (empty csv file, empty database select statement, etc), the dtype of the datetimes in the dataframe are object.
- if you left join other dataframe to this empty dataset, you will end up with a new dataframe with datetime columns with NaN values and dtype objects
- Any further merge with that column will produce an exception.
To avoid this situation, in pandas 0.24.1., you have to convert any datetime column at the time of loading or before the merge. This is adding an extra unnecessary cost and produced a verbose code. Missing this conversion is prompt to generate future bugs if when empty datasets are loaded.
If this control cannot be reverted, then the description about compatibility between NaN and NaT should be clarified or deleted.
#### Expected Output
As described before, in the code section pandas version 0.22.0,
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_GB.UTF-8
pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
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: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 13 | 2019-03-10T14:33:05Z | 2019-06-28T12:28:09Z | 2019-06-28T12:13:22Z | CONTRIBUTOR | null | Picking up #22471 again. Needed to readd some fixtures that were removed by #24885.
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} | 4 | 2019-03-10T14:36:26Z | 2019-03-11T19:18:47Z | 2019-03-11T12:41:37Z | MEMBER | null | Closes #25439, alternative to https://github.com/pandas-dev/pandas/pull/25595 for 0.24.2 | {
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just started happening. I guess we are not running 1.12 on the 2.7 build, so need to skip this test.
```
================================== FAILURES ===================================
_______ TestDataFrameConstructors.test_constructor_maskedrecarray_dtype ________
[gw1] linux2 -- Python 2.7.6 /venv/bin/python
self = <pandas.tests.frame.test_constructors.TestDataFrameConstructors object at 0xd9592c2c>
def test_constructor_maskedrecarray_dtype(self):
# Ensure constructor honors dtype
data = np.ma.array(
np.ma.zeros(5, dtype=[('date', '<f8'), ('price', '<f8')]),
mask=[False] * 5)
> data = data.view(ma.mrecords.mrecarray)
E AttributeError: 'module' object has no attribute 'mrecords'
/venv/local/lib/python2.7/site-packages/pandas/tests/frame/test_constructors.py:797: AttributeError
_____________ TestDataFrameConstructors.test_constructor_mrecarray _____________
[gw1] linux2 -- Python 2.7.6 /venv/bin/python
self = <pandas.tests.frame.test_constructors.TestDataFrameConstructors object at 0xd95ba3cc>
def test_constructor_mrecarray(self):
# Ensure mrecarray produces frame identical to dict of masked arrays
# from GH3479
assert_fr_equal = functools.partial(tm.assert_frame_equal,
check_index_type=True,
check_column_type=True,
check_frame_type=True)
arrays = [
('float', np.array([1.5, 2.0])),
('int', np.array([1, 2])),
('str', np.array(['abc', 'def'])),
]
for name, arr in arrays[:]:
arrays.append(('masked1_' + name,
np.ma.masked_array(arr, mask=[False, True])))
arrays.append(('masked_all', np.ma.masked_all((2,))))
arrays.append(('masked_none',
np.ma.masked_array([1.0, 2.5], mask=False)))
# call assert_frame_equal for all selections of 3 arrays
for comb in itertools.combinations(arrays, 3):
names, data = zip(*comb)
> mrecs = ma.mrecords.fromarrays(data, names=names)
E AttributeError: 'module' object has no attribute 'mrecords'
/venv/local/lib/python2.7/site-packages/pandas/tests/frame/test_constructors.py:826: AttributeError
=============================== warnings summary ===============================
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} | 7 | 2019-03-10T16:31:53Z | 2019-07-03T17:02:36Z | 2019-07-03T17:02:36Z | CONTRIBUTOR | null | I think we can take the final step an remove all remaining references to Panel.
```
(pandas) bash-3.2$ grep -r Panel pandas --exclude '*.pickle' --exclude *.msgpack --include '*.py' |wc
146 911 11617
```
note that the pickle & msgpack files will stay.
TODO
- [ ] Have `_constructor_expanddim` raise a `Notimplementederror` | {
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} | 12 | 2019-03-10T17:09:20Z | 2019-06-28T12:27:44Z | 2019-06-28T12:13:43Z | CONTRIBUTOR | null | One more step towards #22471. Happy to move the fixture to the `test_indexing` module if that's now the preferred form. | {
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TL;DR: `maybe_promote` is quite broken. #23982 tries to come up with tests that it should pass, and #25425 tries to fix the implementation. However, #23982 is quite big, so @jreback asked for a smaller version that (mostly) just tests existing behaviour. | {
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} | 3 | 2019-03-10T21:23:59Z | 2019-03-11T19:19:43Z | 2019-03-10T22:57:27Z | CONTRIBUTOR | null | skip on PY2 & old numpy for masked arrays
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} | 7 | 2019-03-10T22:35:40Z | 2019-03-12T20:48:28Z | 2019-03-12T20:45:24Z | CONTRIBUTOR | null | - [x] closes #25616
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
See discussion in #25616.
When `.replace` saw a value that looks like an int, it would try to convert it even if it caused an `OverflowError`. This issue is only happening in newer versions of pandas due to the addition of `coerce_to_target_dtype` in `_replace_coerce`. `coerce_to_target_dtype` is required to fix a lot of other issues, so the fix here was to prevent a coercion to an int that would cause an OverflowError by catching that exception, allowing the values to remain as objects.
I tried to play around with `coerce_to_target_dtype` as well (moving it until after the replace, only doing it when covert is True, etc.) but this caused various other coercion and replace tests to fail, so I left that untouched.
Tests have been added for both cases where I found `OverflowError` could occur with `replace`. | {
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} | 2 | 2019-03-10T23:11:20Z | 2019-03-11T00:24:54Z | 2019-03-11T00:24:54Z | CONTRIBUTOR | null | xref #25630
PR #25643
PR #25638 | {
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} | 3 | 2019-03-11T02:29:58Z | 2019-03-11T17:38:09Z | 2019-03-11T12:10:55Z | MEMBER | null | I randomly came across this code when messing around with some Mypy configurations. It looks like this code depends on third party packages which we don't even use in the code base. They also haven't been touched in 7-8 years so I assume this is dead code.
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} | 4 | 2019-03-11T03:35:00Z | 2020-06-25T17:38:09Z | 2020-06-25T17:38:09Z | MEMBER | null | When a user tries to specify a non-existent argument in `read_csv` the error message pertains to what seems to be an internal function (`parser_f`). This behavior might be a bit surprising / confusing to users, who would expect the error message to refer to the function actually being used.
```python
import pandas as pd
df = pd.read_csv("some_file.csv", bad_arg=True)
```
yields
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-d4606a60902f> in <module>()
----> 1 df = pd.read_csv("some_file.csv", bad_arg=True)
TypeError: parser_f() got an unexpected keyword argument 'bad_arg'
``` | {
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} | 9 | 2019-03-11T04:54:07Z | 2019-03-20T16:23:49Z | 2019-03-20T12:25:42Z | MEMBER | null | xref #25632
I didn't remove Panel reference from the whatsnew docs. Figured they should still be kept there for users who install older versions.
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https://api.github.com/repos/pandas-dev/pandas/issues/25650 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25650/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25650/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25650/events | https://github.com/pandas-dev/pandas/issues/25650 | 419,351,305 | MDU6SXNzdWU0MTkzNTEzMDU= | 25,650 | Support of read_excel password protected files | {
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] | closed | false | null | [] | null | 4 | 2019-03-11T08:42:45Z | 2020-12-16T14:25:17Z | 2019-03-11T09:22:26Z | NONE | null | #### Problem description
Most of the times the excel files are password protected and hence unable to be read by the read_excel unless some sort of pre-processing or manual work. Need support for reading the excel files through win32com or different API.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-1072-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.3
pytest: None
pip: 18.0
setuptools: 40.4.1
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
pyarrow: 0.8.0
xarray: None
IPython: 2.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml: 3.6.4
bs4: None
html5lib: 0.999
sqlalchemy: None
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 8 | 2019-03-11T09:37:20Z | 2019-03-12T12:56:18Z | 2019-03-12T12:43:02Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/25077 | {
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} | 1 | 2019-03-11T10:10:49Z | 2019-03-11T12:13:33Z | 2019-03-11T12:13:33Z | MEMBER | null | A test started to fail the last hours on the build with numpy/scipy master.
I assume it is related to a recent scipy change, commented about that here: https://github.com/scipy/scipy/pull/9920#issuecomment-471477082
But it is easy to fix on our side to just update the testing code (the construction of the sparse matrix)
Example log: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=9238&view=logs&jobId=3a03f79d-0b41-5610-1aa4-b4a014d0bc70
```
__________________________ TestAccessor.test_from_coo __________________________
[gw0] linux -- Python 3.7.2 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.arrays.sparse.test_array.TestAccessor object at 0x7fa1bf7982b0>
def test_from_coo(self):
sparse = pytest.importorskip("scipy.sparse")
row = [0, 3, 1, 0]
col = [0, 3, 1, 2]
data = [4, 5, 7, 9]
> sp_array = sparse.coo_matrix(data, (row, col))
pandas/tests/arrays/sparse/test_array.py:1094:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/scipy/sparse/coo.py:187: in __init__
if check_shape(shape) != self._shape:
../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/scipy/sparse/sputils.py:279: in check_shape
new_shape = tuple(operator.index(arg) for arg in args)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.0 = <tuple_iterator object at 0x7fa1bf7987b8>
> new_shape = tuple(operator.index(arg) for arg in args)
E TypeError: 'list' object cannot be interpreted as an integer
```
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} | 5 | 2019-03-11T12:47:45Z | 2019-03-13T13:34:33Z | 2019-03-13T12:59:06Z | MEMBER | null | xref #19269
https://github.com/pandas-dev/pandas/blob/de52d0b10c28318ae48390dbe7abcd1807a1154a/pandas/core/categorical.py#L7 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25656 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25656/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25656/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25656/events | https://github.com/pandas-dev/pandas/issues/25656 | 419,459,337 | MDU6SXNzdWU0MTk0NTkzMzc= | 25,656 | Rolling.apply documentation not clear | {
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"descript... | closed | false | null | [] | null | 3 | 2019-03-11T13:07:05Z | 2019-03-16T02:34:06Z | 2019-03-14T01:53:42Z | NONE | null | The current documentation for [rolling.apply](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.Rolling.apply.html) states:
> func : function
> Must produce a single value from an ndarray input if raw=True or a Series if raw=False.
This makes it sound like a series consisting of multiple values can be returned by the apply func. This is not true. The following error results:
Pandas v 0.24.1
```python
samples = roll.apply(lambda x: pd.Series([1]), raw=True)
example = pd.Series(np.random.rand(10000,))
example.rolling(100).apply(lambda x: pd.Series([1,2,3,4]), raw=False)
~/anaconda3/envs/pandas/core/series.py in wrapper(self)
91 return converter(self.iloc[0])
92 raise TypeError("cannot convert the series to "
---> 93 "{0}".format(str(converter)))
94
95 wrapper.__name__ = "__{name}__".format(name=converter.__name__)
TypeError: cannot convert the series to <class 'float'>
``` | {
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} | 2 | 2019-03-11T13:11:11Z | 2019-03-11T13:56:27Z | 2019-03-11T13:55:46Z | CONTRIBUTOR | null | …ne-aware DatetimeIndex
manual backport of https://github.com/pandas-dev/pandas/pull/25629 | {
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"description": "Warnings... | open | false | null | [] | null | 5 | 2019-03-11T13:34:35Z | 2021-06-27T19:51:21Z | null | NONE | null | #### Code example where setting an index with drop=false produce an ambiguous dataframe
```python
import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3, 4],
'value': ['A', 'B', 'C', 'D']})
df = df.set_index(['id'], drop=False)
df = df.sort_values(by=['value', 'id'], ascending=[True, False])
# ValueError: 'id' is both an index level and a column label, which is ambiguous.
```
#### Problem description
The change introduced in _sort_values_ in version 0.23.0, allows specifying index or column level names. A valid feature. The problem is that you can still set an index and not dropping the value. (drop = False). By design, these 2 features are contradicting. You should not allow to define a dataframe that is raising exception when you use some of its functionalities. Otherwise, every time that you set index not dropping, you have to rename the index. Such us:
```
df = df.set_index(['id'], drop=False)
df.index.names = ['id_index']
```
#### Expected Output
##### Either
sort_value should have a priority order to use index names (1st) or column names (2nd) if not found in index. And to stop raising an exception on ambiguous columns
##### Or
set_index should force an index name to be set if drop=False. And the column name should be different than any existing columns in the dataframe
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: 0.5.1
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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... | closed | false | null | [] | null | 3 | 2019-03-11T15:22:11Z | 2019-05-01T12:06:29Z | 2019-05-01T12:06:29Z | CONTRIBUTOR | null | - closes #17391
- tests added (two tests taken from #22114)
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
Beside the bug of decreasing TimedeltaIndex, this PR also solve the following
```
In [2]: idx1 = pd.to_timedelta(range(1,20), unit='s')
In [3]: idx2 = pd.to_timedelta((1,2,3,4,5,6,7,8,9,10,20), unit='s')
In [4]: result = idx1.intersection(idx2)
In [5]: result
Out[5]:
TimedeltaIndex(['00:00:01', '00:00:02', '00:00:03', '00:00:04', '00:00:05',
'00:00:06', '00:00:07', '00:00:08', '00:00:09', '00:00:10',
'00:00:11', '00:00:12', '00:00:13', '00:00:14', '00:00:15',
'00:00:16', '00:00:17', '00:00:18', '00:00:19'],
dtype='timedelta64[ns]', freq=None)
```
The above is also added into the tests. | {
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The actual contributors are not picked up by our `.. contributors::` sphinx extension, as the commit authors area lost in the backporting process using MeeseeksDev.
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```
asv compare upstream/master HEAD -s --sort ratio
Benchmarks that have improved:
before after ratio
[63755498] [eb051291]
<plot_perf~1> <plot_perf>
- 67.0±0.8ms 57.4±0.4ms 0.86 plotting.FramePlotting.time_frame_plot('hexbin')
- 61.1±2ms 50.3±0.7ms 0.82 plotting.FramePlotting.time_frame_plot('scatter')
- 48.3±0.2ms 39.6±0.2ms 0.82 plotting.TimeseriesPlotting.time_plot_regular_compat
- 52.3±2ms 42.2±0.4ms 0.81 plotting.TimeseriesPlotting.time_plot_irregular
Benchmarks that have stayed the same:
before after ratio
[63755498] [eb051291]
<plot_perf~1> <plot_perf>
171±4ms 175±5ms 1.02 plotting.SeriesPlotting.time_series_plot('hist')
92.9±0.2ms 94.1±0.2ms 1.01 plotting.SeriesPlotting.time_series_plot('pie')
143±0.2ms 144±0.2ms 1.01 plotting.FramePlotting.time_frame_plot('pie')
851±6ms 844±6ms 0.99 plotting.FramePlotting.time_frame_plot('kde')
1.97±0.02s 1.94±0.01s 0.99 plotting.TimeseriesPlotting.time_plot_table
1.20±0.1s 1.18±0.1s 0.99 plotting.Misc.time_plot_andrews_curves
80.1±5ms 78.7±6ms 0.98 plotting.SeriesPlotting.time_series_plot('line')
736±10ms 723±10ms 0.98 plotting.SeriesPlotting.time_series_plot('kde')
193±0.2ms 190±1ms 0.98 plotting.FramePlotting.time_frame_plot('barh')
87.6±0.3ms 85.7±0.3ms 0.98 plotting.SeriesPlotting.time_series_plot('bar')
208±5ms 203±3ms 0.98 plotting.FramePlotting.time_frame_plot('area')
87.5±0.4ms 85.5±0.6ms 0.98 plotting.SeriesPlotting.time_series_plot('barh')
199±5ms 192±2ms 0.96 plotting.SeriesPlotting.time_series_plot('area')
196±0.8ms 189±0.4ms 0.96 plotting.FramePlotting.time_frame_plot('bar')
91.3±0.4ms 86.9±10ms 0.95 plotting.FramePlotting.time_frame_plot('line')
201±2ms 191±3ms 0.95 plotting.FramePlotting.time_frame_plot('hist')
83.5±0.2ms 76.7±1ms 0.92 plotting.TimeseriesPlotting.time_plot_regular
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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intention originally to just deal with the commented out instance of `pytest.raises` for #24332 but may have gone too far!
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} | 0 | 2019-03-11T17:42:15Z | 2019-03-11T17:44:18Z | 2019-03-11T17:44:18Z | NONE | null | Backport PR #25669: Add 0.24.1 and 0.24.2 to whatsnew index | {
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} | 2 | 2019-03-11T18:37:29Z | 2019-08-08T13:38:48Z | 2019-08-08T13:38:48Z | MEMBER | null | In https://github.com/pandas-dev/pandas/blob/master/doc/source/development/developer.rst, there is no affordance for storing RangeIndex without serializing it to a column of integers. This wastes both memory and time
I'll propose an evolution of the metadata that permits "non-serialized" indexes like RangeIndex to be stored without a conversion step of some kind
This will have to mind _forward_ compatibility (so we can read old files, but _not_ backward compatibility -- i.e. allowing _new_ files to be read by _old_ readers -- see below). I would suggest changing the `index_columns` to include dictionaries like
```
{
'kind': 'range',
'start': 0,
'stop': 10,
'step': 1
}
```
versus
```
{
'kind': 'serialized',
'field_name': '__index_level_0__'
}
```
So if a string is encountered in this field (instead of a dict), we know it is "old" metadata. This will break old readers but I think that is OK
Cross ref with https://issues.apache.org/jira/browse/ARROW-1639
cc @cpcloud @martindurant @kszucs @xhochy | {
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} | 2 | 2019-03-11T20:07:35Z | 2019-03-14T21:20:44Z | 2019-03-14T21:20:44Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(dict(a=np.repeat(unichr(170),1)))
df[df["a"]==unichr(170)]
# Traceback (most recent call last):
# File "pandas/core/ops.py", line 1728, in wrapper
# (is_extension_array_dtype(other) and not is_scalar(other))):
# File "pandas/core/dtypes/common.py", line 1749, in is_extension_array_dtype
# registry.find(dtype) is not None)
# File "pandas/core/dtypes/dtypes.py", line 89, in find
# return dtype_type.construct_from_string(dtype)
# File "pandas/core/dtypes/dtypes.py", line 699, in construct_from_string
# raise TypeError(msg.format(string))
# UnicodeEncodeError: 'ascii' codec can't encode character u'\xaa' in position 0: ordinal not in range(128)
```
#### Problem description
The above comparison should not fail. The problem was introduced in 0.24 and 0.24.1 for Python 2.7 in the ``construct_from_string`` methods of the new datatypes DateTimeTZDtype, IntegerArray and SparseDtype, when trying to ``msg.format(string)`` containing a unicode character above the 128 ascii range, as Python 2.7 is trying to get it ascii encoded by default.
I guess that changing the raised ``TypeError`` not to print the target string with format (as the case with other ``construct_from_string`` methods) may be a way of fixing this.
#### Expected Output
Having the input dataframe.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-514.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: 2.8.5
pip: 9.0.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.13.0
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml.etree: 3.6.0
bs4: 4.4.1
html5lib: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-03-11T20:57:20Z | 2019-03-11T21:55:02Z | 2019-03-11T21:54:57Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
data1 = pd.DataFrame.from_dict({
'id': [1],
'A' : [np.nan]
}).astype({'id':"int64", "A":"Int64"})
data2 = pd.DataFrame.from_dict({
'id': [1],
'B' : [3]
}).astype({'id':"int64", "B":"Int64"})
data1.merge(data2, on="id")
```
#### Problem description
My apologies if this has been posted before I didn't see it. I don't seem to be able to merge dataframes if there is a NaN present in an 'Int64' column (note the capitol I). The example above throws with "TypeError: data type not understood".
I would expect the merge to work normally and simple have the column with a NaN there.
#### Expected Output
pd.DataFrame.from_dict({
'id': [1],
'A' : [np.nan]
'B' : [3]
}).astype({'id':"int64", "A":"Int64", "B":"Int64"})
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 9.0.3
setuptools: 39.2.0
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.18
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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(NOTE: overlapping PR with #25675) | {
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} | 2 | 2019-03-12T00:32:27Z | 2019-03-12T09:45:27Z | 2019-03-12T01:38:17Z | NONE | null | Long shot (and apologies if duplicate, but nothing came up). It would be great if there was an "alias" to the `df.loc[i, j]` in the form of `df[i, j]`. | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 1 | 2019-03-12T01:06:15Z | 2021-06-27T19:52:24Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas.util.testing as tm
tm.makeFloatIndex().difference(tm.makeStringIndex()) # returns dtype(float), should possibly be dtype(O)
tm.makeFloatIndex().symmetric_difference(tm.makeIntIndex()) # returns dtype(float), should possibly be dtype(O)
pd.Float64Index([1,2,3]).intersection(pd.Int64Index([1,2,3])) # returns dtype(float), should possibly be dtype(O)
```
#### Problem description
With this PR (https://github.com/pandas-dev/pandas/pull/23538), all union operations between inconsistent index types will return a standard index dtype of object. Perhaps this should hold true for all setops. The biggest violator of this currently would be difference, which just returns the left hand index. Intersection and symmetric difference handle this fine for the most part, however there are some cases where they do not respect this, for example `FloatIndex.difference(IntIndex)`.
#### Output of ``pd.show_versions()``
0.25.0.dev0
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For example
```python
start_date = '2019-03-02'
end_date = '2019-03-03'
h = pd.DataFrame(index=pd.date_range(start_date, end_date, freq='H', closed='left') )
d = pd.DataFrame(index=pd.date_range(start_date, end_date, freq='D', closed='left') )
assert len(d.asfreq('H').ffill().index) == len(h.index)
```
When `freq='H'` last index would be `2019-03-03 00:00:00` .
Usually we need ` closed='left'` , make last index be `2019-03-02 23:00:00`
When `freq='D'` , last index would be `2019-03-03` . It is fine to use ` closed='left'` too.
But, if I need `s.asfreq('H').ffill()`, it would not result same index as `pd.DataFrame(index=pd.date_range(start_date, end_date, freq='H', closed='left' ))`
I mean, people usually use `2019-03-03` as `2019-03-02 23:59:59 599999` if they assign freq='H' .
Try to imagine, we have a dataset, I need analyze how it perform on day , hour and minute level.
The end day is 2019-03-03,so we need align `M` , `H`, `D` with same end `2019-03-03` .Current behavious is not convenient for it. I think this should be a built-in feature.
Maybe add an option to `close_freq='H' ` .
Which means
```
from datetime import timedelta
end_date = pd.to_datetime(end_date) - timedelta(hours=1)
```
Would be good with default `end_date = pd.to_datetime(end_date) - timedelta(microseconds=1)`
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In [1]: import pandas as pd
In [2]: s = pd.Series([1, 2], dtype='Sparse[int]')
In [3]: s.to_dense()
Out[3]:
0 1
1 2
dtype: Sparse[int64, 0]
``` | {
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} | 0 | 2019-03-12T03:06:01Z | 2019-05-14T14:13:46Z | 2019-05-14T14:13:46Z | CONTRIBUTOR | null | I'd like to add a `.sparse` accessor to DataFrame, to assist with deprecating SparseDataFrame.
It'll contain
- from_spmatrix (part of the SparseDataFrame constructor)
- to_dense (SparseDataFrame.to_dense)
- to_coo (SparseDataFrame.to_coo)
- density
A few design questions:
1. When should the `_validate` raise?
a. When there are *no sparse columns*
b. When there is *any non-sparse columns*
c. Never.
It's slightly easier to implement if we assume everything is sparse.
2. Return value of `DataFrame.sparse.density`. If we mirror `SparseDataFrame.density`, this returns a float. Would it be more useful to return a Series with the density of each column? (and users can `.mean()` if they want the average density)
I believe that with these methods, the essentially all the functionality of SparseDataFrame will be replicable with a DataFrame of sparse values (the main exception being an expanding `__setitem__` creating a sparse column by default; but that's OK to not provide that functionality). | {
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} | 10 | 2019-03-12T03:27:15Z | 2019-05-14T14:13:49Z | 2019-05-14T14:13:46Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/25681
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Refer to #25686 | {
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} | 8 | 2019-03-12T10:36:18Z | 2019-03-30T17:36:36Z | 2019-03-30T17:36:23Z | NONE | null | fixes #25623
- [x] closes #25623
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25688 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25688/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25688/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25688/events | https://github.com/pandas-dev/pandas/pull/25688 | 419,975,977 | MDExOlB1bGxSZXF1ZXN0MjYwMzU0NjM3 | 25,688 | Precursor for #25607 | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Nothing new
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https://api.github.com/repos/pandas-dev/pandas/issues/25689 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25689/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25689/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25689/events | https://github.com/pandas-dev/pandas/issues/25689 | 419,987,601 | MDU6SXNzdWU0MTk5ODc2MDE= | 25,689 | rolling apply gives different output when raw = True or raw = False | {
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I am working with pandas.Series.apply() and got some unexpected output. I created the following example code (I could not manage to reproduce the issue with any other values, maybe there is something that I am not seeing):
```python
import pandas as pd
import numpy as np
data=pd.Series([12.74,
12.99,
12.81,
12.7,
11.46,
10.91,
11.05,
10.71,
11.29,
11.73,
7.42,
6.51,
5.12,
3.9,
4.62,
3.97,
4.48,
4.09,
4.68,
4.68,
3.59,
2.54,
2.03,
2.52,
1.98,
0.8,
1.47,
1.49,
0.38,
0.44,
0.93,
1.23,
1.62,
1.28,
2.79,
2.88,
4.28,
1.74,
3.31,
3.32,
4.23,
3.96,
4.15,
2.81,
4.21,
4.5,
4.22,
1.34,
2.26,
1.72,
1.52,
3.54,
2.43,
2.61,
4.48,
5.3,
5.9,
6.55,
7.37,
8.27,
6.97,
8.81,
8.45,
8.09,
7.02,
8.69,
8.15,
7.41,
8.61,
9.02,
9.44,
10.28,
9.0,
7.87,
8.08,
7.81,
6.41,
6.02,
7.04,
5.7,
6.2,
5.96,
6.28,
6.81,
6.66,
6.17,
6.92,
7.92,
5.78,
7.33,
6.9,
6.36,
8.24,
7.41,
8.96,
10.55,
11.43,
12.44,
13.78,
13.44,
14.23,
11.71,
12.36,
13.41,
13.15,
14.76,
14.23,
14.75,
13.76,
14.1,
13.63,
12.5,
11.3,
11.48,
12.9,
10.71,
10.62,
10.33,
10.29,
10.4,
11.64,
12.92,
12.79,
11.48,
11.2,
10.79,
10.04,
10.51,
11.2,
12.88,
11.72,
11.98,
12.57,
13.37,
13.15,
13.5,
12.79,
14.6,
15.36,
15.64,
17.39,
16.43,
16.82,
18.15,
18.48,
19.12,
20.47,
20.8,
19.66,
20.95,
20.81,
21.63,
15.88,
17.17,
17.9,
18.41,
17.3,
17.66,
17.71,
18.31,
17.55,
17.28,
17.36,
15.46,
15.15,
14.77,
14.79,
15.18,
15.2,
14.05,
13.91,
14.24,
13.73,
14.81,
16.61,
17.77,
18.17,
18.49,
18.23,
19.34,
18.85,
18.73,
19.83,
21.43,
22.28,
20.27,
19.81,
20.11,
20.25,
18.82,
20.73,
20.03,
19.91,
18.9,
18.71,
18.8,
17.33,
19.98,
18.76,
19.71,
19.56,
20.26,
20.89,
19.41,
19.82,
19.5,
18.26,
17.92,
17.58,
17.05,
17.5,
15.32,
16.44,
17.82,
18.72,
19.83,
19.26,
19.4,
18.51,
18.83,
18.02,
18.37,
17.49,
17.78,
16.5,
17.6,
18.3,
19.43,
17.78,
17.43,
18.19,
16.8,
16.59,
16.27,
16.91,
15.98,
15.08,
13.93,
14.44,
14.72,
16.96,
15.98,
14.68,
13.85,
12.35,
10.25,
11.88,
12.16,
11.68,
13.28,
11.78,
11.36,
11.15,
9.76,
9.72,
8.64,
6.99,
6.82,
6.07,
6.53,
6.45,
7.12,
6.94,
6.33,
5.46,
5.46,
2.63,
2.63,
1.89,
2.45,
3.29,
3.73,
5.43,
3.98,
4.53,
5.46,
5.57,
4.66,
3.6,
4.24,
5.25,
3.54,
2.51,
1.65,
1.72,
3.58,
5.15,
5.25,
4.87,
4.04,
4.49,
4.8,
4.94,
5.66,
6.05,
7.49,
6.06,
6.23,
6.65,
4.92,
5.04,
4.72,
4.4,
5.17,
4.36,
4.97,
6.23,
5.03,
4.21,
3.37,
3.28,
2.82,
1.13,
0.31,
1.49,
1.33,
0.32,
1.01,
0.12,
0.92,
1.63,
3.79,
4.82,
4.67,
3.53,
2.38,
1.92,
0.97,
2.47,
3.01,
2.31,
2.51,
3.97,
3.44,
4.13,
3.44,
3.51,
3.2,
3.47,
5.06,
5.95,
4.28,
3.87,
5.67,
4.88,
5.48,
6.46,
5.0,
3.73,
3.27,
3.33,
3.92,
2.68,
1.3,
0.32,
1.23,
1.26,
2.74,
2.65,
1.19,
1.71,
4.18,
3.47,
2.13,
1.02,
2.46,
3.46,
2.59,
2.17,
1.95,
2.38,
1.17,
1.88,
4.03,
4.76,
6.38,
7.53,
8.28,
9.26,
7.68,
6.99,
6.71,
7.64,
8.23,
8.66,
8.78,
7.8,
8.08,
7.01,
7.73,
8.6,
8.49,
9.76,
8.92,
8.11,
7.76,
7.67,
8.79,
9.0,
8.22,
6.34,
8.1,
7.23,
7.83,
7.48,
8.16,
8.5,
7.8,
8.83,
8.65])
ravg=data.rolling(600,min_periods=1).apply(np.mean,raw=True)
ravg2=data.rolling(600,min_periods=1).apply(np.mean,raw=False)
print('%.35f' % ravg.iloc[-1])
print('%.35f' % ravg2.iloc[-1])
```
#### Problem description
#### Expected Output
I expect the print statement in the end to return 9.34999999999999964472863211994990706
#### Output of ``pd.Series.apply()``
When setting raw=True, I get the following output:
```
9.34999999999999964472863211994990706
```
When setting raw=False, I get the following output:
```
9.35000000000001030286966852145269513
```
Is the difference intentional or a bug?
Kind regards,
Nick
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} | 7 | 2019-03-12T15:43:12Z | 2019-03-27T14:30:47Z | 2019-03-27T12:46:49Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/24886#issuecomment-468984081
have included Series constructed with an empty dict.
Series constructed with an empty list, tuple or generator are not the same as Series() or Series({}), so have not included them in this pass. (although for many tests the difference in the Index is not relevant)
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} | 0 | 2019-03-12T17:06:00Z | 2019-04-05T00:45:20Z | 2019-04-05T00:45:20Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```ipython
Python 3.7.2 (default, Feb 21 2019, 17:35:59) [MSC v.1915 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.3.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas as pd
In [2]: pd.DataFrame(())
Out[2]:
Empty DataFrame
Columns: []
Index: []
In [3]: pd.DataFrame((()))
Out[3]:
Empty DataFrame
Columns: []
Index: []
In [4]: pd.DataFrame(((),()))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-8f97dbc592ec> in <module>
----> 1 pd.DataFrame(((),()))
~\OneDrive\code\pandas-simonjayhawkins\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
433 if is_named_tuple(data[0]) and columns is None:
434 columns = data[0]._fields
--> 435 arrays, columns = to_arrays(data, columns, dtype=dtype)
436 columns = ensure_index(columns)
437
~\OneDrive\code\pandas-simonjayhawkins\pandas\core\internals\construction.py in to_arrays(data, columns, coerce_float, dtype)
396 if isinstance(data[0], (list, tuple)):
397 return _list_to_arrays(data, columns, coerce_float=coerce_float,
--> 398 dtype=dtype)
399 elif isinstance(data[0], compat.Mapping):
400 return _list_of_dict_to_arrays(data, columns,
~\OneDrive\code\pandas-simonjayhawkins\pandas\core\internals\construction.py in _list_to_arrays(data, columns, coerce_float, dtype)
423 def _list_to_arrays(data, columns, coerce_float=False, dtype=None):
424 if len(data) > 0 and isinstance(data[0], tuple):
--> 425 content = list(lib.to_object_array_tuples(data).T)
426 else:
427 # list of lists
TypeError: Argument 'rows' has incorrect type (expected list, got tuple)
In [6]: pd.DataFrame(([],[]))
Segmentation fault
```
on 23.4
```python-traceback
Python 3.7.2 (default, Feb 21 2019, 17:35:59) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.__version__
'0.23.4'
>>> pd.DataFrame(((),()))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pandas\core\frame.py", line 422, in __init__
raise ValueError('DataFrame constructor not properly called!')
ValueError: DataFrame constructor not properly called!
>>> pd.DataFrame(([],[]))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\pandas\core\frame.py", line 422, in __init__
raise ValueError('DataFrame constructor not properly called!')
ValueError: DataFrame constructor not properly called!
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: c28529051ea07800792889a84cd7ffb34d9f5538
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.25.0.dev0+240.gc28529051.dirty
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.6.3
Cython: 0.29.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-03-12T17:56:22Z | 2019-03-19T04:28:10Z | 2019-03-19T04:28:10Z | MEMBER | null | Spurred by conversation in https://github.com/pandas-dev/pandas/pull/25224#discussion_r264786749 right now we are using plain old dictionaries in most of the groupby aggregation functions. For Py35 this makes the order of the returned result non-deterministic.
We could theoretically make a stronger assertion around the order here by replacing those dicts with OrderedDicts | {
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} | 7 | 2019-03-12T19:15:40Z | 2019-03-19T05:46:45Z | 2019-03-19T04:28:10Z | CONTRIBUTOR | null | - [x] closes #25692
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"description": "np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate",
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"color": "009800"... | closed | false | null | [] | null | 4 | 2019-03-12T19:37:27Z | 2019-09-18T14:02:13Z | 2019-09-18T14:02:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.SparseDataFrame({1: {2: 3}, 3: {3: 4}}, dtype=np.int64, default_fill_value=0)
```
#### Problem description
The command fails with `ValueError: Cannot convert non-finite values (NA or inf) to integer`. To me the problem seems to be that missing dict entries as always treated as `np.nan` instead of the fill value, however the nan values cannot be casted to ints. I haven't found any way to work around this yet that is feasible with larger data. E.g. if I use `pd.SparseDataFrame({1: {2: 3}, 3: {3: 4}}, dtype=np.int64)`, the fill value is `np.nan`, also if I use `pd.SparseDataFrame({1: {2: 3}, 3: {3: 4}}, default_fill_value=0)` (then just the dtype changes).
#### Expected Output
```python
1 3
2 3 0
3 0 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 9.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
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: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 2 | 2019-03-12T20:25:37Z | 2019-03-12T21:07:01Z | 2019-03-12T20:46:08Z | NONE | null | Backport PR #25592: BUG: fix usage of na_sentinel with sort=True in factorize() | {
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} | 7 | 2019-03-12T20:33:59Z | 2019-05-07T01:01:55Z | 2019-05-07T01:01:43Z | MEMBER | null | This is a possible alternative solution to what we have been discussing in https://github.com/pandas-dev/pandas/pull/25592.
This moves the logic into `safe_sort`, with:
- adding a `check_outofbounds` keyword to disable extra checks (otherwise the performance benefit of `take_1d` is lost)
- fixing `safe_sort` to work for EAs
The `check_outofbounds` make it a bit more complicated, but without it, we can't benefit of the performance improvement for which `take_1d` was used originally in `factorize`.
(another solution is to simply decide that this performance improvement is not worth this extra code, and we simply use the current `safe_sort` (but fixed to work for EAs) in `factorize`)
Need to add some more tests for the combination of EAs with a custom na_sentinel (a case that is currently broken) | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 1 | 2019-03-12T23:50:46Z | 2019-03-13T02:52:46Z | 2019-03-13T02:52:41Z | NONE | null | I need your kind help in an issue that I face with plotting. I have an excel sheet with data for couple of months, Im trying to plot graphs based on one month at a time (line chart over days of that month). For this I let the pandas read from the excel and create a dataframe which include a column for all dates (sorted) and column for the corresponding data values. Whenever I match the first month in the dataframe time column, the plot works fine but when i try to choose any following month in the dataframe time column, the plot function throws a KeyError: 0L
Here is my code that I use trying to match the data by month to plot it:
file = "D:\\Raw.xlsx"
df = pd.read_excel(file, 0)
df['timestamp'] = pd.to_datetime(df.timestamp)
df = df[df['timestamp'].dt.month == 2]
fig, ax = plt.subplots()
df['timestamp'] = df['timestamp'].dt.strftime('%#m/%d/%y')
df.plot(ax=ax, xticks=df.index, figsize=(15, 5), rot=45)
ax.tick_params(axis='x', labelsize=9)
ax.set_xticklabels(df.timestamp)
plt.grid(True)
plt.show() | {
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} | 8 | 2019-03-13T01:23:30Z | 2019-08-21T21:09:14Z | 2019-03-13T11:52:35Z | MEMBER | null | Fixes issues with the LINT failures as of recent. Quick fix with more permanent solutions split between #25697 and #25622
@TomAugspurger | {
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} | 3 | 2019-03-13T01:56:19Z | 2019-03-19T23:40:31Z | 2019-03-19T23:40:31Z | CONTRIBUTOR | null | I messed up the logic somewhere along the way.
```python
In [11]: df = pd.DataFrame({"A": pd.SparseArray([0, 1])})
In [12]: type(pd.concat([df, df]))
Out[12]: pandas.core.sparse.frame.SparseDataFrame
```
That should be a DataFrame with sparse values.
When should concat return a SparseSeries/Frame?
a.) When *any* of the inputs are `SparseSeries` / `SparseDataFrame`
b.) When *all* of the inputs are SparseSeries/Frame.
Option b is probably least disruptive. Implementing it isn't a pain. | {
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```python
with pd.HDFStore(hdf) as store:
chunks = store.select(key, chunksize=20, where='position > 14599 & position < 14605')
for chunk in chunks:
print(chunk)
print('end')
```
#### Problem description
When iterating through chunks from an HDFStore, the loop runs ok but never breaks when a condition is provided in the select statement e.g. in the above example the chunks are printed but it doesn't print 'end'. If I omit the 'where' condition, it works as expected, i.e. the chunks are printed and the loop breaks.
#### Expected Output
```
#chunks
end
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.1
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.15.2
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.0.4
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-03-13T12:50:10Z | 2019-03-30T23:45:26Z | 2019-03-30T23:45:26Z | NONE | null |
Here is a link to the discussion: [pandas Series groupby with one group](https://stackoverflow.com/questions/55129421/pandas-series-groupby-with-one-group)
#### Code Sample, a copy-pastable example if possible
```python
# from the stackoverflow link above
import pandas as pd
data = pd.DataFrame(data={'date':[pd.Timestamp('2016-02-15')]*3,
'time':[pd.Timedelta(x) for x in ('07:30:00','10:10:00','11:10:00')],'name':['A']*3, 'N':[1,2,3]}
).set_index(['date','time','name']).sort_index()
data = data[ data.index.get_level_values('time')>=pd.to_timedelta('09:30:00') ]
dataGB = data['N'].groupby(['date','name'])
print(data)
print('Number of groups:',len(dataGB))
print(dataGB.sum())
print(pd.__version__)
```
#### Problem description
1. The code produces 2 groups while clearly there should be only one.
2. dataGB.sum() result is incorrect
#### **Real** Output
```
>>> print(data)
N
date time name
2016-02-15 10:10:00 A 2
11:10:00 A 3
>>> print('Number of groups:',len(dataGB))
Number of groups: 2
>>> print(dataGB.sum())
date 2
name 3
Name: N, dtype: int64
>>> print(pd.__version__)
0.24.1
```
#### **Expected** Output
```
>>> print(data)
N
date time name
2016-02-15 10:10:00 A 2
11:10:00 A 3
>>> print('Number of groups:',len(dataGB))
Number of groups: 1
>>> dataGB.sum()
date name
2016-02-15 A 5
Name: N, dtype: int64
>>> print(pd.__version__)
0.24.1
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.11.6.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.utf-8
LANG: en_US.utf-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 3.3.2
pip: 19.0.3
setuptools: 39.0.1
Cython: 0.27.3
numpy: 1.16.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.9999999
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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https://api.github.com/repos/pandas-dev/pandas/issues/25705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25705/events | https://github.com/pandas-dev/pandas/issues/25705 | 420,485,757 | MDU6SXNzdWU0MjA0ODU3NTc= | 25,705 | Dateoffset - month offset shows incorrect result | {
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] | closed | false | null | [] | null | 1 | 2019-03-13T12:58:06Z | 2019-03-13T17:25:58Z | 2019-03-13T17:25:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
(Pdb) dr[29:32]
DatetimeIndex(['2015-07-30', '2015-07-31', '2015-08-01'], dtype='datetime64[ns]', freq='D')
(Pdb) dr[29:32]-pd.DateOffset(months=3)
*** ValueError: Inferred frequency None from passed values does not conform to passed frequency D
(Pdb) dr[29:32]-pd.DateOffset(months=4)
DatetimeIndex(['2015-03-30', '2015-03-31', '2015-04-01'], dtype='datetime64[ns]', freq='D')
(Pdb) dr[29:32]-pd.DateOffset(months=2)
DatetimeIndex(['2015-05-30', '2015-05-31', '2015-06-01'], dtype='datetime64[ns]', freq='D')
```
#### Problem description
Not able to offset 3 months
Using pandas - 0.24.1
#### Expected Output
DatetimeIndex(['2015-05-01', '2015-05-02', '2015-05-03'], dtype='datetime64[ns]', freq='D')
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.1
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.2.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-03-13T13:25:34Z | 2019-03-13T16:40:10Z | 2019-03-13T14:16:55Z | CONTRIBUTOR | null | (cherry picked from commit 93568cc3c529a10a897879561b4cd75486c618be)
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} | 1 | 2019-03-13T13:41:03Z | 2019-03-13T21:46:40Z | 2019-03-13T21:46:39Z | CONTRIBUTOR | null | The test `test_write_fspath_all` in https://github.com/pandas-dev/pandas/blob/86879ac9af524adc684bec77c621d27b19a36ca0/pandas/tests/io/test_common.py#L256 asserts that two files are byte-for-byte identical. This can fail for files like stata, which encode other information like the current timestamp, in the binary data.
DataFrame.to_stata provides a keyword `time_stamp` to control whether the timestamp is written in the binary file.
To avoid spurious test failures from writing the first file at timestamp 1, and the second file at timestamp 2, we should pass that keyword through by adding `{'time_stamp': False}` to the dict in https://github.com/pandas-dev/pandas/blob/86879ac9af524adc684bec77c621d27b19a36ca0/pandas/tests/io/test_common.py#L254 | {
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} | 5 | 2019-03-13T14:39:44Z | 2019-04-19T01:06:46Z | 2019-04-19T01:06:46Z | NONE | null | #### Large DataFrame with dates is slower in 0.24.0
```python
import pandas as pd
import numpy as np
d = '2018-11-29'
dt = '2018-11-26 11:18:27.0'
N = 10000000
df = pd.DataFrame({'dt': [np.datetime64(dt)] * N, 'd': [np.datetime64(d)] * N, 'r': [np.random.uniform()] * N})
```
#### Problem description
Using pandas < 0.24.0 dumping a large dataset (10M rows, 40 columns) which contains text, dates and floats/ints takes about 12 minutes but using version 0.24.0 the same code takes about 2hours and 40 minutes.
We understand the code base to create the csv has change a bit. I didn't notice the speed degradation when the columns are not dates. I've not checked with datetime objects.
We've tried to condense the problem to the few lines above. Below the results I get from %prun in each version.
#### ``%prun`` for pandas `0.24.0`:
```
121342 function calls (120733 primitive calls) in 153.774 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 153.774 153.774 {built-in method builtins.exec}
1 0.000 0.000 153.774 153.774 <string>:1(<module>)
1 0.000 0.000 153.774 153.774 generic.py:2873(to_csv)
1 0.000 0.000 153.773 153.773 csvs.py:130(save)
1 0.002 0.002 153.684 153.684 csvs.py:272(_save)
301 0.278 0.001 153.681 0.511 csvs.py:290(_save_chunk)
301 0.009 0.000 119.472 0.397 blocks.py:2200(to_native_types)
301 0.002 0.000 101.820 0.338 format.py:1300(_get_format_datetime64_from_values)
301 96.018 0.319 101.818 0.338 format.py:1249(_is_dates_only)
301 20.040 0.067 20.040 0.067 {pandas._libs.writers.write_csv_rows}
301 17.525 0.058 17.580 0.058 {pandas._libs.tslib.format_array_from_datetime}
602 13.766 0.023 13.766 0.023 {method 'astype' of 'numpy.ndarray' objects}
301 0.005 0.000 8.095 0.027 blocks.py:1982(to_native_types)
1204 5.798 0.005 5.798 0.005 {method 'reduce' of 'numpy.ufunc' objects}
302 0.003 0.000 5.796 0.019 base.py:1023(to_native_types)
302 0.005 0.000 5.777 0.019 base.py:1049(_format_native_types)
301 0.002 0.000 5.749 0.019 {method 'sum' of 'numpy.ndarray' objects}
301 0.001 0.000 5.747 0.019 _methods.py:34(_sum)
603 0.001 0.000 0.089 0.000 missing.py:25(isna)
603 0.003 0.000 0.088 0.000 missing.py:105(_isna_new)
[snip]
```
#### cProfile of my script with 0.24.0
(I took the liberty of removing paths/username information)
```
stats: 3434284 function calls (3423766 primitive calls) in 8642.079 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 8642.079 8642.079 ../lib/python3.7/site-packages/pandas/core/generic.py:2873(to_csv)
1 0.000 0.000 8642.077 8642.077 ../lib/python3.7/site-packages/pandas/io/formats/csvs.py:130(save)
1 0.042 0.042 8642.002 8642.002../lib/python3.7/site-packages/pandas/io/formats/csvs.py:272(_save)
5242 3.588 0.001 8641.950 1.649../lib/python3.7/site-packages/pandas/io/formats/csvs.py:290(_save_chunk)
5242 0.165 0.000 8294.922 1.582 ../lib/python3.7/site-packages/pandas/core/internals/blocks.py:2200(to_native_types)
5242 0.044 0.000 8203.096 1.565 ../lib/python3.7/site-packages/pandas/io/formats/format.py:1300(_get_format_datetime64_from_values)
5242 7757.167 1.480 8203.052 1.565 ../lib/python3.7/site-packages/pandas/io/formats/format.py:1249(_is_dates_only)
20968 445.184 0.021 445.184 0.021 {method 'reduce' of 'numpy.ufunc' objects}
5242 0.041 0.000 444.994 0.085 {method 'sum' of 'numpy.ndarray' objects}
5242 0.023 0.000 444.953 0.085 ../lib/python3.7/site-packages/numpy/core/_methods.py:34(_sum)
5242 212.677 0.041 212.677 0.041 {pandas._libs.writers.write_csv_rows}
15726 122.216 0.008 122.216 0.008 {method 'astype' of 'numpy.ndarray' objects}
5242 90.999 0.017 91.300 0.017 {pandas._libs.tslib.format_array_from_datetime}
5242 0.570 0.000 79.782 0.015 ../lib/python3.7/site-packages/pandas/core/internals/blocks.py:1982(to_native_types)
10484 0.200 0.000 43.947 0.004 ../lib/python3.7/site-packages/pandas/core/internals/blocks.py:730(to_native_types)
5243 0.031 0.000 7.008 0.001 ../lib/python3.7/site-packages/pandas/core/indexes/base.py:1023(to_native_types)
...the rest was removed...
```
#### ``%prun`` for pandas `0.23.4`:
```
71818 function calls (71517 primitive calls) in 52.371 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 52.371 52.371 {built-in method builtins.exec}
1 0.000 0.000 52.371 52.371 <string>:1(<module>)
1 0.000 0.000 52.371 52.371 frame.py:1653(to_csv)
1 0.000 0.000 52.370 52.370 csvs.py:123(save)
1 0.002 0.002 51.546 51.546 csvs.py:270(_save)
301 0.245 0.001 51.543 0.171 csvs.py:288(_save_chunk)
301 19.846 0.066 19.846 0.066 {pandas._libs.writers.write_csv_rows}
301 0.007 0.000 18.031 0.060 internals.py:2743(to_native_types)
301 17.654 0.059 17.698 0.059 {pandas._libs.tslib.format_array_from_datetime}
602 13.309 0.022 13.309 0.022 {method 'astype' of 'numpy.ndarray' objects}
301 0.006 0.000 7.480 0.025 internals.py:2052(to_native_types)
302 0.002 0.000 5.940 0.020 base.py:2408(to_native_types)
302 0.004 0.000 5.925 0.020 base.py:2434(_format_native_types)
2 0.823 0.411 0.823 0.411 {method 'close' of '_io.TextIOWrapper' objects}
301 0.001 0.000 0.293 0.001 format.py:1291(_get_format_datetime64_from_values)
301 0.249 0.001 0.292 0.001 format.py:1243(_is_dates_only)
603 0.001 0.000 0.081 0.000 missing.py:32(isna)
603 0.003 0.000 0.080 0.000 missing.py:112(_isna_new)
603 0.033 0.000 0.072 0.000 missing.py:189(_isna_ndarraylike)
1204 0.067 0.000 0.067 0.000 {method 'reduce' of 'numpy.ufunc' objects}
903 0.001 0.000 0.044 0.000 _methods.py:42(_any)
302 0.026 0.000 0.026 0.000 {method 'ravel' of 'numpy.ndarray' objects}
301 0.001 0.000 0.024 0.000 {method 'sum' of 'numpy.ndarray' objects}
```
#### cProfile of my script with 0.23.4
```
stats: 2170825 function calls (2165580 primitive calls) in 442.783 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 442.783 442.783 ../lib/python3.7/site-packages/pandas/core/frame.py:1653(to_csv)
1 0.000 0.000 442.782 442.782 ../lib/python3.7/site-packages/pandas/io/formats/csvs.py:123(save)
1 0.041 0.041 442.781 442.781 ../lib/python3.7/site-packages/pandas/io/formats/csvs.py:270(_save)
5242 2.815 0.001 442.730 0.084 ../lib/python3.7/site-packages/pandas/io/formats/csvs.py:288(_save_chunk)
5242 216.105 0.041 216.105 0.041 {pandas._libs.writers.write_csv_rows}
15726 125.188 0.008 125.188 0.008 {method 'astype' of 'numpy.ndarray' objects}
5242 0.121 0.000 90.872 0.017 ../lib/python3.7/site-packages/pandas/core/internals.py:2743(to_native_types)
5242 88.329 0.017 88.590 0.017 {pandas._libs.tslib.format_array_from_datetime}
5242 0.606 0.000 79.149 0.015 ../lib/python3.7/site-packages/pandas/core/internals.py:2052(to_native_types)
10484 0.188 0.000 46.683 0.004 ../lib/python3.7/site-packages/pandas/core/internals.py:755(to_native_types)
5243 0.026 0.000 7.077 0.001 ../lib/python3.7/site-packages/pandas/core/indexes/base.py:2408(to_native_types)
5243 0.039 0.000 6.497 0.001 ../lib/python3.7/site-packages/pandas/core/indexes/base.py:2434(_format_native_types)
```
#### What is Expected
No degradation in speed.
#### Output of ``pd.show_versions()`` for 0.24.0
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.72-68.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.0
pytest: None
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.4
patsy: None
dateutil: 2.8.0
pytz: 2018.9
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
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
#### Output of ``pd.show_versions()`` for 0.23.4
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.72-68.55.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 3.7.1
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.4
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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} | 10 | 2019-03-13T14:56:55Z | 2019-08-08T13:38:49Z | 2019-08-08T13:38:48Z | MEMBER | null | Closes #25672
- [x] closes #xxxx
- [x] tests added / passed (N/A)
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` (N/A)
- [ ] whatsnew entry (N/A) | {
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} | 3 | 2019-03-13T15:07:50Z | 2019-03-14T12:38:53Z | 2019-03-14T12:38:53Z | CONTRIBUTOR | null | This may be useful to downstream projects (e.g. https://github.com/apache/arrow/pull/3868).
Initially, I think the idea was for RangeIndex to be a drop-in optimized replacement for the equivalent Int64Index. Other libraries might want public access to the same optimizations. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25711 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25711/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25711/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25711/events | https://github.com/pandas-dev/pandas/issues/25711 | 420,590,193 | MDU6SXNzdWU0MjA1OTAxOTM= | 25,711 | Unexpected behavior for pd.DataFrame.assign() | {
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```python
# make a dataframe
df = pd.DataFrame({
'a': [1,2,3,4]
})
# chained assignment does not work for columns created by .assign() in the same chain.
# KeyError: 'b'
df\
.assign(b=2*df['a'])\
.assign(c=2*df['b'])
```
#### Problem description
I think chained assignment should work with each part of the chain returning the modified DataFrame with the new column, but gives a key error when referencing columns from previous assignments in the chain.
I understand that after modifying the original DataFrame, the copy does not have the name `df`, so it can't make the reference to df['b']. But, I think there should be a way to do chained assignment.
Running the following we see that using .assign() once does create a modified version of the DataFrame with 'b' as an accessible column.
```python
# assigning one new column works fine. Column 'b' is accessible in the DataFrame.
df\
.assign(b=2*df['a'])
```
Also, I don't see any need to optimize assignment, because if I know that none of the columns I want to create depend on any of the other columns I want to create, I can write the following.
```python
# there shouldn't be any need to optimize chained assignment, because if I know I need to create multiple new columns that don't refer to newly assigned columns, I can just do this.
df.assign(
b = 2 * df['a'],
c = 4 * df['a']
)
```
#### Expected Output
a b c
0 1 2 4
1 2 4 8
2 3 6 12
3 4 8 16
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 3.6.2
pip: 18.1
setuptools: 39.2.0
Cython: 0.28.3
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.5
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.4
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.5
lxml: 4.2.2
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.8
pymysql: 0.8.1
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25712 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25712/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25712/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25712/events | https://github.com/pandas-dev/pandas/pull/25712 | 420,607,849 | MDExOlB1bGxSZXF1ZXN0MjYwODUxMjAy | 25,712 | Make Rolling.apply documentation clearer | {
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} | 3 | 2019-03-13T16:49:03Z | 2019-03-14T01:53:47Z | 2019-03-14T01:53:42Z | CONTRIBUTOR | null | Fix #25656 by making Rolling.apply documentation clearer (hope this is what @js3711 intended)
- [x] closes #25656
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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