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} | 2 | "2021-05-11T16:59:10Z" | "2021-05-14T16:09:24Z" | "2021-05-14T16:09:23Z" | NONE | null | #### Location of the documentation
[`pandas.DataFrame.hist`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html)
#### Documentation problem
The introduction to the documentation currently reads:
> Make a histogram of the DataFrame’s.
>
> A histogram is a representation of the distribution of data. This function calls matplotlib.pyplot.hist(), on each series in the DataFrame, resulting in one histogram per column.
As defined in `pandas.plotting._core` [here](https://github.com/pandas-dev/pandas/blob/15dff11b799f665b50e9078ae52dd820ac1bc678/pandas/plotting/_core.py#L135-L141). This first sentence seems incomplete - the DataFrame's _what_?
#### Suggested fix for documentation
The sentence should probably be completed, likely in a manner that matches [`pandas.DataFrame.plot.hist`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hist.html),
the simplest form of which is probably just
> Make a histogram of the DataFrame's **columns**.
which might be a bit small to be its own PR, but could be wrapped together with marking `column` and `figsize` as optional parameters (currently also not reflected in the documentation) | {
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Also this is the first time, I am trying to contribute to a open source, please help me out so that my question/feature request makes sense to all.
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} | 0 | "2021-05-11T19:59:13Z" | "2021-05-12T01:36:06Z" | "2021-05-12T01:08:32Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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https://api.github.com/repos/pandas-dev/pandas/issues/41425 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41425/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41425/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41425/events | https://github.com/pandas-dev/pandas/pull/41425 | 888,488,060 | MDExOlB1bGxSZXF1ZXN0NjQxNjY4ODA2 | 41,425 | BUG: always strip .freq when putting DTI/TDI into Series/DataFrame | {
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} | 2 | "2021-05-11T20:14:32Z" | "2021-05-17T16:35:51Z" | "2021-05-17T16:31:51Z" | MEMBER | null | ATM when we put a DatetimeArray/TimedeltaArray/DatetimeIndex/TimedeltaIndex (from here on, just "DTI") into a Series or DataFrame, we drop its .freq for BlockManager-DataFrame cases, but not the other three cases (BlockManager-Series, ArrayManager-DataFrame, ArrayManager-Series).
The long-term behavior is definitely going to always drop the freq (more specifically, DTA/TDA won't _have_ freq, xref #31218). So this PR standardizes always-dropping.
cc @mroeschke this should unblock your window PR
cc @jorisvandenbossche bc this touches ArrayManager | {
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These fixtures were only specific to the tests in this directory.
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I think we agreed that doing it eagerly (but of course still behind a try/except, since it's an optional dependency) was fine, since most of the import time of pyarrow is due to import numpy, which is something we need to do anyway.
However, at the time that gave circular import problems with old pyarrow versions(https://github.com/pandas-dev/pandas/pull/28371#issuecomment-572032856). Nowadays we require more recent version of pyarrow, and this should no longer be a problem.
(triggered by https://issues.apache.org/jira/browse/ARROW-12732) | {
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} | 3 | "2021-05-12T09:34:33Z" | "2021-05-22T15:09:03Z" | "2021-05-17T17:15:59Z" | CONTRIBUTOR | null | This works but someone with more knowledge might be able to point out a better definition of the type for `Subset`,
i.e. here `Subset = Union[slice, Sequence, Index]`
It types all the `subset` args in `Styler` | {
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} | 4 | "2021-05-12T11:20:36Z" | "2021-06-09T12:17:05Z" | "2021-06-09T12:17:05Z" | CONTRIBUTOR | null | https://pandas.pydata.org/speed/pandas/index.html#series_methods.Clip.time_clip?python=3.8&Cython=0.29.21&p-n=50&commits=29512dc9ea2b066ab5f4a7cb80c1f8f5becef1c2
https://github.com/pandas-dev/pandas/pull/41141/
I don't know if there's any easy way to get this back, but it'd be nice to avoid the slowdown if possible.
cc @Rasori | {
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} | 1 | "2021-05-12T12:37:35Z" | "2021-05-16T06:58:13Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df.columns.name = 'cols'
print(df)
# drops the columns' name 'cols'
print(df.convert_dtypes())
```
#### Problem description
DataFrame.convert_dtypes() drops the columns' name.
This is unexpected.
#### Expected Output
convert_dtypes() should keep the columns' name.
It does so for the index name.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-50-generic
Version : #56~20.04.1-Ubuntu SMP Mon Apr 12 21:46:35 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
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
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pyxlsb : None
s3fs : 0.6.0
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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] | open | false | null | [] | null | 1 | "2021-05-12T14:55:33Z" | "2021-06-06T17:18:21Z" | null | NONE | null | It seems impossible to save a DataFrame as a HDF file when:
- One of the column is named 'index'
- data_columns=['index']
- format='table'
The same also happens using HDFStore + put
```python
>>> import pandas as pd
>>> df = pd.DataFrame({'column_1': [1, 2], 'index': [3, 4]})
>>> df.index.name = 'something_else' # Optionnaly
>>> df.to_hdf(path, 'data', format='table', data_columns=['index'])
...
in write_data(self, chunksize, dropna)
4293 bvalues = []
4294 for i, v in enumerate(values):
-> 4295 new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape
4296 bvalues.append(values[i].reshape(new_shape))
4297
IndexError: tuple index out of range
```
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} | 6 | "2021-05-12T15:08:34Z" | "2021-05-14T20:06:42Z" | "2021-05-14T20:06:42Z" | MEMBER | null | Looks like the format of our team grid is broken when there is only one maintainer in a row, check Patrick here: https://pandas.pydata.org/about/team.html
The website generates invisible cards in the last row so the size stays the same, regardless of the actual (visible) number of cards. But something doesn't seem to be working as expected. | {
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] | open | false | null | [] | null | 0 | "2021-05-12T15:24:31Z" | "2021-05-12T15:24:31Z" | null | MEMBER | null | Related to #28580, I think it should be quite quick, and add a lot of value, if we render the docs using the website header and footer, so the user feels it's always in the same page. It would also give more visibility to the home page (where the sponsor logos are), to the Contribute section (where the donation and other stuff to engage with pandas are), and to everything else in the website navigation.
Being specific on what I'd do:
- I'd remove the logo and the social media icons from the docs bar
- I'd remove the footer of the docs
- I'd change the base template of the sphinx theme, to the base template of the docs, so the website bar is on top of the docs bar in the final result
I think the main challenge is that the base template contains content that comes from the yaml settings, but that can be provided to sphinx in the settings. I think it can be done in an easy and clean way.
@jorisvandenbossche thoughts? | {
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- [x] tests added / passed
tests may be in wrong place.
solution may be the easiest, but it maybe not the best or most robust.
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} | 2 | "2021-05-12T17:47:15Z" | "2021-05-14T09:14:28Z" | "2021-05-14T00:26:37Z" | MEMBER | null | test changes needed in #41372 revealed an issue with the current implementation. fixing for completeness and to add release note.
the dtype for an empty DataFrame from .str.get_dummies looks iffy so marking this as draft until further investigation. | {
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} | 9 | "2021-05-12T20:42:36Z" | "2021-06-01T14:56:11Z" | "2021-05-14T17:13:47Z" | MEMBER | null | closes #41366
Reverts pandas-dev/pandas#41370
Looks like fastparquet released a new version. | {
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} | 6 | "2021-05-12T21:26:53Z" | "2021-05-17T13:18:33Z" | "2021-05-17T13:18:28Z" | CONTRIBUTOR | null | Change index to [0, 2] to stress that ignore_index=True resets the index of both dataframes
- [x] closes #41407
- [x] tests passed
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Test Output:
```
################################################################################
################################## Validation ##################################
################################################################################
1 Errors found:
Return value has no description
```
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} | 1 | "2021-05-12T22:52:11Z" | "2021-05-14T16:05:19Z" | "2021-05-14T16:05:15Z" | MEMBER | null | - [x] closes #39123
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 16 | "2021-05-13T02:33:03Z" | "2021-05-14T21:24:37Z" | "2021-05-14T20:06:42Z" | CONTRIBUTOR | null | - [x] closes #41438
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Screenshot taken on Google Chrome 90.0.4430.93
To reproduce,
- go to https://pandas.pydata.org/about/team.html
- open developer tools
- go to elements tab
- navigate to <div class="row maintainers">
- edit as HTML
- delete <div class="row maintainers"> and the closing tag at the end
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} | 2 | "2021-05-13T03:46:35Z" | "2021-07-02T01:23:08Z" | "2021-07-02T01:23:08Z" | NONE | null | - [Y] I have checked that this issue has not already been reported.
- [Y] I have confirmed this bug exists on the latest version of pandas.
- [N] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
pandas.read_excel('chartsheet.xlsx', sheet_name=None)
```
Example sheet file:
[chartsheet.xlsx](https://github.com/pandas-dev/pandas/files/6470217/chartsheet.xlsx)
#### Problem description
When reading an excel file that contains a chart sheet (a sheet that contains a chart and no table), the following exception was raised:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-89c95e3cb7b1> in <module>
1 import pandas
----> 2 pandas.read_excel('chartsheet.xlsx', sheet_name=None)
D:\Softwares\Anaconda\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
297 )
298 warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
--> 299 return func(*args, **kwargs)
300
301 return wrapper
D:\Softwares\Anaconda\lib\site-packages\pandas\io\excel\_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, storage_options)
365 skipfooter=skipfooter,
366 convert_float=convert_float,
--> 367 mangle_dupe_cols=mangle_dupe_cols,
368 )
369 finally:
D:\Softwares\Anaconda\lib\site-packages\pandas\io\excel\_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, converters, true_values, false_values, skiprows, nrows, na_values, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
1188 convert_float=convert_float,
1189 mangle_dupe_cols=mangle_dupe_cols,
-> 1190 **kwds,
1191 )
1192
D:\Softwares\Anaconda\lib\site-packages\pandas\io\excel\_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, dtype, true_values, false_values, skiprows, nrows, na_values, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
490 sheet = self.get_sheet_by_index(asheetname)
491
--> 492 data = self.get_sheet_data(sheet, convert_float)
493 usecols = maybe_convert_usecols(usecols)
494
D:\Softwares\Anaconda\lib\site-packages\pandas\io\excel\_openpyxl.py in get_sheet_data(self, sheet, convert_float)
546 data: List[List[Scalar]] = []
547 last_row_with_data = -1
--> 548 for row_number, row in enumerate(sheet.rows):
549 converted_row = [self._convert_cell(cell, convert_float) for cell in row]
550 if not all(cell == "" for cell in converted_row):
AttributeError: 'Chartsheet' object has no attribute 'rows'
```
#### Expected Output
I expect that either the chart sheet is ignored, returned as some appropriate object, or a warning is shown that the chart sheet cannot be read, instead of raising the exception.
In my case I'm reading from many zipped archives of excel files, so manually removing the charts from the files is not an option.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : None.None
pandas : 1.2.4
numpy : 1.19.4
pytz : 2019.3
dateutil : 2.8.1
pip : 21.0.1
setuptools : 51.0.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : 1.0.8
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 7 | "2021-05-13T08:11:54Z" | "2021-05-15T07:19:20Z" | "2021-05-15T07:19:20Z" | MEMBER | null | - [ ] xref #16529
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Per the discussion yesterday, I just wanted to clarify that putting in a deprecation warning (such as thing one) in v1.3.0 gives enough time to remove `inplace` in v2.0.0 if, as is likely, there is not another minor version between v1.3.0 and v2.0.0. Will it be OK to change it to a `FutureWarning` in a patch release of v1.3?
If this is OK, then I'm running a little sprint on Saturday for PyLadiesLondon, and putting in more of these warning will make for a good sprint issue | {
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} | 4 | "2021-05-13T09:03:11Z" | "2021-06-10T00:13:28Z" | "2021-06-10T00:13:28Z" | MEMBER | null | Looks like an upgrade broke our doc build.
Successful build:
https://github.com/pandas-dev/pandas/runs/2567593238
Broken build:
https://github.com/pandas-dev/pandas/pull/41444/checks?check_run_id=2573993166
This occurs in more than one pr, so not related to changes in the prs
Full version diff:
```
packages version_now version_previous
70 boto3 1.17.72 1.17.71
71 botocore 1.20.72 1.20.71
86 click 8.0.0 7.1.2
141 gitpython 3.1.16 3.1.15
203 jinja2 3.0.0 2.11.3
327 markupsafe 2.0.0 1.1.1
343 moto 2.0.7 2.0.5
403 pandas 1.3.0.dev0+1607.g0a3a7c7f5d 1.3.0.dev0+1605.g4ec6925c19
591 terminado 0.9.5 0.9.4
661 NaN NaN #
``` | {
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} | 13 | "2021-05-13T10:10:01Z" | "2021-10-04T07:49:58Z" | null | NONE | null | Currently, when saving a DataFrame with an IntervalIndex as a CSV, there is no easy way to parse it again. With this PR, class methods are introduced to handle this:
```python
import tempfile
import numpy as np
import pandas as pd
# Create a DataFrame with an IntervalIndex
df = pd.DataFrame(data=[1, 2, 3], index=pd.IntervalIndex.from_breaks([2., 3., 4., 5.]))
# Create a temporary directory to save the csv in
temp_dir = tempfile.TemporaryDirectory()
df.to_csv(temp_dir.name + 'df.csv')
print(df)
# Read the DataFrame containing the IntervalIndex column
df2 = pd.read_csv(temp_dir.name + 'df.csv')
# Convert the column to an IntervalIndex
df2.index = pd.IntervalIndex.from_strings(df2.iloc[:, 0])
df2.drop(columns=df2.columns[0], inplace=True)
print(df2)
# Validate that the original and parsed indices are the same
assert np.array_equal(df.index, df2.index)
```
```
0
(2.0, 3.0] 1
(3.0, 4.0] 2
(4.0, 5.0] 3
0
(2.0, 3.0] 1
(3.0, 4.0] 2
(4.0, 5.0] 3
```
As can be seen in the tests, the conversion supports each valid dtype of an Interval and raises descriptive exceptions if it fails.
- [X] closes #23595
- [X] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-05-13T13:52:58Z" | "2021-05-13T17:50:16Z" | "2021-05-13T17:48:57Z" | MEMBER | null | - [x] closes #39176
Pinned this a few months ago, checking if new releases fixed the bugs | {
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} | 1 | "2021-05-13T17:34:26Z" | "2021-05-14T16:09:27Z" | "2021-05-14T16:09:24Z" | CONTRIBUTOR | null | Complete sentence and mark column and figsize as optional parameters, following suggested fix.
- [ ] closes #41421
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-05-13T18:28:36Z" | "2021-05-14T09:17:56Z" | "2021-05-13T23:25:14Z" | MEMBER | null | planning to remove the padding code from _wrap_result eventually, but until then we can skip it when we return a integer array from get_dummies
adding tests and benchmarks as precursor to potential changes to _wrap_result #41372
have a working implementation for ArrowStringArray using pyarrow native functions but is slower than object fallback, so am leaving that for a followup.
```
before after ratio
[4ec6925c] [091b0b02]
<master> <get_dummies>
- 2.58±0.02s 655±10ms 0.25 strings.Dummies.time_get_dummies('arrow_string')
- 2.58±0.03s 643±9ms 0.25 strings.Dummies.time_get_dummies('string')
- 2.59±0.07s 638±7ms 0.25 strings.Dummies.time_get_dummies('str')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
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} | 8 | "2021-05-13T19:26:33Z" | "2021-07-06T21:32:52Z" | "2021-07-06T21:32:52Z" | CONTRIBUTOR | null | Bootstrap has a [new stable version v5.0](https://blog.getbootstrap.com/2021/05/05/bootstrap-5/). I think it is a good idea to upgrade the website to use this new version. It no longer uses jQuery (one less dependency) and new features can be utilized by developers to further improve the website.
Please share your thoughts.
related to issue #41438 and pull request #41447 | {
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} | 4 | "2021-05-13T19:59:06Z" | "2021-05-14T16:26:06Z" | "2021-05-14T16:09:46Z" | MEMBER | null | - [x] closes #39328
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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#### Code Sample, a copy-pastable example
```python
from datetime import date, datetime
import pandas as pd
df = pd.DataFrame({"date": pd.date_range("2021-01-01", "2021-01-02")})
my_datetime = datetime(2021, 1, 1)
my_date_str = "2021-01-01"
my_date = date(2021, 1, 1)
print(df[df["date"] == my_datetime])
print(df[df["date"] > my_datetime])
print(df[df["date"] == my_date_str])
print(df[df["date"] > my_date_str])
print(df[df["date"] == my_date])
print(df[df["date"] > my_date])
```
#### Output
```
date
0 2021-01-01
date
1 2021-01-02
date
0 2021-01-01
date
1 2021-01-02
Empty DataFrame
Columns: [date]
Index: []
---------------------------------------------------------------------------
InvalidComparison Traceback (most recent call last)
```
#### Rest of traceback:
<details>
```
/opt/venv/lib/python3.8/site-packages/pandas/core/arrays/datetimelike.py in _cmp_method(self, other, op)
931 try:
--> 932 other = self._validate_comparison_value(other)
933 except InvalidComparison:
/opt/venv/lib/python3.8/site-packages/pandas/core/arrays/datetimelike.py in _validate_comparison_value(self, other)
450 elif not is_list_like(other):
--> 451 raise InvalidComparison(other)
452
InvalidComparison: 2021-01-01
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/usr/src/app/notebooks/005-whirlpool/06-get-fratecaster.py in
409 print(df[df["date"] > my_date_str])
410 print(df[df["date"] == my_date])
---> 411 print(df[df["date"] > my_date])
412
/opt/venv/lib/python3.8/site-packages/pandas/core/ops/common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
/opt/venv/lib/python3.8/site-packages/pandas/core/arraylike.py in __gt__(self, other)
43 @unpack_zerodim_and_defer("__gt__")
44 def __gt__(self, other):
---> 45 return self._cmp_method(other, operator.gt)
46
47 @unpack_zerodim_and_defer("__ge__")
/opt/venv/lib/python3.8/site-packages/pandas/core/series.py in _cmp_method(self, other, op)
4976 rvalues = extract_array(other, extract_numpy=True)
4977
-> 4978 res_values = ops.comparison_op(lvalues, rvalues, op)
4979
4980 return self._construct_result(res_values, name=res_name)
/opt/venv/lib/python3.8/site-packages/pandas/core/ops/array_ops.py in comparison_op(left, right, op)
227 if should_extension_dispatch(lvalues, rvalues):
228 # Call the method on lvalues
--> 229 res_values = op(lvalues, rvalues)
230
231 elif is_scalar(rvalues) and isna(rvalues):
/opt/venv/lib/python3.8/site-packages/pandas/core/ops/common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
/opt/venv/lib/python3.8/site-packages/pandas/core/arraylike.py in __gt__(self, other)
43 @unpack_zerodim_and_defer("__gt__")
44 def __gt__(self, other):
---> 45 return self._cmp_method(other, operator.gt)
46
47 @unpack_zerodim_and_defer("__ge__")
/opt/venv/lib/python3.8/site-packages/pandas/core/arrays/datetimelike.py in _cmp_method(self, other, op)
932 other = self._validate_comparison_value(other)
933 except InvalidComparison:
--> 934 return invalid_comparison(self, other, op)
935
936 dtype = getattr(other, "dtype", None)
/opt/venv/lib/python3.8/site-packages/pandas/core/ops/invalid.py in invalid_comparison(left, right, op)
32 else:
33 typ = type(right).__name__
---> 34 raise TypeError(f"Invalid comparison between dtype={left.dtype} and {typ}")
35 return res_values
36
TypeError: Invalid comparison between dtype=datetime64[ns] and date
```
</details>
#### Problem description
It seems reasonable to expect that if a pandas datetime column can be compared to a date string, that it should be able to be compared to a date object with exactly the same information. I see [this recent PR](https://github.com/pandas-dev/pandas/pull/36131) which if I'm reading it right made it so that a pandas Timestamp won't EQUAL a date object to match stdlib behavior, which I'm fine with...users should expect equality to be really picky. However, if the goal is to match stdlib behavior, we probably shouldn't allow equality between pandas Timestamps and strings either. All that aside, my main point is that if greater than/less than comparisons are allowed between Timestamps and date strings, they should be allowed between Timestamps and date objects.
#### Expected Output
```
date
0 2021-01-01
date
1 2021-01-02
date
0 2021-01-01
date
1 2021-01-02
Empty DataFrame
Columns: [date]
Index: []
date
1 2021-01-02
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.24.1.el7.x86_64
Version : #1 SMP Thu Mar 25 21:21:56 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 47.1.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.15
tables : None
tabulate : 0.8.9
xarray : 0.18.0
xlrd : None
xlwt : None
numba : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41466 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41466/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41466/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41466/events | https://github.com/pandas-dev/pandas/issues/41466 | 891,572,933 | MDU6SXNzdWU4OTE1NzI5MzM= | 41,466 | BUG: `assert_series_equal` raises when series contains `dict` of `np.array`s | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
s = pd.Series([{'a': np.array(['xx', 'yy'])}])
s2 = pd.Series([{'a': np.array(['xx', 'yy'])}])
pd.testing.assert_series_equal(s, s2)
```
#### Problem description
Raises:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-94-c9299c384117> in <module>
2 s = pd.Series([{'a': np.array(['xx', 'yy'])}])
3 s2 = pd.Series([{'a': np.array(['xx', 'yy'])}])
----> 4 pd.testing.assert_series_equal(s, s2)
~/compose/etc/conda/cuda_11.2/envs/notebooks/lib/python3.7/site-packages/pandas/_testing.py in assert_series_equal(left, right, check_dtype, check_index_type, check_series_type, check_less_precise, check_names, check_exact, check_datetimelike_compat, check_categorical, check_category_order, check_freq, check_flags, rtol, atol, obj)
1482 check_dtype=check_dtype,
1483 obj=str(obj),
-> 1484 index_values=np.asarray(left.index),
1485 )
1486
pandas/_libs/testing.pyx in pandas._libs.testing.assert_almost_equal()
pandas/_libs/testing.pyx in pandas._libs.testing.assert_almost_equal()
~/compose/etc/conda/cuda_11.2/envs/notebooks/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in array_equivalent(left, right, strict_nan, dtype_equal)
427 # string dtypes must be come to this path for NumPy 1.7.1 compat
428 if is_string_dtype(left.dtype) or is_string_dtype(right.dtype):
--> 429 return _array_equivalent_object(left, right, strict_nan)
430
431 # NaNs can occur in float and complex arrays.
~/compose/etc/conda/cuda_11.2/envs/notebooks/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _array_equivalent_object(left, right, strict_nan)
482 else:
483 try:
--> 484 if np.any(np.asarray(left_value != right_value)):
485 return False
486 except TypeError as err:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
#### Expected Output
`True`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.12.0
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.3.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41467 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41467/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41467/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41467/events | https://github.com/pandas-dev/pandas/issues/41467 | 891,681,162 | MDU6SXNzdWU4OTE2ODExNjI= | 41,467 | BUG: random warning about dataframe.sub() | {
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- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
x file: https://github.com/jiahe224/bug_report/blob/909726d7693af1c1b34a1f6c0604b768c845bf99/x.csv
y file: https://github.com/jiahe224/bug_report/blob/909726d7693af1c1b34a1f6c0604b768c845bf99/y.csv
```python
# Your code here
x = pd.read_csv('x.csv')
y = pd.read_csv('y.csv')
x = x.set_index(['年份','133行业名称'])
y = y.set_index(['年份','133行业名称'])
x.sub(y)
```
#### Problem description
A warning will appear randomly, the warning message is as follows.
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py:3369: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.
uniq_tuples = lib.fast_unique_multiple([self._values, other._values], sort=sort)
x.sub(y) Runs correctly occasionally：
https://github.com/jiahe224/bug_report/blob/6b500dfd729b4772c9c2ee57cff63eaee9393628/1.png
But most of the time it will warning：
https://github.com/jiahe224/bug_report/blob/6b500dfd729b4772c9c2ee57cff63eaee9393628/2.png
x.shape is (501, 2)
y.shape is (500, 2)
But after testing, different index does not affect the normal operation, eg:
`df_test1 = pd.DataFrame({'col1':[1,2,3,4]},index=[list('xxxx'),list('1111'),list('opqn')])`
`df_test2 = pd.DataFrame({'col1':[1,2,3]},index=[list('xxx'),list('111'),list('opq')])`
`df_test1.sub(df_test2)`
The following runs are correct without warning：
`x.loc['2017'].sub(y.loc['2017']) # '2018', '2019', '2020' are all correct`
so, Why does it work correctly after slicing, but a warning appears when running as a whole?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Chinese (Simplified)_China.936
pandas : 1.1.4
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.53.0
</details>
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] | null | 10 | "2021-05-14T09:11:29Z" | "2021-08-20T05:26:27Z" | "2021-08-20T05:26:27Z" | NONE | null | Same as stackoverflow question:
https://stackoverflow.com/questions/48867875/slow-csv-writing-with-python3-compared-to-python2-with-pandas-to-csv
I found that pandas to_csv is much slower in my python 3 venv compared to python 2. I did a test to write a 7MB to c drive 1000 times and measure the average time used. I found that python 2 venv only use half of the time compared to python 3.
Versions:
python 2: 2.7.9
pandas: 0.19.2
python 3: 3.6.8
pandas: 0.25.1
Does this performance difference between python 2 and 3 expected?
[py3_8_profile.txt](https://github.com/pandas-dev/pandas/files/6486534/py3_8_profile.txt)
[py3_6_profile.txt](https://github.com/pandas-dev/pandas/files/6486536/py3_6_profile.txt)
[py2_7_profile.txt](https://github.com/pandas-dev/pandas/files/6486540/py2_7_profile.txt)
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} | 5 | "2021-05-14T14:58:28Z" | "2021-05-18T13:56:07Z" | "2021-05-18T13:05:52Z" | CONTRIBUTOR | null | Seperates the refactoring of existing methods out of #41153. Will make it easier to see what #41153 actually brings in of new things.
This PR changes no functionality, but only makes the exisiting numeric indexes into thin subclasses of the existing `NumericIndex`. | {
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} | 1 | "2021-05-14T15:48:45Z" | "2021-05-17T15:56:23Z" | "2021-05-17T15:31:31Z" | MEMBER | null | separate commits to simplify review (we may be able to remove pandas/tests/strings/test_string_array.py eventually) | {
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} | 1 | "2021-05-14T16:04:44Z" | "2021-05-14T17:14:48Z" | "2021-05-14T17:11:30Z" | MEMBER | null | no changes, only moved. maybe could split pandas/tests/strings/test_strings.py by method | {
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} | 0 | "2021-05-14T16:52:37Z" | "2021-05-26T04:37:57Z" | "2021-05-26T01:53:59Z" | MEMBER | null | - [x] closes #21664
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Discussed on this week's call. | {
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] | open | false | null | [] | null | 2 | "2021-05-14T17:37:55Z" | "2021-08-20T05:27:17Z" | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
From https://replit.com/@tlaytongoogle/DFGroupbyBadIndex:
```
import pandas as pd
class Wrapper:
def __init__(self, value):
self.value = value
df = pd.DataFrame({'key': ['a', 'b']}, index=['foo', 'bar'])
print('df:\n' + str(df))
def f(group_df):
print('group_df:\n' + str(group_df)) # Correct
return Wrapper(group_df.index)
y = df.groupby('key').apply(f)
print('Value within `a`:\n' + str(y.loc['a'].value)) # Correct
print('Value within `b`:\n' + str(y.loc['b'].value)) # Ought to be `pd.Index(['foo'])``
```
#### Problem description
When `DataFrame.groupby.apply` is given a function that returns an object, which in turn has a field containing an `Index`, the resulting `Series` of objects is incorrect in a specific way: the objects' fields all contain the same `Index`, which only one of those objects ought to contain, instead of each containing the correct `Index` as it appeared in the return value of the function.
#### Expected Output
See reproduction above. Note that the `Index` values are all correct as printed from within the applied function, but not as later printed from the returned `Series`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1043-gcp
Version : #46-Ubuntu SMP Mon Apr 19 19:17:04 UTC 2021
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 : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 19.3.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-05-14T20:25:23Z" | "2021-11-13T19:32:56Z" | "2021-05-24T10:46:22Z" | MEMBER | null | cc @jreback We could avoid the version changes in the pre commits if you prefer | {
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} | 1 | "2021-05-14T23:13:21Z" | "2021-05-17T16:09:49Z" | "2021-05-17T15:19:46Z" | CONTRIBUTOR | null | Separating various stuff from #41153, making that PR easier to review after this is merged, Similar idea as #41472.
Nothing in this PR changes anything, but just makes it e.g. simpler to update test fixtures after this is merged. | {
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} | 2 | "2021-05-15T01:37:10Z" | "2021-05-21T21:18:11Z" | "2021-05-21T20:59:56Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Discussed on this week's call | {
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} | 1 | "2021-05-15T03:18:46Z" | "2021-05-17T16:16:35Z" | "2021-05-17T15:18:37Z" | MEMBER | null | - [x] xref #37535
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-05-15T05:07:17Z" | "2021-05-17T16:17:57Z" | "2021-05-17T15:17:30Z" | MEMBER | null | - [x] closes #13427
- [x] closes #13432
- [x] closes #13501
- [x] closes #13569
- [x] closes #13872
- [x] closes #13912
- [x] closes #13934
- [x] closes #14018
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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df.shape
(96554023, 16)
df.to_pickle('data/test_sample.pkl')
Description：
df.memory_usage(deep=True)
Index 772432184
id 772432184
request_timestamp 772432184
position 772432184
uid 772432184
aid 772432184
imp_ad_size 772432184
bid 772432184
pctr 772432184
quality_ecpm 772432184
totalEcpm 772432184
request_day 772432184
wday 772432184
hour 772432184
minute 772432184
period_id 772432184
imp 772432184
dtype: int64
The entire dataframe can be completely loaded into memory
error:
But when the dataframe is written to the hard disk through df.to_pickle(path), the memory required for the entire process exceeds 64G. I executed the (os:linux)top command in real time. After the memory reached 98%, The entire process is terminated by the operating system
**df.to_pickle('data/test_sample.pkl')** The entire process caused by this line is terminated
memory:64G
```
python3.6
absl-py 0.12.0
astor 0.8.1
boto3 1.17.72
botocore 1.20.72
cached-property 1.5.2
certifi 2020.12.5
chardet 4.0.0
click 8.0.0
dataclasses 0.8
filelock 3.0.12
future 0.18.2
gast 0.4.0
gensim 3.6.0
grpcio 1.37.1
h5py 3.1.0
idna 2.10
importlib-metadata 4.0.1
jmespath 0.10.0
joblib 1.0.1
Keras 2.2.4
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
Markdown 3.3.4
numpy 1.15.4
pandas 1.1.5
pip 21.1.1
protobuf 3.17.0
python-dateutil 2.8.1
pytz 2021.1
PyYAML 5.4.1
regex 2021.4.4
requests 2.25.1
s3transfer 0.4.2
sacremoses 0.0.45
scikit-learn 0.24.2
scipy 1.2.1
sentencepiece 0.1.95
setuptools 49.6.0.post20210108
six 1.16.0
sklearn 0.0
smart-open 5.0.0
termcolor 1.1.0
threadpoolctl 2.1.0
tokenizers 0.5.2
tqdm 4.60.0
transformers 2.8.0
typing-extensions 3.10.0.0
urllib3 1.26.4
Werkzeug 2.0.0
wheel 0.36.2
zipp 3.4.1
```
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} | 35 | "2021-05-15T10:06:09Z" | "2021-06-08T15:37:13Z" | "2021-06-08T15:37:13Z" | MEMBER | null | ## Background
Having many parameters with defaults makes static typing a lot harder when one of them changes the return type, as they require many* overloads.
There's still no broad agreement about whether or not to deprecate the `inplace` argument, so in the meanwhile, if we force default-arguments to be keyword-only, then we can at least set the correct return types for these methods without requiring all these overloads (as many as 38 in one case!).
## What to do
1. Choose a method from the following list:
- [ ] pandas/core/computation/eval.py:164:0, eval (skip for now)
- [x] pandas/core/frame.py:4718:4, set_axis
- [x] pandas/core/frame.py:4743:4, drop
- [x] pandas/core/frame.py:5134:4, fillna
- [x] pandas/core/frame.py:5306:4, set_index
- [x] pandas/core/frame.py:5572:4, reset_index
- [x] pandas/core/frame.py:5809:4, dropna
- [x] pandas/core/frame.py:5958:4, drop_duplicates
- [x] pandas/core/frame.py:6196:4, sort_values
- [x] pandas/core/frame.py:6267:4, sort_index
- [x] pandas/core/generic.py:6432:4, ffill
- [x] pandas/core/generic.py:6495:4, bfill
- [x] pandas/core/generic.py:6700:4, interpolate
- [x] pandas/core/generic.py:7473:4, clip
- [x] pandas/core/generic.py:9080:4, where
- [x] pandas/core/generic.py:9234:4, mask
- [x] pandas/core/indexes/base.py:1530:4, set_names
- [x] pandas/core/indexes/multi.py:810:4, set_levels
- [x] pandas/core/indexes/multi.py:997:4, set_codes
- [x] pandas/core/resample.py:831:4, interpolate
- [x] pandas/core/series.py:1279:4, reset_index
- [x] pandas/core/series.py:2028:4, drop_duplicates
- [x] pandas/core/series.py:3228:4, sort_values
- [x] pandas/core/series.py:3438:4, sort_index
- [x] pandas/core/series.py:4475:4, set_axis
- [x] pandas/core/series.py:4490:4, drop
- [x] pandas/core/series.py:4713:4, fillna
- [x] pandas/core/series.py:5064:4, dropna
- [x] read_csv
- [x] read_table
- [x] concat
2. Use `pandas.util._decorators.deprecate_nonkeyword_arguments` to issue a FutureWarning that the method's default arguments will be keyword-only in the next release.
3. Fixup any tests / internal code which uses such default arguments positionally. See [this gist](https://gist.github.com/MarcoGorelli/1e8fb9d4cfb24353b0022f03150651a0) for a helper-script which can help identify cases which need fixing, just replace `"drop"` with whatever method you're working on and then run it as `python visitor.py`.
4. Add a new test, checking that a FutureWarning is raised if this method's default arguments are used positionally.
5. Add a note to `doc/source/whatsnew/v1.3.0.rst`
See https://github.com/pandas-dev/pandas/pull/41511 for an example of how to do this.
In most of these methods, the first argument is probably fine to be kept as is, it's the ones after that (such as `axis`) which should become keyword-only.
----
*(2^{number of default types preceding the one to overload} +2)
----
It's really common to call `replace` with both `value` and `to_replace` as positional, so I think we can make an exception for that one. It'll only require 6 overloads, which I'd argue isn't too many, especially if no other method requires more than 3 or 4
---
TODO: unify the whatsnew entries | {
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} | 3 | "2021-05-15T10:10:55Z" | "2021-05-27T12:26:11Z" | "2021-05-27T12:07:18Z" | MEMBER | null | - [ ] xref #41485
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-05-15T11:17:40Z" | "2021-05-17T15:53:18Z" | "2021-05-17T15:08:21Z" | MEMBER | null | using pyarrow native functions for _str_startswith/_str_endswith #41222 were merged before _str_contains #41217 so we can de-duplication some logic.
and also a change to _str_match for perf gain on object fallback
```
- 32.8±0.2ms 25.5±0.1ms 0.78 strings.Methods.time_match('arrow_string')
``` | {
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} | 8 | "2021-05-15T15:20:04Z" | "2021-07-28T01:50:22Z" | "2021-07-28T01:50:05Z" | CONTRIBUTOR | null | - [x] closes #41356 #14869
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
-----------------
**Problem description**: _CustomBusinessMonth does not apply extra offset when initially rolled month begin `new` is already a business day. This happens because we currently use CustomBusinessDay.rollforward (rollback) to both 1) roll to a business day and 2) apply the extra offset.
**Fix**: Separating the logic to 1) roll to a business day and 2) apply the extra offset. | {
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} | 1 | "2021-05-15T16:55:01Z" | "2021-05-16T10:49:44Z" | "2021-05-16T09:10:28Z" | CONTRIBUTOR | null | Added https://github.com/pre-commit/pre-commit-hooks#debug-statements as a pre-commit hook.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-05-15T17:15:34Z" | "2021-05-18T08:35:05Z" | "2021-05-17T20:50:32Z" | MEMBER | null | ```
before after ratio
[40482273] [1788ffe6]
<master> <extract-single-group>
- 134±2ms 106±1ms 0.79 strings.Extract.time_extract_single_group('string', False)
- 359±5ms 110±0.8ms 0.31 strings.Extract.time_extract_single_group('arrow_string', False)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
``` | {
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} | 1 | "2021-05-15T18:17:20Z" | "2021-05-27T12:23:32Z" | "2021-05-27T12:08:38Z" | MEMBER | null | - [ ] xref #41485
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-05-15T18:45:53Z" | "2021-06-04T14:40:53Z" | "2021-06-04T14:40:53Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
All installed plotting backends are loaded on the first `DataFrame.plot` call of a kernel, resulting in a potentially unnecessary module loading time, although quite minor, as it's only done on the very first call. However, if the number of pluggable backends out there increases in the future, this could become a real nuisance for users who installed many of them in their environment.
#### Describe the solution you'd like
In `pandas.plotting._core._find_backend`, `entry_point.load()` should only be done for the solicited backend. This is straightforward to implement, I could write the PR myself.
#### API breaking implications
None as far as I know.
#### Describe alternatives you've considered
#### Additional context
With just hvPlot installed, the following
```python
for entry_point in pkg_resources.iter_entry_points("pandas_plotting_backends"):
if entry_point.name == "matplotlib":
# matplotlib is an optional dependency. When
# missing, this would raise.
continue
entry_point.load()
```
takes ~2s , while
```python
for entry_point in pkg_resources.iter_entry_points("pandas_plotting_backends"):
if entry_point.name == "matplotlib":
# matplotlib is an optional dependency. When
# missing, this would raise.
continue
```
takes a few ms. As one would expect, `entry_point.load()` is the slowest part to execute by far. | {
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} | 3 | "2021-05-15T19:22:14Z" | "2021-05-19T17:25:38Z" | "2021-05-19T12:53:59Z" | MEMBER | null | - [x] closes #14467
- [x] closes #14564
- [x] closes #14773
- [x] closes #15036
- [x] closes #15106
- [x] closes #15239
- [x] closes #15273
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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] | closed | false | null | [] | null | 2 | "2021-05-15T19:30:26Z" | "2021-05-17T20:51:12Z" | "2021-05-17T20:51:12Z" | MEMBER | null | `fillna` doesn't allow users to pass both `value` and `method`, so in effect the arguments after `value` are already keyword-only. So, the overloads can be simplified immediately.
```python
import pandas as pd
inplace : bool
reveal_type(pd.Series([1,2,3]).fillna(value=0, inplace=False))
reveal_type(pd.Series([1,2,3]).fillna(method='pad', inplace=False))
reveal_type(pd.Series([1,2,3]).fillna(value=0, axis=0, inplace=False))
reveal_type(pd.Series([1,2,3]).fillna(method='pad', axis=0, inplace=False))
reveal_type(pd.Series([1,2,3]).fillna(value=0, inplace=True))
reveal_type(pd.Series([1,2,3]).fillna(method='pad', inplace=True))
reveal_type(pd.Series([1,2,3]).fillna(value=0, axis=0, inplace=True))
reveal_type(pd.Series([1,2,3]).fillna(method='pad', axis=0, inplace=True))
reveal_type(pd.Series([1,2,3]).fillna(value=0, inplace=inplace))
reveal_type(pd.Series([1,2,3]).fillna(method='pad', inplace=inplace))
reveal_type(pd.Series([1,2,3]).fillna(value=0, axis=0, inplace=inplace))
reveal_type(pd.Series([1,2,3]).fillna(method='pad', axis=0, inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).fillna(value=0, inplace=False))
reveal_type(pd.DataFrame([1,2,3]).fillna(method='pad', inplace=False))
reveal_type(pd.DataFrame([1,2,3]).fillna(value=0, axis=0, inplace=False))
reveal_type(pd.DataFrame([1,2,3]).fillna(method='pad', axis=0, inplace=False))
reveal_type(pd.DataFrame([1,2,3]).fillna(value=0, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).fillna(method='pad', inplace=True))
reveal_type(pd.DataFrame([1,2,3]).fillna(value=0, axis=0, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).fillna(method='pad', axis=0, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).fillna(value=0, inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).fillna(method='pad', inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).fillna(value=0, axis=0, inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).fillna(method='pad', axis=0, inplace=inplace))
```
still type checks correctly:
```
$ mypy t.py
t.py:5: note: Revealed type is 'pandas.core.series.Series'
t.py:6: note: Revealed type is 'pandas.core.series.Series'
t.py:7: note: Revealed type is 'pandas.core.series.Series'
t.py:8: note: Revealed type is 'pandas.core.series.Series'
t.py:10: note: Revealed type is 'None'
t.py:11: note: Revealed type is 'None'
t.py:12: note: Revealed type is 'None'
t.py:13: note: Revealed type is 'None'
t.py:15: note: Revealed type is 'Union[pandas.core.series.Series, None]'
t.py:16: note: Revealed type is 'Union[pandas.core.series.Series, None]'
t.py:17: note: Revealed type is 'Union[pandas.core.series.Series, None]'
t.py:18: note: Revealed type is 'Union[pandas.core.series.Series, None]'
t.py:21: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:22: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:23: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:24: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:26: note: Revealed type is 'None'
t.py:27: note: Revealed type is 'None'
t.py:28: note: Revealed type is 'None'
t.py:29: note: Revealed type is 'None'
t.py:31: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:32: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:33: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:34: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
``` | {
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} | 0 | "2021-05-15T19:54:08Z" | "2021-05-26T14:50:31Z" | "2021-05-26T12:40:03Z" | MEMBER | null | - [ ] xref #41485
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [ ] tests added / passed
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-05-16T00:31:48Z" | "2021-05-17T13:27:59Z" | "2021-05-17T12:52:27Z" | MEMBER | null | - [x] closes #38278
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This was fixed way back in #38744 and looks thoroughly tested in #41387, so just a whatsnew here.
I think the other string types could also be supported with tweaks to `ea_wrap_cython_operation` to pull object data out of those types if we want to support those too. | {
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- [ ] tests added / passed
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- [ ] tests added / passed
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- [ ] tests added / passed
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before after ratio
[40482273] [5d1bc3e0]
<master> <wrap-result>
- 59.6±0.5ms 36.2±0.6ms 0.61 strings.Methods.time_partition('arrow_string')
- 57.8±0.5ms 34.9±0.2ms 0.60 strings.Methods.time_rpartition('arrow_string')
- 54.5±0.3ms 32.0±0.4ms 0.59 strings.Methods.time_partition('str')
- 53.3±0.2ms 30.9±0.5ms 0.58 strings.Methods.time_rpartition('str')
- 49.0±0.5ms 26.4±0.3ms 0.54 strings.Methods.time_partition('string')
- 48.2±0.2ms 25.7±0.4ms 0.53 strings.Methods.time_rpartition('string')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
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} | 1 | "2021-05-16T14:17:32Z" | "2021-05-22T06:28:03Z" | "2021-05-21T16:20:32Z" | CONTRIBUTOR | null | This PR closes #41142.
It adds a new `pandas.options` context for Styler (see #41395), and create the `sparse.index` and `sparse.columns` options, defaulting to True.
It also allows a keyword application for sparsification via the `.render` method, (this might not really be necessary: opinion welcome)
- [x] tests added for kwarg application and option context
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} | 4 | "2021-05-16T23:07:46Z" | "2021-05-21T16:12:09Z" | "2021-05-21T16:12:05Z" | CONTRIBUTOR | null | - [ ] closes #41423
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Added to Series section more info in `copy` constructor parameter and 2 examples.
Ran validation script:
```
python scripts/validate_docstrings.py pandas.Series
```
Output:
```
################################################################################
########################## Docstring (pandas.Series) ##########################
################################################################################
One-dimensional ndarray with axis labels (including time series).
Labels need not be unique but must be a hashable type. The object
supports both integer- and label-based indexing and provides a host of
methods for performing operations involving the index. Statistical
methods from ndarray have been overridden to automatically exclude
missing data (currently represented as NaN).
Operations between Series (+, -, /, *, **) align values based on their
associated index values-- they need not be the same length. The result
index will be the sorted union of the two indexes.
Parameters
----------
data : array-like, Iterable, dict, or scalar value
Contains data stored in Series. If data is a dict, argument order is
maintained.
index : array-like or Index (1d)
Values must be hashable and have the same length as `data`.
Non-unique index values are allowed. Will default to
RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
and index is None, then the keys in the data are used as the index. If the
index is not None, the resulting Series is reindexed with the index values.
dtype : str, numpy.dtype, or ExtensionDtype, optional
Data type for the output Series. If not specified, this will be
inferred from `data`.
See the :ref:`user guide <basics.dtypes>` for more usages.
name : str, optional
The name to give to the Series.
copy : bool, default False
Copy input data. If False and the Series returns a `copy`
of the data, the memory location for the values is not shared.
If False and the Series returns a `view` on the data,
the memory location for the values is shared.
Examples
--------
Constructing Series from a dictionary with an Index specified
>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> ser = pd.Series(data=d, index=['a', 'b', 'c'])
>>> ser
a 1
b 2
c 3
dtype: int64
The keys of the dictionary match with the Index values, hence the Index
values have no effect.
>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> ser = pd.Series(data=d, index=['x', 'y', 'z'])
>>> ser
x NaN
y NaN
z NaN
dtype: float64
Note that the Index is first build with the keys from the dictionary.
After this the Series is reindexed with the given Index values, hence we
get all NaN as a result.
Constructing Series from an array with `copy=False`.
>>> r = [1,2]
>>> ser = pd.Series(r, copy=False)
>>> ser.iloc[0] = 999
>>> r
[1, 2]
>>> ser
0 999
1 2
dtype: int64
The Series returns a `copy` of the original data, so
the original data is unchanged.
Constructing Series from a `numpy.array` with `copy=False`.
>>> r = np.array([1,2])
>>> ser = pd.Series(r, copy=False)
>>> ser.iloc[0] = 999
>>> r
array([999, 2])
>>> ser
0 999
1 2
dtype: int32
The Series returns a `view` on the original data, so
the original data is changed as well.
################################################################################
################################## Validation ##################################
################################################################################
3 Errors found:
Parameters {'fastpath'} not documented
See Also section not found
flake8 error: E902 PermissionError: [Errno 13] Permission denied: 'C:\\Users\\mdhsi\\AppData\\Local\\Temp\\tmpxz50buet'
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```python
In [4]: df = pd.DataFrame({'date': pd.date_range('2021-05-16',periods=5)})
In [5]: df
Out[5]:
date
0 2021-05-16
1 2021-05-17
2 2021-05-18
3 2021-05-19
4 2021-05-20
In [6]: df['date'] - pd.Timedelta('12 m')
Out[6]:
0 2021-05-15 23:48:00
1 2021-05-16 23:48:00
2 2021-05-17 23:48:00
3 2021-05-18 23:48:00
4 2021-05-19 23:48:00
Name: date, dtype: datetime64[ns]
In [7]: df['date'] - pd.Timedelta('12 M') # Pandas gives same result for m and M
Out[7]:
0 2021-05-15 23:48:00
1 2021-05-16 23:48:00
2 2021-05-17 23:48:00
3 2021-05-18 23:48:00
4 2021-05-19 23:48:00
Name: date, dtype: datetime64[ns]
In [8]: df['date'] - np.timedelta64(12,'m')
Out[8]:
0 2021-05-15 23:48:00
1 2021-05-16 23:48:00
2 2021-05-17 23:48:00
3 2021-05-18 23:48:00
4 2021-05-19 23:48:00
Name: date, dtype: datetime64[ns]
In [9]: df['date'] - np.timedelta64(12,'M') # numpy gives DIFFERENT result for m and M
Out[9]:
0 2020-05-15 18:10:48
1 2020-05-16 18:10:48
2 2020-05-17 18:10:48
3 2020-05-18 18:10:48
4 2020-05-19 18:10:48
Name: date, dtype: datetime64[ns]
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- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
I have a very large CSV file, and my memory can't read the whole CSV at all. How can I get the first 100 lines of this CSV without reading all the data？
```python
# Your code here, if applicable
```
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] | open | false | null | [] | null | 1 | "2021-05-17T08:47:58Z" | "2021-08-20T05:27:58Z" | null | NONE | null | Hello Everyone, I searched a possible answer for my question without success.
Suppose I start my python script in the root directory of this structure tree:
```
.
├── Dockerfile
├── files
├── scripts
```
In this script I read a csv from files, with a `read_csv('files/filename.csv')`
When I try to export a modified dataframe with `to_json('files/export.zip', compression='zip')`, I get a zipped file with the correct name, but when unzipping, I get a folder named `files`, and the json inside.
How can I export the json file in zip without having the root `files` directory listed ?
*Note: This is a related problem below, but I was hoping a builtin solution with `to_json()`*
https://unix.stackexchange.com/questions/182032/zip-the-contents-of-a-folder-without-including-the-folder-itself
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} | 4 | "2021-05-17T08:50:19Z" | "2021-05-19T23:33:02Z" | null | NONE | null | Hi,
I was recently updating my pandas from 0.24.2 to 1.1.5 and future warning informed me that default value for sort has changed from None to False. It said that to present current behavior I should change it to sort=True. After i updated my package, I checked if sort=None is still accessible and it is, while docs says it accepts only bool.
May I understand that I can still use sort=None to present old behavior? Or should it be depreciated and raise Error?
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} | 2 | "2021-05-17T14:29:06Z" | "2021-05-17T19:03:11Z" | "2021-05-17T19:00:07Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Recent-ish edits to the `convert` function make the fallback unnecessary. | {
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