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`vol` is undocumented and was just `std` which is more standard in our API | {
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} | 1 | 2021-01-17T11:03:16Z | 2021-01-17T11:33:44Z | 2021-01-17T11:33:31Z | NONE | null | I am working with a time series dataframe, and I would like to know how to get a number of rows before, and after the condition is met.
I have a datetime index but also trying a numbered index for ease of use, however, i am not sure how to do either...
The output I want is as follows:
7 rows(or days/hours/minutes) before, then the condition row, and 7 rows(or days,etc...) after.
Any help is massively appreciated! Thank you.
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I realize using this function to compute distances is sort of a non-standard usage but is there any reason to hardcode it? Would it make more sense as a parameter that could be set? | {
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] | closed | false | null | [] | null | 1 | 2021-01-17T11:50:41Z | 2021-01-17T12:01:56Z | 2021-01-17T12:01:43Z | NONE | null | #### Is your feature request related to a problem?
I often want to use the corr function to also compute distances between columns (e.g. df.corr(method = spicy.spatial.distance.euclidean). However, I noticed that the .corr function always sets the i=j case to 1 (which is incorrect for the distance case) (see here).
#### Describe the solution you'd like
I realize using this function to compute distances is sort of a non-standard usage but is there any reason to hardcode it? Would it make more sense as a parameter that could be set?
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} | 10 | 2021-01-17T12:16:27Z | 2021-01-17T14:18:41Z | 2021-01-17T14:17:38Z | MEMBER | null | This reverts commit 3bd3d1ec
- [x] xref #39205 can close this now probably
- [x] closes #39089
- [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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- [ ] 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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} | 0 | 2021-01-17T13:51:28Z | 2021-01-26T00:01:50Z | 2021-01-26T00:01:50Z | MEMBER | null | Failure is only on Series and only when the index is entirely `NaT`.
```
freq = "D"
df = DataFrame([1, 2], index=PeriodIndex([NaT, NaT], freq=freq))
print(df.resample(freq).sum())
ser = Series([1, 2], index=PeriodIndex([NaT, NaT], freq=freq))
print(ser.resample(freq).sum())
```
Second raises, first returns the expected
```
Empty DataFrame
Columns: []
Index: []
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- [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 | 2021-01-17T14:37:26Z | 2021-01-26T15:00:28Z | 2021-01-26T00:01:50Z | MEMBER | null | - [x] closes #39227
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- [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 will also resolve the core issue of https://github.com/pandas-dev/pandas/pull/34998#discussion_r556463043. cc @jorisvandenbossche
The added test:
```
s = series.copy()
s.index = PeriodIndex([NaT] * len(s), freq=freq)
result = getattr(s.resample(freq), resample_method)()
```
currently fails for `freq="M"`, where upsample is used instead of downsample leading to failure since the index is not unique. I can't tell if this is a bug or an operation that should raise. It also fails for `DataFrame`, so I think is unrelated to the issue here.
cc @jbrockmendel | {
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reverts #28733
cc @nrebena @jreback @WillAyd @charlesdong1991
just the complete revert for now for ci testing, will add commits for release notes. will NOT add regression tests see https://github.com/pandas-dev/pandas/issues/38721#issuecomment-761840221 | {
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} | 1 | 2021-01-17T19:11:11Z | 2021-01-19T18:15:08Z | 2021-01-19T18:11:37Z | 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
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} | 18 | 2021-01-17T20:15:01Z | 2021-01-20T09:42:33Z | 2021-01-20T07:22:57Z | MEMBER | null | Closes #39184
This is obviously a last-minute change, but if people agree on the deprecation, I think we should try to include it in v1.2.1. I *think* my patch is relatively safe, since converting the input to numpy arrays (what I am doing now manually as fallback) is what happened before adding `DataFrame.__array_ufunc__` as well.
It adds quite some lines of code, but it's mostly some simple checking of the exact case which is a bit verbose.
The specific tests I added were verified to pass on pandas 1.1.5, so they codify the previous behaviour (minus the warnings).
cc @TomAugspurger
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} | 1 | 2021-01-17T21:30:05Z | 2021-06-21T13:06:25Z | 2021-06-21T13:06:25Z | NONE | null | #### Is your feature request related to a problem?
I have often looked for the option to use the `explode` function on multiple columns at the same time.
Example:
```
A | B | C | D
------------------------
1 | a | [1,2] | [7,8]
2 | b | [3,4] | [9,1]
3 | c | [5,6] | [2,3]
```
===>
```
A | B | C | D
------------------
1 | a | 1 | 7
1 | a | 2 | 8
2 | b | 3 | 9
2 | b | 4 | 1
3 | c | 5 | 2
3 | c | 6 | 3
```
For obvious reasons exploding first one and then the other column will not yield the result we want here, because we will end up duplicating the values in the first explode during the second explode.
#### Describe the solution you'd like
Either it could be `df.explode(["C","D"])` or it could be its own function `df.multi_explode(["C","D"])`. Both ways could use more or less the same method that the existing explode function uses.
#### API breaking implications
The only issue I see with this are cases where the lists in the C and D column aren't the same length, in which case we would need to pad them.
#### Context
I don't know if this has been discussed previously, but if there is interest in this a Pull request concerning this feature I could try to implement it.
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} | 3 | 2021-01-17T22:02:49Z | 2021-01-21T23:41:12Z | 2021-01-21T23:41:08Z | CONTRIBUTOR | null | - This is my first time contributing - please let me know if any other detail is needed! Essentially I just swapped the order of the "Excel" and "CSV" paragraphs in the data importing section, so that the examples given for to_excel and read_excel would flow from the CSV example without needing repeated code/codeblocks.
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Hi
I am not sure what caused the problem, but when I try to import pandas I get a numpy attribute error. I tried pip install although I use anaconda and did not work
```
pip uninstall -y numpy
pip uninstall -y setuptools
pip install setuptools
pip install numpy
```
so when I try to import pandas this is the error I get
```
import pandas as pd
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-7dd3504c366f> in <module>
----> 1 import pandas as pd
~/opt/anaconda3/lib/python3.7/site-packages/pandas/__init__.py in <module>
20
21 # numpy compat
---> 22 from pandas.compat.numpy import (
23 np_version_under1p17 as _np_version_under1p17,
24 np_version_under1p18 as _np_version_under1p18,
~/opt/anaconda3/lib/python3.7/site-packages/pandas/compat/__init__.py in <module>
12 import warnings
13
---> 14 from pandas._typing import F
15
16 PY38 = sys.version_info >= (3, 8)
~/opt/anaconda3/lib/python3.7/site-packages/pandas/_typing.py in <module>
52 # array-like
53
---> 54 AnyArrayLike = TypeVar("AnyArrayLike", "ExtensionArray", "Index", "Series", np.ndarray)
55 ArrayLike = TypeVar("ArrayLike", "ExtensionArray", np.ndarray)
56
AttributeError: module 'numpy' has no attribute 'ndarray'
```
below are my python and jupyter versions
```
from platform import python_version
3.7.9
jupyter core : 4.7.0
jupyter-notebook : 6.2.0
qtconsole : 5.0.1
ipython : 7.19.0
ipykernel : 5.4.2
jupyter client : 6.1.11
jupyter lab : 3.0.5
nbconvert : 6.0.7
ipywidgets : 7.6.3
nbformat : 5.1.2
traitlets : 5.0.5
(base) __________-MacBook-Air:~ ____________$
```
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} | 3 | 2021-01-17T22:46:46Z | 2021-01-21T18:22:40Z | 2021-01-21T17:36:50Z | 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
- [ ] whatsnew entry
xref #38964 analogous bug in Timedelta (separate PR upcoming)
```
ts = pd.Timestamp.max - pd.Timedelta(304)
>>> ts.ceil("s")
Timestamp('1677-09-21 00:12:43.290448384')
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} | 2 | 2021-01-17T23:01:12Z | 2021-01-20T23:17:56Z | 2021-01-20T22:08:09Z | MEMBER | null | - [x] xref #24124
- [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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} | 4 | 2021-01-18T09:16:39Z | 2021-01-18T10:56:28Z | 2021-01-18T10:55:38Z | MEMBER | null | First commit is fixing links to the reference docs (many were not actually working links), the second commit is just a reorder to group related ones together (but together it makes the diff a bit difficult to interpret)
cc @simonjayhawkins | {
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} | 9 | 2021-01-18T09:43:13Z | 2021-01-28T20:55:16Z | 2021-01-19T13:58:31Z | 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.
---
**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
```python
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import codecs
x = [ 1, 2, 3, 4, 5 ]
y = [ 6, 7, 8, 9, 10 ]
z = [ 'a', 'b', 'c', 'd', 'e' ]
data = { "X": x, "Y":y, "Z":z }
df = pd.DataFrame( data, columns=[ "X", "Y", "Z" ] )
print( "Pandas version = %s" % pd.__version__ )
print(df)
fp = codecs.open( "out-testPD12.csv", "w", "utf-8" )
fp.write( "Pandas version = %s\n" % pd.__version__ )
df.to_csv( fp, index=False, header=True )
fp.close()
```
#### Problem description
When saving the file, `TypeError: utf_8_encode() argument 1 must be str, not bytes` flags on.
#### Expected Output
The output below has been obtained by downgrading and pinned Pandas to V1.1.5.
V1.1.2 is also tested OK.
```
Pandas version = 1.1.5
X,Y,Z
1,6,a
2,7,b
3,8,c
4,9,d
5,10,e
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
```
(base) Catalina1{kazzz-s} temp (1)% python testPD12.py
Pandas version = 1.2.0
X Y Z
0 1 6 a
1 2 7 b
2 3 8 c
3 4 9 d
4 5 10 e
Traceback (most recent call last):
File "testPD12.py", line 52, in <module>
df.to_csv( fp, index=False, header=True )
File "/Users/kazzz-s/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 3384, in to_csv
return DataFrameRenderer(formatter).to_csv(
File "/Users/kazzz-s/opt/anaconda3/lib/python3.8/site-packages/pandas/io/formats/format.py", line 1083, in to_csv
csv_formatter.save()
File "/Users/kazzz-s/opt/anaconda3/lib/python3.8/site-packages/pandas/io/formats/csvs.py", line 248, in save
self._save()
File "/Users/kazzz-s/opt/anaconda3/lib/python3.8/site-packages/pandas/io/common.py", line 104, in __exit__
self.close()
File "/Users/kazzz-s/opt/anaconda3/lib/python3.8/site-packages/pandas/io/common.py", line 89, in close
self.handle.flush()
File "/Users/kazzz-s/opt/anaconda3/lib/python3.8/codecs.py", line 721, in write
return self.writer.write(data)
File "/Users/kazzz-s/opt/anaconda3/lib/python3.8/codecs.py", line 377, in write
data, consumed = self.encode(object, self.errors)
TypeError: utf_8_encode() argument 1 must be str, not bytes
```
</details>
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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.
---
**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
```python
import pandas
df = pandas.read_excel("excel_sheetname_title.xlsx")
IndexError: list index out of range
```
#### Output of ``pd.show_versions()``
<details>
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
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 : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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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.
---
**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
```python
# Your code here
import pandas
df = pandas.read_excel("excel_sheetname_title.xlsx")
IndexError: list index out of range
```
<details>
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
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 : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
The file which read_excel fails on.
[excel_sheetname_title.xlsx](https://github.com/pandas-dev/pandas/files/5830079/excel_sheetname_title.xlsx) | {
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---
Hi
I am not sure what caused the problem, but when I try to import pandas I get a numpy attribute error. I am Mac user on Jupyter when I try to import pandas this is the error I get. I previously posted this as a question but I did not provide all the necessary info so I closed the QST#39243
```
import pandas as pd
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-7dd3504c366f> in <module>
----> 1 import pandas as pd
~/opt/anaconda3/lib/python3.7/site-packages/pandas/__init__.py in <module>
20
21 # numpy compat
---> 22 from pandas.compat.numpy import (
23 np_version_under1p17 as _np_version_under1p17,
24 np_version_under1p18 as _np_version_under1p18,
~/opt/anaconda3/lib/python3.7/site-packages/pandas/compat/__init__.py in <module>
12 import warnings
13
---> 14 from pandas._typing import F
15
16 PY38 = sys.version_info >= (3, 8)
~/opt/anaconda3/lib/python3.7/site-packages/pandas/_typing.py in <module>
52 # array-like
53
---> 54 AnyArrayLike = TypeVar("AnyArrayLike", "ExtensionArray", "Index", "Series", np.ndarray)
55 ArrayLike = TypeVar("ArrayLike", "ExtensionArray", np.ndarray)
56
AttributeError: module 'numpy' has no attribute 'ndarray'
below are my python and jupyter versions
```
Here are some relevant packages versions:
numpy :1.19.5
pandas. : 1.2.0
geopandas :0.63
python :3.7.9
anaconda :client 1.7.2
anaconda :0.0.1.1
setuptools 49.6.0
jupyter core : 4.7.0
jupyter-notebook : 6.2.0
qtconsole : 5.0.1
ipython : 7.19.0
ipykernel : 5.4.2
jupyter client : 6.1.11
jupyter lab : 3.0.5
nbconvert : 6.0.7
ipywidgets : 7.6.3
nbformat : 5.1.2
traitlets : 5.0.5
(base) __________-MacBook-Air:~ ____________$
I tried pip install although I use anaconda and did not work
```
pip uninstall -y numpy
pip uninstall -y setuptools
pip install setuptools
pip install numpy
```
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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
- [ ] whatsnew entry
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} | 2 | 2021-01-18T17:49:00Z | 2021-01-21T15:23:36Z | 2021-01-21T15:15:19Z | MEMBER | null | - [x] closes #38622
- [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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} | 2 | 2021-01-18T20:51:12Z | 2021-01-19T16:19:24Z | 2021-01-19T16:19:24Z | 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.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
import numpy as np
# %% Create random data
np.random.seed(50)
number_of_observations = 100
values = np.random.randn(number_of_observations)
# %% Form DataFrame
data = pd.DataFrame(values, columns=['A'])
data = data.cumsum() # Get more local maximums for comparison
# %% Accumulate the maximum
pandas_accumulate = np.maximum.accumulate(data).to_numpy()
numpy_accumulate = np.maximum.accumulate(data.to_numpy())
assert np.allclose(pandas_accumulate, numpy_accumulate)
```
#### Problem description
Numpy maximum accumulate does nothing to the data compared with the same function applied to the numpy array (confirmed with axis argument specified too). Assert statement passed on pandas version 1.1.5. Broken in master as of writing also.
#### Expected Output
As previous version, the result of the numpy function in the pandas DataFrame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_GB.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2021-01-18T21:33:49Z | 2021-01-19T18:00:19Z | 2021-01-19T16:19:24Z | MEMBER | null | Closes #39259
So we shouldn't call non-`__call__` ufuncs block-by-block, since they can work along a certain axis, like `accumulate`. In this case there were 2 problems: 1) we were applying the ufunc on the Block values, which is 2D but transposed compared to the DataFrame (so the accumulation was done in the wrong direction) and 2) the `axis` keyword was also not passed through in case the user specified it. | {
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# %% Create data
data = pd.DataFrame([
[1,2,3,4],
[1,2,3,4]
], columns=['A', 'B', 'C', 'D'])
# %% Check missing data assignment
addition = pd.DataFrame([
[5,6,7],
[1,2,3]
], columns=['A', 'B', 'C'])
data.iloc[0, :] = addition.iloc[0, :] # Fails
# %% Check misaligned assignment
data = pd.DataFrame([
[1,2,3,4],
[1,2,3,4]
], columns=['A', 'B', 'C', 'D'])
addition_misaligned = pd.DataFrame([
[5,6,7,8],
[1,2,4,3]
], columns=['A', 'B', 'D', 'C'])
data.iloc[0, :] = addition_misaligned.iloc[0, :] # Works, result incorrect
pd.testing.assert_frame_equal(data, addition_misaligned, check_like=True)
```
#### Problem description
My apologies if this is two reports in one.
Previously (pandas 1.1.5), assignment of partial series to rows in a DataFrame would complete and the alignment would be such that a missing value was applied to the element that was missing in the assignment. This now errors with a broadcast shape error.
Related, assignment of a fully specified series works, but the labels are not assigned correctly and instead assigned by their matrix position. Again, the values were aligned in pandas 1.1.5.
Reading the discussion on #39004 I think this may be related. And the error is similar to [this comment](https://github.com/pandas-dev/pandas/issues/39004#issuecomment-756685730).
```python-traceback
Traceback (most recent call last):
File "venv/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-c0c0cf213f59>", line 13, in <module>
data.iloc[0, :] = addition.iloc[0, :] # Fails
File "venv/lib/python3.7/site-packages/pandas/core/indexing.py", line 695, in __setitem__
iloc._setitem_with_indexer(indexer, value, self.name)
File "venv/lib/python3.7/site-packages/pandas/core/indexing.py", line 1644, in _setitem_with_indexer
self._setitem_single_block(indexer, value, name)
File "venv/lib/python3.7/site-packages/pandas/core/indexing.py", line 1872, in _setitem_single_block
self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
File "venv/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 565, in setitem
return self.apply("setitem", indexer=indexer, value=value)
File "venv/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 431, in apply
applied = getattr(b, f)(**kwargs)
File "venv/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 994, in setitem
values[indexer] = value
ValueError: could not broadcast input array from shape (3) into shape (4)
```
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
Assignment of the data to DataFrame respecting the labels of the assigning data.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_GB.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | 2021-01-18T23:27:56Z | 2021-01-19T16:23:39Z | 2021-01-19T16:21:05Z | 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
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} | 0 | 2021-01-19T00:35:06Z | 2021-07-15T04:22:00Z | 2021-07-15T04:22:00Z | 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.
- [X] (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
```python
import numpy as np
import pandas as pd
a = np.array(['a', 'b'], dtype='object')
df = pd.DataFrame(a)
assert np.shares_memory(df.values, a) # True
b = np.array([['a', 'b'],
['c', 'd']], dtype='object')
df2 = pd.DataFrame(b)
assert np.shares_memory(df2.values, b) # False
```
#### Problem description
This issue addresses the TODO in `pandas.core.internals.construction.py`, line 250 on master at time of writing. This was introduced by #26825. It's quite annoying, I'm trying to use DataFrame as an interface to access my dtype object numpy array, but I can't without copying it in first.
#### Expected Output
Exit code 0.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.10.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.5
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fsspec : 0.6.2
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.2.0
numba : 0.48.0
</details>
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} | 2 | 2021-01-19T02:08:34Z | 2021-01-21T23:59:18Z | 2021-01-21T23:59:14Z | CONTRIBUTOR | null | - [ ] partial fix for #38802
- [ ] 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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] | closed | false | null | [] | null | 2 | 2021-01-19T02:52:47Z | 2021-08-15T03:31:02Z | 2021-08-15T03:31:02Z | NONE | null | #### Is your feature request related to a problem?
When I use `pivot` or `pivot_table` and pass a list of `values`, I get a multiindex on the columns, as the docs say. However, there is no kwarg in the `pivot` or `pivot_table` functions to name the levels of this multiindex.
#### Describe the solution you'd like
It would be nice to have a `level_names` kwarg in `pivot` and `pivot_table` so I could pass a string or list of strings for the desired names of the new column multiindex levels.
#### Describe alternatives you've considered
Can be fixed by running `df_pivoted.columns.set_names('name', level=0)`, but it would be nice to not have to do this as a separate step.
```python
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
df_pivoted = df.pivot_table(index=['A','C'], columns='B', values=['D','E'])
# df_pivoted = df.pivot_table(index=['A','C'], columns='B', values=['D','E'], level_names='name') # this is what I want to be able to do
df_pivoted.columns.names # returns [None, 'B'], when what I want is ['name', 'B']
```
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Remove ``import pandas as pd`` in ``pandas/core/computation``. | {
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} | 44 | 2021-01-19T15:40:46Z | 2021-07-15T04:22:10Z | 2021-07-15T04:22:00Z | CONTRIBUTOR | null | This change is aesthetically unpleasing, it indents too far. #26825 already suggested to separate this part into a function. Please let me know what exactly I should abstract into a function, and where to put it. Alternatively, amend this PR yourself.
Thanks for pandas. :)
- [X] closes #39263
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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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} | 5 | 2021-01-19T17:59:42Z | 2021-01-29T19:14:21Z | null | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/39260#discussion_r559806188
Currently, an "accumulate" ufunc is applied on the full DataFrame at once, with the consequence that it doesn't preserve dtypes if you have mixed numeric columns, eg:
```
In [4]: df = pd.DataFrame({"a": [1, 3, 2, 4], "b": [0.1, 4.0, 3.0, 2.0]})
In [5]: df
Out[5]:
a b
0 1 0.1
1 3 4.0
2 2 3.0
3 4 2.0
In [6]: np.maximum.accumulate(df)
Out[6]:
a b
0 1.0 0.1
1 3.0 4.0
2 3.0 4.0
3 4.0 4.0
```
It is certainly possible for the default case (corresponding to ``.accumulate(axis=0)``) to apply this ufunc on each column or block, to preserve the column dtypes. When `axis=1` is passed to the ufunc this is not possible.
See at the linked PR discussion above for some more details at what is involved to implement this.
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} | 4 | 2021-01-19T19:35:55Z | 2021-01-21T23:48:17Z | 2021-01-21T23:40:06Z | 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
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} | 0 | 2021-01-19T20:13:25Z | 2021-01-20T19:24:12Z | 2021-01-20T19:21:59Z | MEMBER | null | Slightly faster for PeriodArray, no change for Period.
```
In [3]: pi = pd.period_range("2016-01-01", periods=100, freq="D")
In [4]: %timeit pi.asfreq("W")
43.5 µs ± 696 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
36.3 µs ± 1.06 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
In [5]: per = pi[0]
In [6]: %timeit per.asfreq("W")
10.5 µs ± 88.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
10.5 µs ± 160 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
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} | 3 | 2021-01-19T20:36:45Z | 2021-01-21T17:28:21Z | 2021-01-21T15:53:36Z | MEMBER | null | - [x] closes #38521
- [x] tests added / passed
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} | 3 | 2021-01-19T21:15:22Z | 2021-01-21T15:25:28Z | 2021-01-21T15:19:46Z | 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
One thing that might make this undesirable is that the annotations on this are accurate for Period but not for PeriodArray. Thoughts @simonjayhawkins
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} | 6 | 2021-01-19T21:21:25Z | 2021-01-21T16:12:55Z | 2021-01-21T15:54:42Z | MEMBER | null | - [x] closes #15695
- [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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Setting with a scalar key which is duplicated in columns should be the same as using a listlike key | {
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} | 2 | 2021-01-19T22:04:49Z | 2021-01-20T22:14:16Z | 2021-01-19T23:39:10Z | NONE | null | Hi,
thanks for all the hardwork and the wonderful pandas.
Is there a way to install "minimal" pandas ?
I just need dataframe and save to CSV functionality.
We have a requirement to keep our alpine docker container really really small and want to install just the dataframe and csv modules. is it possible?
thanks. | {
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] | closed | false | null | [] | null | 4 | 2021-01-19T22:24:24Z | 2021-04-30T00:50:10Z | 2021-04-30T00:50:10Z | NONE | null | - [x] closes #39111
- [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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} | 1 | 2021-01-19T22:43:44Z | 2021-04-07T02:34:07Z | null | MEMBER | null | We probably can remove both checks, see #39092 | {
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] | closed | false | null | [] | null | 1 | 2021-01-20T00:31:26Z | 2021-01-20T00:34:20Z | 2021-01-20T00:34:20Z | 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.
---
**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
```python
import pandas as pd
import numpy as np
import os
df = pd.read_excel('BinanceExport.xlsx')
df
```
#### Problem description
pandas.read_excel doesn't correctly read certain binance.com xlsx exports (Microsoft Workbook format)
Trying to my binance exports returns an empty dataframe
```python
df = pd.read_excel('BinanceExport.xlsx')
df
>>> Date(UTC)
---------
```
or
```python
df = pd.read_excel('BinanceExport.xlsx')
df
>>> Empty DataFrame
Columns: [Date(UTC)]
Index: []
```
Renaming the file doesn't help, but manually resaving does
It seems related to this issue https://github.com/pandas-dev/pandas/issues/39250
but mine isn't a corrupted file nor does it return an error
I posted a question on stackoverflow and the export is downloadable down below
#### Expected Output
return complete dataframe
#### Output of ``pd.show_versions()``
<details>
[INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.0.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None]
</details>
[BinanceExport.xlsx](https://github.com/pandas-dev/pandas/files/5839398/BinanceExport.xlsx) | {
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} | 2 | 2021-01-20T02:17:21Z | 2021-01-28T14:59:33Z | 2021-01-28T14:59:30Z | CONTRIBUTOR | null | - [ ] closes #21881
- [ ] 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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] | closed | false | null | [] | null | 0 | 2021-01-20T05:07:00Z | 2021-01-21T17:23:23Z | 2021-01-21T17:23:23Z | CONTRIBUTOR | null | only seems to happen sometimes in "Database / Linux_py37_cov"
```python
_______________ test_to_csv_compression_encoding_gcs[zip-cp1251] _______________
[gw1] linux -- Python 3.7.9 /usr/share/miniconda/envs/pandas-dev/bin/python
gcs_buffer = <_io.BytesIO object at 0x7f4959d0b650>, compression_only = 'zip'
encoding = 'cp1251'
@td.skip_if_no("gcsfs")
@pytest.mark.parametrize("encoding", ["utf-8", "cp1251"])
def test_to_csv_compression_encoding_gcs(gcs_buffer, compression_only, encoding):
"""
Compression and encoding should with GCS.
GH 35677 (to_csv, compression), GH 26124 (to_csv, encoding), and
GH 32392 (read_csv, encoding)
"""
from fsspec import registry
registry.target.clear() # remove state
df = tm.makeDataFrame()
# reference of compressed and encoded file
compression = {"method": compression_only}
if compression_only == "gzip":
compression["mtime"] = 1 # be reproducible
buffer = BytesIO()
df.to_csv(buffer, compression=compression, encoding=encoding, mode="wb")
# write compressed file with explicit compression
path_gcs = "gs://test/test.csv"
df.to_csv(path_gcs, compression=compression, encoding=encoding)
> assert gcs_buffer.getvalue() == buffer.getvalue()
E AssertionError: assert b'PK\x03\x04\...0\x00\x00\x00' == b'PK\x03\x04\...0\x00\x00\x00'
E At index 10 diff: b'\xa6' != b'\xa5'
E Use -v to get the full diff
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} | 0 | 2021-01-20T07:14:41Z | 2021-01-20T15:17:03Z | 2021-01-20T14:52:46Z | MEMBER | null | - [ ] xref #36833
- [ ] 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
Moved handing timestamp data from ``describe_categorical_1d`` into ``describe_timestamp_as_categorical_1d``.
Extracted function ``select_describe_func`` for selecting the proper function, suitable for a given data type.
Pros:
- Simplified logic on which function is selected for which data type.
- Improved way of issuing ``FutureWarning``.
- Next logical step in enabling the strategy pattern.
Cons:
- Unnecessary argument ``percentiles_ignored`` passed into functions for handling categorical types. This is done to unify the interface across all functions for describing a series. The problem will be handled when enabling strategy pattern as the percentiles will become an instance attribute for the strategy class.
- Slight duplication in ``describe_categorical_1d`` into ``describe_timestamp_as_categorical_1d``, which will be handled as the strategy class for categorical data is created (it will be encapsulated there). | {
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} | 0 | 2021-01-20T07:28:05Z | 2021-01-20T08:15:48Z | 2021-01-20T08:15:48Z | NONE | null | Backport PR #39239: DEPR: raise deprecation warning in numpy ufuncs on DataFrames if not aligned + fallback to <1.2.0 behaviour | {
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} | 7 | 2021-01-20T09:43:08Z | 2021-07-08T13:11:03Z | 2021-07-08T13:11:03Z | CONTRIBUTOR | null | @jorisvandenbossche pls following up and fix the docs
it's not a style issue rather this is completely inconsistent with the current docs
we NEVER use the style - always ipython docs style
__Originally posted by @jreback in https://github.com/pandas-dev/pandas/pull/39239#issuecomment-763475054__ | {
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} | 1 | 2021-01-20T11:27:14Z | 2021-01-21T15:44:04Z | 2021-01-21T15:44:04Z | CONTRIBUTOR | null | #### Location of the documentation
https://pandas.pydata.org/docs/whatsnew/v1.2.0.html
#### Suggested fix for documentation
It would be more accurate for [this line](https://github.com/pandas-dev/pandas/blame/ff628b17ccfcaf14ceefeb438aafa56d3849937c/doc/source/whatsnew/v1.2.0.rst#L749) to reference https://github.com/pandas-dev/pandas/pull/37983 (itself) as #27101 does _not_ deal with the `xarray` upgrade, whereas #37983 does.
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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
- [x] whatsnew entry
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} | 6 | 2021-01-20T13:23:01Z | 2021-01-21T17:30:12Z | 2021-01-21T15:04:53Z | CONTRIBUTOR | null | - [x] closes #39203
- [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
I didn't add a whatsnew entry since it's only style fixes.
I'll open a new issue regarding @jreback suggestion to ban this with a precommit style rule.
I prefer it to be done in a different PR. | {
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} | 19 | 2021-01-20T13:49:02Z | 2021-02-09T21:41:35Z | 2021-02-09T21:26:11Z | MEMBER | null | Tracking issue for the 1.2.2 release.
https://github.com/pandas-dev/pandas/milestone/82
Currently scheduled for February 07, 2021
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression | {
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- [ ] 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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} | 4 | 2021-01-20T14:33:39Z | 2021-08-15T03:34:04Z | 2021-08-15T03:34:04Z | 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.
---
**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
```python
writer = pd.ExcelWriter("test.xlsx", engine="openpyxl")
pd.DataFrame().to_excel(writer, sheet_name="sheet", index=False)
writer.save()
pd.read_excel("test.xlsx", sheet_name="sheet")
>> KeyError: 'Worksheet sheet does not exist.'
```
#### Problem description
Executing DataFrame.to_excel() with sheet_name="sheet" creates an Excel with a sheet named "sheet1" instead of the requested name "sheet".
#### Expected Output
An Excel with a sheet named "sheet".
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-38-generic
Version : #43~20.04.1-Ubuntu SMP Tue Jan 12 16:39:47 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_ES.UTF-8
LOCALE : es_ES.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 41.2.0
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 : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] 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 a skeleton for the strategy pattern in ``pandas/core/describe.py``.
Currently the methods ``describe`` simply delegate to the corresponding ``describe_`` functions.
In the next steps I plan on moving the ``describe`` functions into each of the strategy classes.
Finally, these functions can be unified, since a lot there has a common pattern. | {
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} | 2 | 2021-01-20T16:02:21Z | 2021-01-21T12:15:35Z | 2021-01-21T11:21:08Z | MEMBER | null | - [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
For some background: there was some consensus on doing this [here](https://github.com/pandas-dev/pandas/pull/36034#issuecomment-685186742)
Then [here](https://github.com/pandas-dev/pandas/pull/39270#pullrequestreview-571190631) there was the suggestion to use `ast.parse`, rather than regular expressions. I had a go at that, and made this into its own package: https://github.com/MarcoGorelli/no-string-hints , which I've included here as a pre-commit hook
The changes here are just a result of running `pre-commit run no-string-hints -a`, and then adding `from __future__ import annotations` to some files. | {
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] | null | 1 | 2021-01-20T16:34:11Z | 2021-01-20T17:29:44Z | 2021-01-20T17:29:44Z | CONTRIBUTOR | null | Followup to issue #39203
Add a precommit rule that validates there is no usage in pd.api.types.<method>
For example:
Wrong:
```pd.api.types.is_number(1)```
Right:
```
from pandas.api.types import is_number
is_number(1)
```
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} | 1 | 2021-01-20T17:35:04Z | 2021-01-20T22:15:15Z | 2021-01-20T22:15:15Z | MEMBER | null | Currently, the pygrep hooks have the "or" `|` symbol at the end of the line, which makes diffs longer than necessary and can cause bugs. It would be better to put that symbol at the beginning of the line.
For example, instead of
```
- id: non-standard-numpy.random-related-imports
name: Check for non-standard numpy.random-related imports excluding pandas/_testing.py
language: pygrep
exclude: pandas/_testing.py
entry: |
(?x)
# Check for imports from np.random.<method> instead of `from numpy import random` or `from numpy.random import <method>`
from\ numpy\ import\ random|
from\ numpy.random\ import
```
it should be
```
- id: non-standard-numpy.random-related-imports
name: Check for non-standard numpy.random-related imports excluding pandas/_testing.py
language: pygrep
exclude: pandas/_testing.py
entry: |
(?x)
# Check for imports from np.random.<method> instead of `from numpy import random` or `from numpy.random import <method>`
from\ numpy\ import\ random
|from\ numpy.random\ import
```
There's even an `|` symbol missing in the `non-standard-imports-in-tests`, which should be fixed (having the symbol at the beginning of the line would probably have made this easier to spot)
The file that needs changing is `.pre-commit-config.yaml` | {
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] | closed | false | null | [] | null | 7 | 2021-01-20T17:35:48Z | 2021-11-08T16:37:01Z | 2021-01-28T21:25:54Z | MEMBER | null | Posting largely for discussion with @phofl.
Motivations:
1) Try to end up with fewer code paths for setitem so that we can ensure consistent inplace/view/copy behavior (xref #38896)
2) Simplify (ATM this does not simplify things)
3) Perf: iterating over blocks instead of columns may save some copies (though doing the Index.intersection calls may go the other way) (havent done any profiling yet)
Non-trivial overlap with #39044.
Some of this would be simplified by #39163.
No logical overlap with #39266, but will require rebase.
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] | closed | false | null | [] | null | 4 | 2021-01-20T19:29:57Z | 2021-07-11T23:40:17Z | 2021-07-11T23:40:17Z | NONE | null | The Following goals are being accomplished in this PR:
- [x] Having the navigation bar's 'Getting Started' link point to `/docs/getting_started/index.html` from `/getting_started.html`
- [x] Removing the redundant markup `pandas/web/pandas/getting_started.md`.
<!--- [x] Adding above redirecting information in `pandas/doc/redirect.csv`.-->
#32307
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} | 1 | 2021-01-20T20:20:43Z | 2021-01-20T22:15:36Z | 2021-01-20T22:15:15Z | CONTRIBUTOR | null | - [x] closes #39301
- [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
I moved the `|` symbols from the end of the line to the beginning of the line in pygrep hooks.
I also added the `|` symbol in the `non-standard-imports-in-tests` at the beginning of the line 93, which was missing as @MarcoGorelli pointed.
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} | 3 | 2021-01-20T22:06:00Z | 2021-01-27T07:13:27Z | 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
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
'a': np.random.randint(100, size=100),
'b': np.random.randint(100, size=100),
'c': pd.date_range(start='1/1/2018', periods=100, freq='s'),
}
)
axs = df.set_index(df['c'] + pd.to_timedelta(df['b'], unit='ms')).drop(columns='c').plot(subplots=True)
#[ax.minorticks_off() for ax in axs] # note uncomment this to work around issue
```
#### Problem description
When using `DataFrame.plot` with `subplots=True`, extra tick marks appear on the x axis.
#### Expected Output
No extra ticks. Uncomment the line above to get the below:
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | 2021-01-20T22:23:46Z | 2021-01-21T15:24:33Z | 2021-01-21T15:16:16Z | 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
Did the same thing for IntervalIndex earlier in #39267 | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (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
```python
import pandas as pd
idx = pd.Index([pd.Timestamp("2011-01-01")], dtype=object)
ser = pd.Series([1], index=idx)
df = pd.DataFrame([1], index=idx)
ser.index
Out[4]: DatetimeIndex(['2011-01-01'], dtype='datetime64[ns]', freq=None)
df.index
Out[5]: Index([2011-01-01 00:00:00], dtype='object')
```
#### Problem description
The behavior should be consistent, but not sure what to expected here. The inference is a nice feature although.
The current behavior leads to weird situations with indexing:
```
ser.loc["2011"]
Out[7]:
2011-01-01 1
dtype: int64
```
works while
```
df.loc["2011"]
Traceback (most recent call last):
File "/home/developer/PycharmProjects/pandas/pandas/core/indexes/base.py", line 3225, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
cpdef get_loc(self, object val):
File "pandas/_libs/index.pyx", line 101, in pandas._libs.index.IndexEngine.get_loc
return self.mapping.get_item(val)
File "pandas/_libs/hashtable_class_helper.pxi", line 5038, in pandas._libs.hashtable.PyObjectHashTable.get_item
cpdef get_item(self, object val):
File "pandas/_libs/hashtable_class_helper.pxi", line 5046, in pandas._libs.hashtable.PyObjectHashTable.get_item
raise KeyError(val)
KeyError: '2011'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/developer/anaconda3/envs/omi_reports/pandas-dev/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-8-59984db8d56a>", line 1, in <module>
df.loc["2011"]
File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 902, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 1128, in _getitem_axis
return self._get_label(key, axis=axis)
File "/home/developer/PycharmProjects/pandas/pandas/core/indexing.py", line 1077, in _get_label
return self.obj.xs(label, axis=axis)
File "/home/developer/PycharmProjects/pandas/pandas/core/generic.py", line 3752, in xs
loc = index.get_loc(key)
File "/home/developer/PycharmProjects/pandas/pandas/core/indexes/base.py", line 3227, in get_loc
raise KeyError(key) from err
KeyError: '2011'
```
#### Expected Output
Bot returning the same index dtype
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : ff628b17ccfcaf14ceefeb438aafa56d3849937c
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-38-generic
Version : #43~20.04.1-Ubuntu SMP Tue Jan 12 16:39:47 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+483.gff628b17cc.dirty
numpy : 1.21.0.dev0+438.gbf4ac1f81
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0.dev0+5e9eb01
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 0 | 2021-01-20T23:21:12Z | 2021-01-21T01:56:01Z | 2021-01-21T00:50:18Z | MEMBER | null | - [x] closes #24124
- [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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] | closed | false | null | [] | null | 2 | 2021-01-21T00:14:56Z | 2021-02-07T15:17:10Z | 2021-02-07T15:17:10Z | 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.
---
**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
```python
def convert_encounter_chunks(file, inpath, outpath, param_encoding='latin1', param_chunksize=500000, debug=False):
starttime=datetime.datetime.now()
print("start time:-", starttime )
filepath=f"{TNP_BUCKET}/{file}"
print('converting ', filepath )
dfreader=pd.read_sas(filepath,
encoding=param_encoding,
chunksize=param_chunksize,iterator=True)
label=file.split('.')[0]
outpath_base=f"{outpath}/{label}"
iter=0
for chunk in dfreader:
chunk.to_parquet(f"{outpath_base}_{iter}")
itertime=datetime.datetime.now()
print("chunk time:-", itertime )
iterdiff=itertime-starttime
cumminutes = iterdiff.seconds / 60
print('Cumulative Time in minutes: ', cumminutes)
iter=iter+1
if debug==True and iter>1:
print('Debug mode on only running two iterations')
break
print("end time:-", endtime )
#display(dfs[0])
```
#### Problem description
A value error is returned related to date formats. I believe the data value meets the datetime criteria but then there is an error when actually implementing the datetime.
Here's the stacktrace:
```python-traceback
`---------------------------------------------------------------------------
OutOfBoundsDatetime Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py in _convert_datetimes(sas_datetimes, unit)
51 try:
---> 52 return pd.to_datetime(sas_datetimes, unit=unit, origin="1960-01-01")
53 except OutOfBoundsDatetime:
/opt/conda/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
804 else:
--> 805 values = convert_listlike(arg._values, format)
806 result = arg._constructor(values, index=arg.index, name=arg.name)
/opt/conda/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
345 result, tz_parsed = tslib.array_with_unit_to_datetime(
--> 346 arg, unit, errors=errors
347 )
pandas/_libs/tslib.pyx in pandas._libs.tslib.array_with_unit_to_datetime()
OutOfBoundsDatetime: cannot convert input with unit 'd'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-21-6b87792f6a22> in <module>
1 myfile='nicuid_encounter_infant.sas7bdat'
----> 2 convert_encounter_chunks(myfile, TNP_BUCKET, TNP_BUCKET_CONVERTED, param_chunksize=2000000, debug=False)
<ipython-input-20-9b8da485bcb0> in convert_encounter_chunks(file, inpath, outpath, param_encoding, param_chunksize, debug)
21 dfs = [] # holds data chunks
22 iter=0
---> 23 for chunk in dfreader:
24 #dfs.append(chunk)
25 #df=dfs[iter]
/opt/conda/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py in __next__(self)
330
331 def __next__(self):
--> 332 da = self.read(nrows=self.chunksize or 1)
333 if da is None:
334 self.close()
/opt/conda/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py in read(self, nrows)
721 p.read(nrows)
722
--> 723 rslt = self._chunk_to_dataframe()
724 if self.index is not None:
725 rslt = rslt.set_index(self.index)
/opt/conda/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py in _chunk_to_dataframe(self)
769 if self.convert_dates:
770 if self.column_formats[j] in const.sas_date_formats:
--> 771 rslt[name] = _convert_datetimes(rslt[name], "d")
772 elif self.column_formats[j] in const.sas_datetime_formats:
773 rslt[name] = _convert_datetimes(rslt[name], "s")
/opt/conda/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py in _convert_datetimes(sas_datetimes, unit)
58 elif unit == "d":
59 return sas_datetimes.apply(
---> 60 lambda sas_float: datetime(1960, 1, 1) + timedelta(days=sas_float)
61 )
62 else:
/opt/conda/lib/python3.7/site-packages/pandas/core/series.py in apply(self, func, convert_dtype, args, **kwds)
4106 else:
4107 values = self.astype(object)._values
-> 4108 mapped = lib.map_infer(values, f, convert=convert_dtype)
4109
4110 if len(mapped) and isinstance(mapped[0], Series):
pandas/_libs/lib.pyx in pandas._libs.lib.map_infer()
/opt/conda/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py in <lambda>(sas_float)
58 elif unit == "d":
59 return sas_datetimes.apply(
---> 60 lambda sas_float: datetime(1960, 1, 1) + timedelta(days=sas_float)
61 )
62 else:
ValueError: cannot convert float NaN to integer
`
```
#### Expected Output
No error / successful conversion
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-12-cloud-amd64
Version : #1 SMP Debian 4.19.152-1 (2020-10-18)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.5.0
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.49.1
</details>
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- [ ] 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 | 2021-01-21T03:22:50Z | 2021-01-28T23:00:37Z | 2021-01-22T14:00:16Z | MEMBER | null | - [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
pre-commit adjusted to account for the false positive on `Union[SeriesGroupBy, DataFrameGroupBy]`.
Moves part of groupby.agg implementation from `pandas.core.aggregation` to `pandas.core.apply`. The only difference in the code is that in `apply`, the error message raised is different when the argument is a string (e.g. "sum") and the kwarg axis=1 is provided. Added a test to check for error message.
After this will be resample and rolling, then much of the implementation code in core.aggregation code can be moved into apply. | {
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} | 1 | 2021-01-21T03:46:29Z | 2021-02-06T13:56:43Z | 2021-01-21T16:33:32Z | MEMBER | null | - [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
Ref: https://github.com/pandas-dev/pandas/pull/39212#issuecomment-763112628
Just a move. Will be following up with combining Series/DataFrame tests where appropriate. | {
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} | 7 | 2021-01-21T09:27:55Z | 2021-02-01T19:15:09Z | 2021-02-01T19:15:09Z | NONE | null | #### Code Sample
```
import pandas as pd
#create a df and insert a series astype String
df = pd.DataFrame({1:[1,2,3]},index=[0,1,3])
df.insert(loc=0,column='test',value=pd.Series(["one","two","three"]).astype("string"))
print(df)
#The values inserted normally evaluates correct though
print('==============')
print(pd.Series(["one","two","three"]).astype("string"))
```
Output:
```
test 1
0 one 1
1 two 2
3 <NA> 3
==============
0 one
1 two
2 three
dtype: string
```
#### Problem description
Issue: StringDtype exhibits strange behaviour when inserting as column. Some elements are inserted as <NaN>, but evaluates correctly outside of the insert function
#### Expected Output
Values should be inserted as is.
```
test 1
0 one 1
1 two 2
3 three 3
==============
0 one
1 two
2 three
dtype: string
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-240.1.1.el8_3.x86_64
Version : #1 SMP Thu Nov 19 17:20:08 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_ZA.UTF-8
LOCALE : en_ZA.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
#### Workaround
Parse to "Values" before inserting. In the example above:
df.insert(loc=0,column='test',value=pd.Series(["one","two","three"]).astype("string").values) | {
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} | 2 | 2021-01-21T10:31:33Z | 2021-01-21T18:00:02Z | 2021-01-21T17:59:40Z | MEMBER | null | Small clarification, because "previously" only made sense in the actual v1.2.0 release notes, and not in the main docs | {
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] | open | false | null | [] | null | 1 | 2021-01-21T11:01:36Z | 2021-08-15T03:36:58Z | null | NONE | null | In MSSQL, JOIN statement is allowed to take in LIKE clause instead of the usual exact match condition.
Hence, I am wondering how is the left_on key being compared to the right_on key in merge. So that maybe I can try to change the exact match to a fuzzy match :( | {
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} | 3 | 2021-01-21T12:09:49Z | 2021-01-22T00:04:13Z | 2021-01-21T15:44:04Z | CONTRIBUTOR | null | - [x] closes #39291
- [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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} | 3 | 2021-01-21T12:26:29Z | 2021-01-25T06:34:17Z | 2021-01-24T21:46:22Z | CONTRIBUTOR | null | **DOC**: this edits the `style.ipynb` file to include examples of the 3 recent enhancements to `Styler`:
- Tooltips
- External CSS classes to datacells
- Row and Column styling via table styles
It also addresses 3 minor bugs that I detected when wirting the documentation:
**BUG1** (lines 696-703): `set_td_classes` only attached classes to 'diagonal elements' of the DataFrame due a misaligned comprehension loop:
```
{
r: {
c: [str(classes.iloc[r, c])]
for c, cn in enumerate(classes.columns) if not mask.iloc[r, c]
}
for r, rn in enumerate(classes.index)
}
```
replaces:
```
{
r: {
c: [str(classes.iloc[r, c])]
}
for r, rn in enumerate(classes.index)
for c, cn in enumerate(classes.columns)
if not mask.iloc[r, c]
}
```
**BUG2** (lines 513 to 537): external css class names from the set_td_classes were appended to the HTML id tag, as well as the HTML class tag, due to a premature variable update relating to the `row_dict`. The solution was to re-order so that 'id' tags were generated first and then 'class' was updated with information from the new methods.
**BUG3** (lines 1920-1925): Tooltips were generated for the wrong indexes/columns, since the null rows/columns were dropped and then the interger locations of rows/columns was mapped. The solution was to avoid dropping null rows/columns.
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} | 4 | 2021-01-21T15:08:00Z | 2021-01-21T23:58:19Z | 2021-01-21T23:58:16Z | CONTRIBUTOR | null | Benchmarks derived from GH37484 (https://github.com/pandas-dev/pandas/issues/37484). This captures a peculiar difference in timings between a DataFrame originating from `df.set_index(...).head(...)` and one from `df.head(...).set_index(...)`.
- [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
I've not included a whatsnew entry, as no section seems particularly relevant to internal-only test/benchmark changes. If it should go in, shout and I'll add a note somewhere. | {
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