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https://api.github.com/repos/pandas-dev/pandas/issues/38023 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38023/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38023/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38023/events | https://github.com/pandas-dev/pandas/issues/38023 | 749,069,731 | MDU6SXNzdWU3NDkwNjk3MzE= | 38,023 | QST: Pandas without MKL? | {
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} | 1 | 2020-11-23T19:34:04Z | 2020-11-24T00:22:18Z | 2020-11-24T00:22:08Z | NONE | null | I build a small application that uses `pandas` and use `pyinstaller` to build `.exe` file. Since the default `numpy` uses MKL, which results in very large folder. I use `conda install conda-forge nomkl numpy` to install numpy without MKL. Then I install pandas using `conda install pandas`. The program works well. However, after I convert the code to `.exe` file. I encounter the following error from pandas:
```
Traceback (most recent call last):
File "\\VBoxSvr\twitterscraper\twitter_scraper_tweepy_Vader_V4.py", line 21, in <module>
File "c:\users\XXXX\miniconda3\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 493, in exec_module
File "pandas\__init__.py", line 16, in <module>
ImportError: Unable to import required dependencies:
numpy: cannot import name '_mklinit' from partially initialized module 'mkl' (most likely due to a circular import) (C:\Users\XXXX\Documents\twitterscraper\dist\twitter_scraper_tweepy_Vader_V4\mkl\__init__.pyc)
[2136] Failed to execute script twitter_scraper_tweepy_Vader_V4
```
I'm not sure why, but it seems that pandas still requires MKL. I wonder if there is a version of pandas that does not require MKL?
I also tried to install pandas using `conda install conda-forge::blas=*=openblas pandas`. The problem remains.
Info of my system:
* OS: Win10
* Python: 3.8.3 (installed from Miniconda)
* Numpy: 1.19.4 (installed from `conda-forge` together with `nomkl` to use blas.
* Pandas: 1.1.3 (installed using `conda install pandas` or `conda install conda-forge::blas=*=openblas pandas`) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38024/events | https://github.com/pandas-dev/pandas/pull/38024 | 749,104,785 | MDExOlB1bGxSZXF1ZXN0NTI1OTc4MDEx | 38,024 | Revert "REF: back IntervalArray by a single ndarray (#37047)" | {
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} | 6 | 2020-11-23T20:32:49Z | 2020-11-24T19:22:32Z | 2020-11-24T17:35:58Z | MEMBER | null | This reverts commit 9cb372376fc78aa66e2559de919592007b74cfaa.
See discussion in https://github.com/pandas-dev/pandas/pull/37047/
@jbrockmendel can you give this a check? (there were already several conflicts) | {
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} | 2 | 2020-11-23T20:44:06Z | 2021-09-29T13:28:07Z | 2021-09-29T13:28:07Z | 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.
---
```python
import pandas as pd
df1 = pd.DataFrame({
"date": ["2015-03-22", pd.NaT],
"a": ["a", "aa"],
"b": [2, 3]
})
df1 = df1.set_index("date") # works as intended
print(df1)
df1 = df1.reset_index()
df1 = df1.set_index(["date", "a"]) # works as intended
print(df1)
df2 = pd.DataFrame({
"date": [pd.NaT, pd.NaT],
"a": ["a", "aa"],
"b": [2, 3]
})
df2 = df2.set_index("date") # works as intended
print(df2)
df2 = df2.reset_index()
df2 = df2.set_index(["date", "a"]) # date will be converted to NaN here
print(df2)
df3 = pd.DataFrame({
"date": [pd.NaT, pd.NaT],
"a": ["a", "aa"],
"b": [2, 3]
})
# working alternative, but at what cost
df3.index = pd.MultiIndex.from_frame(df3.loc[:, ["date", "a"]])
df3 = df3.drop(columns=["date", "a"])
print(df3)
df3.reset_index() # ValueError thrown here
```
python 3.8.3
pandas 1.1.3
This bug will not occur if the column in question contains any values other than NaT, nor if setting a single column as the index.
With df1, everything works as expected.
In df2 (identical to df1 except "date" contains exclusively pd.NaT values) when set_index is used with more than one column, the date level of the resulting MultiIndex has now been converted to NaNs.
With df3 (identical to df2), the use of set_index is circumvented and the MultiIndex is set as intended, however this now leads to another issue where attempting to use reset_index will throw "ValueError: cannot convert float NaN to integer".
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https://api.github.com/repos/pandas-dev/pandas/issues/38026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38026/events | https://github.com/pandas-dev/pandas/pull/38026 | 749,193,070 | MDExOlB1bGxSZXF1ZXN0NTI2MDUwMzE2 | 38,026 | Bug in loc raising KeyError when MultiIndex columns has only one level | {
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@jbrockmendel I think we should check for MultiIndex instead of level number here | {
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} | 3 | 2020-11-24T03:07:32Z | 2021-01-01T01:35:55Z | 2020-12-31T19:11:28Z | CONTRIBUTOR | null | - [x] closes #37510
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xref #16277
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the .corr method filter the columns to only keep the numerical columns before apply any corr calclation. one issue with that is if I would like to apply a callable method _cramer_v to calc the correlation between categorical features, the .corr will skip it. I think for callable method, it should not skip any type of columns | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38032/events | https://github.com/pandas-dev/pandas/issues/38032 | 749,330,860 | MDU6SXNzdWU3NDkzMzA4NjA= | 38,032 | BUG: series construction from dict of Timedelta scalar doesn't work | {
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} | 5 | 2020-11-24T04:28:02Z | 2020-12-12T23:23:56Z | 2020-12-12T23:23:56Z | MEMBER | null | Construction of `Series` from a dictionary of scalars sometimes fails:
#### Code Sample, a copy-pastable example
```python
In [31]: import pandas as pd
...: import pandas._testing as tm
...:
...: td = pd.Timedelta(nanoseconds=500)
...: ser = pd.Series({"a": td})
...: expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
...:
...: tm.assert_series_equal(ser, expected)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-31-a9c6a6312101> in <module>
6 expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
7
----> 8 tm.assert_series_equal(ser, expected)
[... skipping hidden 1 frame]
~/repos/pandas/pandas/_testing.py in assert_extension_array_equal(left, right, check_dtype, index_values, check_less_precise, check_exact, rtol, atol)
1243 # Avoid slow object-dtype comparisons
1244 # np.asarray for case where we have a np.MaskedArray
-> 1245 assert_numpy_array_equal(
1246 np.asarray(left.asi8), np.asarray(right.asi8), index_values=index_values
1247 )
[... skipping hidden 1 frame]
~/repos/pandas/pandas/_testing.py in _raise(left, right, err_msg)
1155 diff = diff * 100.0 / left.size
1156 msg = f"{obj} values are different ({np.round(diff, 5)} %)"
-> 1157 raise_assert_detail(obj, msg, left, right, index_values=index_values)
1158
1159 raise AssertionError(err_msg)
~/repos/pandas/pandas/_testing.py in raise_assert_detail(obj, message, left, right, diff, index_values)
1085 msg += f"\n[diff]: {diff}"
1086
-> 1087 raise AssertionError(msg)
1088
1089
AssertionError: numpy array are different
numpy array values are different (100.0 %)
[index]: [a]
[left]: [500]
[right]: [0]
```
Ran on 1.2 master | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38033 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38033/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38033/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38033/events | https://github.com/pandas-dev/pandas/issues/38033 | 749,339,692 | MDU6SXNzdWU3NDkzMzk2OTI= | 38,033 | TST: dataframe constructor should preserve order with standard Python dicts | {
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} | 6 | 2020-11-24T04:51:48Z | 2020-12-02T03:04:37Z | 2020-12-02T03:04:37Z | MEMBER | null | We have a couple of dataframe tests (xref #13304) related to column ordering when constructed from an `OrderedDict`:
https://github.com/pandas-dev/pandas/blob/e4cf3abe0020aa801950e0383fffc13ee9703456/pandas/tests/frame/test_constructors.py#L1250-L1288
As of [PEP 468](https://www.python.org/dev/peps/pep-0468/) standard dictionaries are ordered so these tests should also work with `OrderedDict` replaced by `dict` | {
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} | 4 | 2020-11-24T05:17:15Z | 2020-11-26T22:30:49Z | 2020-11-26T22:24:31Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-11-24T09:36:48Z | 2020-11-25T10:53:11Z | 2020-11-25T10:52:56Z | MEMBER | null | Backport PR #37986 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38036 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38036/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38036/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38036/events | https://github.com/pandas-dev/pandas/issues/38036 | 749,662,069 | MDU6SXNzdWU3NDk2NjIwNjk= | 38,036 | speed up apply calculation | {
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"descript... | closed | false | null | [] | null | 2 | 2020-11-24T12:23:41Z | 2020-11-24T17:15:14Z | 2020-11-24T17:15:14Z | NONE | null | Hi, I have a huge database with multi index ((8832, 14384)) from which I calculate some statistics with code like that :
```python
(df
.apply([
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
lambda x: ...,
], axis=1)
)
```
My problem is that it took **more than 5 min** to do the calculation (!!!!).
Is there a way to do it better ? Multiprocessing but don't know how to structure it ?
Thx | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38037/events | https://github.com/pandas-dev/pandas/issues/38037 | 749,673,072 | MDU6SXNzdWU3NDk2NzMwNzI= | 38,037 | KeyError in datetime pandas when accessing with datetime.date | {
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... | open | false | null | [] | null | 5 | 2020-11-24T12:40:34Z | 2021-11-18T00:13:05Z | null | NONE | null | I've just upgrade Big Sur 11.0.1 and I'm facing some issue while trying to execute a notebook. Weird because it worked on Catalina. Could anyone help ?
```
import numpy as np
import pandas as pd
import yfinance as yf
# In[56]:
#parameters
RISKY_ASSET = 'ADBE'
START_DATE = '2017-01-01'
END_DATE = '2020-07-31'
# In[57]:
#Download data:
df = yf.download(RISKY_ASSET, start=START_DATE, end=END_DATE, adjusted=True)
# In[58]:
#daily return
adj_close = df['Adj Close']
returns = adj_close.pct_change().dropna()
print(f'Average return: {100 * returns.mean():.2f}%')
returns.plot(title=f'{RISKY_ASSET} returns: {START_DATE} - {END_DATE}')
# In[59]:
#Split the data into training and test sets:
train = returns['2017-01-01':'2020-06-30']
test = returns['2020-07-01':'2020-07-31']
# In[60]:
#parameters of the simulation:
T = len(test)
N = len(test)
S_0 = adj_close[train.index[-1].date()]
N_SIM = 1000
mu = train.mean()
sigma = train.std()
```
**And here the errors:**
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-7-9316c025c623> in <module>
2 T = len(test)
3 N = len(test)
----> 4 S_0 = adj_close[train.index[-1].date()]
5 N_SIM = 1000
6 mu = train.mean()
~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py in __getitem__(self, key)
880
881 elif key_is_scalar:
--> 882 return self._get_value(key)
883
884 if is_hashable(key):
~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py in _get_value(self, label, takeable)
987
988 # Similar to Index.get_value, but we do not fall back to positional
--> 989 loc = self.index.get_loc(label)
990 return self.index._get_values_for_loc(self, loc, label)
991
~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
620 else:
621 # unrecognized type
--> 622 raise KeyError(key)
623
624 try:
KeyError: datetime.date(2019, 12, 31) `
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https://api.github.com/repos/pandas-dev/pandas/issues/38038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38038/events | https://github.com/pandas-dev/pandas/issues/38038 | 749,678,978 | MDU6SXNzdWU3NDk2Nzg5Nzg= | 38,038 | REGR: Performance regression on RollingGroupby | {
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} | 9 | 2020-11-24T12:49:16Z | 2020-12-01T09:12:37Z | 2020-12-01T09:12:37Z | NONE | null | pd.DataFrame({'a': np.random.randn(10000000), 'b': 1}).groupby('b').rolling(3).mean() is approximately 10x slower between 1.0.5 and 1.1.x | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38039/events | https://github.com/pandas-dev/pandas/issues/38039 | 749,781,680 | MDU6SXNzdWU3NDk3ODE2ODA= | 38,039 | Is possible pass `Series` to `groupby.head` ? | {
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... | open | false | null | [] | null | 1 | 2020-11-24T14:59:50Z | 2021-08-14T05:53:21Z | null | NONE | null | From this [answer](https://stackoverflow.com/a/64988556/2901002) working returned first top groups from another column:
data = {'month': ['Jan','Jan','Jan','Jan','Feb','Feb','Feb','Feb','Feb','Feb'],
'userid': ['2345','2345','2345','2345', '2345','2345','2345','5678', '5678', '5678'],
'cntr': [3,3,3,3,1,1,1,2,2,2]}
df = pd.DataFrame(data = data, columns = ['month','userid','cntr'])
df1 = df.groupby(['month','userid']).head(df.cntr.astype(int))
print (df1)
month userid cntr
0 Jan 2345 3
1 Jan 2345 3
2 Jan 2345 3
4 Feb 2345 1
7 Feb 5678 2
8 Feb 5678 2
If different values is used first value of group:
data = {'month': ['Jan','Jan','Jan','Jan','Feb','Feb','Feb','Feb','Feb','Feb'],
'userid': ['2345','2345','2345','2345', '2345','2345','2345','5678', '5678', '5678'],
'cntr': [2,3,3,3,1,1,1,2,2,2]}
df = pd.DataFrame(data = data, columns = ['month','userid','cntr'])
df1 = df.groupby(['month','userid']).head(df.cntr.astype(int))
print (df1)
month userid cntr
0 Jan 2345 2
1 Jan 2345 3
2 Feb 2345 1
3 Feb 5678 2
4 Feb 5678 2
---
If it is valid way, why is not mentioned it in documentation, [link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.head.html)?
If it is not valid, why not rase error?
Thank you.
If necessary:
print (pd.show_versions())
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 7
Version : 6.1.7601
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : Slovak_Slovakia.1250
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200925
Cython : 0.29.21
pytest : 6.1.0
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.4
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: 0.9.0
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
None | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38040/events | https://github.com/pandas-dev/pandas/pull/38040 | 749,855,024 | MDExOlB1bGxSZXF1ZXN0NTI2NjAzMjUw | 38,040 | BUG: Series constructor drops nanoseconds of Timedelta scalar | {
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... | closed | false | null | [] | null | 4 | 2020-11-24T16:22:30Z | 2021-03-22T20:24:55Z | 2020-12-11T08:49:23Z | CONTRIBUTOR | null | - [x] closes #38032
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 10 | 2020-11-24T17:26:35Z | 2020-11-25T15:28:59Z | 2020-11-25T03:58:19Z | MEMBER | null | There are failures I'm getting locally since homebrew bumped up to py39. I'm not seeing them on the CI, but they _did_ appear around the same time as these [different](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=48423&view=logs&j=2d7fb38a-2053-50f3-a67c-09f6e91d3121&t=449937cc-3d50-56b5-5662-e489f41f1268) unexplained pytables failures started showing up. | {
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... | open | false | null | [] | null | 2 | 2020-11-24T17:29:44Z | 2020-11-28T17:16:18Z | null | MEMBER | null | xref #9772
```
df = pd.DataFrame({i:pd.Series(np.random.normal(size=10),
index=range(10)) for i in range(11)})
g = df.groupby(['a']*6+['b']*5, axis=1)
g.apply(lambda x : x.sum())
```
now raises, but used to give the (perhaps) surprising output
```
a b
0 -0.381070 NaN
1 -1.214075 NaN
2 -1.496252 NaN
3 3.392565 NaN
4 -0.782376 NaN
5 1.306043 NaN
6 NaN -1.772334
7 NaN 4.125280
8 NaN 1.992329
9 NaN 4.283854
10 NaN -4.791092
```
A fix for this (today and previously) would be to pass `axis=1` into the call to sum, but again I think that is viewed as unintuitive. In https://github.com/pandas-dev/pandas/issues/9772#issuecomment-723498819 I argued:
> ...when pandas feeds a group of values into the UDF, they are _not_ transposed. It seems reasonable to me to argue that they should be, but one technical hurdle here is what happens with a frame where the columns are different dtypes. Upon transposing, you now have columns of mixed dtypes, which are coerced to object type. So upon transposing the result back you lose type information. Since the UDF can return anything, there is no way to reliably determine that the resulting dtypes should be.
>
> Of course, an argument against transposing the group when passing it to the UDF is that this would be a rather large change for what seems to me to be of little value.
A few counter points that I've realized in the meantime:
- The case of multiple dtypes seems to me to be a very minor one, to the point of insignificance. Is there an example of a (somewhat natural) function that is applied to multiple dtypes where the resulting dtype does not get coerced correctly? I've played around with this in the code, the only such example in the tests is the identify function.
- This is notably not how `groupby(..., axis=1).transform` works, nor is it how apply/transform/agg with no groupby and `axis=1` work. These methods all feed in the row as a Series so that supplying `axis=1` results in an error.
I'm now of the opinion that transposing the inputs and results is more maintainable and easier to grok for users. | {
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} | 3 | 2020-11-24T17:40:31Z | 2020-12-06T14:03:54Z | 2020-11-28T17:46:03Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @simonjayhawkins
For the added type-ignore, the attribute `inferred_freq` is added dynamically onto the index instance. | {
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} | 1 | 2020-11-24T17:58:15Z | 2020-12-06T14:03:54Z | 2020-11-26T16:17:44Z | MEMBER | null | - [ ] closes #xxxx
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Closure of #22046 allows removing the workaround. | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2020-11-24T19:18:09Z | 2020-12-18T23:05:35Z | 2020-12-18T23:05:35Z | NONE | null | ```python
import pandas as pd
from pandas.tseries.offsets import CDay, BDay
idx = pd.bdate_range("2010/02/01","2010/02/15", freq='6H')
t1 = idx + BDay(offset=pd.Timedelta(3, unit='H'))
t2 = idx + CDay(offset=pd.Timedelta(3, unit='H'))
t1.equals(t2)
False
```
#### Problem description
BDay does not seem to be offsetting when a timedelta is supplied.
#### Expected Output
True
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.12.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-514.6.1.el7.x86_64
Version : #1 SMP Wed Jan 18 13:06:36 UTC 2017
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
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 : 7.16.1
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 0 | 2020-11-24T19:30:11Z | 2020-11-26T18:55:15Z | 2020-11-26T17:37:58Z | CONTRIBUTOR | null | - [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Remove (manually reviewed) repeated words from documentation/comments as well as wrong usage of `it's` (replaced by `its` where appropriated). | {
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} | 1 | 2020-11-24T22:48:40Z | 2020-11-27T08:15:07Z | 2020-11-26T17:43:44Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Tracemalloc is a standard tool for tracing of memory consumption in Python. Until now khash-maps were invisible for this tool.
This PR follows more or less numpy's approach (see https://github.com/numpy/numpy/commit/03534ec90dba2bcdcd649be64be57939dde4c6f5) for adding tracemalloc-support.
This PR fixes also silly mistakes from #37920 (see https://github.com/pandas-dev/pandas/pull/38048/commits/3291ed198432a80eb559382fbf9a35cb6a8ad749) and the somewhat imprecise implementation of `sizeof` for `XXXHashTable`-classes.
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2020-11-25T02:21:50Z | 2020-12-02T23:28:04Z | 2020-12-02T23:28:04Z | MEMBER | null | ```
index = pd.Index(range(3)).repeat(2)
>>> index.drop(1)
Traceback (most recent call last):
[...]
pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
It is using Index.get_indexer, which is only for uniques indexes. | {
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} | 0 | 2020-11-25T02:26:54Z | 2020-11-26T18:48:00Z | 2020-11-26T17:56:18Z | MEMBER | null | - [ ] closes #xxxx
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There's a check in get_indexer that is missing from get_indexer_non_unique, which this adds. This is going to make some de-duplication easier.
Hard to find a user-facing example bc indexing with all-bool indexer we treat it as a mask and not a list of labels. The closest I got to a user-facing example was in Series.drop, but that ran in to #38051. | {
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} | 2 | 2020-11-25T02:51:39Z | 2020-11-29T15:59:10Z | 2020-11-29T15:59:10Z | NONE | null | - [YES] I have checked that this issue has not already been reported.
- [YES] I have confirmed this bug exists on the latest version of pandas.
- [NO] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
idx1 = pd.MultiIndex.from_tuples([(False, 1.0),
(False, 10.0),
(True, 1.0),
(True, 10.0)])
s1 = pd.Series((0, 1, 2, 3), index = idx1)
s1.at[(False, 1.0)] # returns '0', expected output
idx2 = pd.MultiIndex.from_tuples([(False,), (True,)])
s2 = pd.Series((0, 1), index = idx2)
s2.at[False] # returns a pandas.Series with one element
```
#### Problem description
As per the user guide, I expect `at[]` to return a scalar. However, when I index a `Series` with a `MultiIndex` with only one level, `at[]` returns a `Series` with a single element. A `Series` with a multi-level `MultiIndex` works as expected.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-54-generic
Version : #60-Ubuntu SMP Fri Nov 6 10:37:59 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 5 | 2020-11-25T03:20:37Z | 2020-11-26T15:34:43Z | 2020-11-26T00:30:57Z | MEMBER | null | It's a footgun.
BTW I expect the CI to fail until #38020 goes in, which will [demonstrate](https://github.com/pandas-dev/pandas/pull/38020#discussion_r529159757) that there is a test case that uses MultiIndex. | {
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- [ ] tests added / passed
Checking if the recent cleanup of the `tests/window` directory had any impact.
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} | 17 | 2020-11-25T09:03:16Z | 2020-12-08T14:36:41Z | 2020-12-01T09:12:37Z | MEMBER | null | Closes #38038
TODO: fix corner cases, add benchmark, .. | {
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} | 1 | 2020-11-25T10:27:28Z | 2020-11-25T14:17:44Z | 2020-11-25T14:15:11Z | 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
>>> df1 = pd.DataFrame({'a':[1, 2, 3]}, index=['a','b','c'])
>>> df1.to_parquet('abc/one.parquet')
>>> df2 = pd.DataFrame({'a':[4, 5, 6]})
>>> df2.to_parquet('abc/two.parquet')
>>> df3 = pd.DataFrame({'a':[7, 8, 9]})
>>> df3.to_parquet('abc/three.parquet')
>>> pd.read_parquet('abc/')
a
a 1
b 2
c 3
NaN 7
NaN 8
NaN 9
NaN 4
NaN 5
NaN 6
```
#### Problem description
When we are reading multiple parquet files using `pd.read_parquet` and one of them has a different index than other two then there are `NaN` values in the rest of Index positions, is this a desired behavior?
#### Expected Output
Ideally I would expect all Index objects to be appended, i.e., we'd have some duplicate Index values in this case.
```python
>>> import pandas as pd
>>> df1 = pd.DataFrame({'a':[1, 2, 3]}, index=['a','b','c'])
>>> df1.to_parquet('abc/one.parquet')
>>> df2 = pd.DataFrame({'a':[4, 5, 6]})
>>> df2.to_parquet('abc/two.parquet')
>>> df3 = pd.DataFrame({'a':[7, 8, 9]})
>>> df3.to_parquet('abc/three.parquet')
>>> pd.read_parquet('abc/')
a
a 1
b 2
c 3
0 7
1 8
2 9
0 4
1 5
2 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
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 : 1.0.1
pytables : 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 | 2020-11-25T10:33:24Z | 2020-11-25T11:51:18Z | 2020-11-25T11:51:02Z | MEMBER | null | Backport PR #37039 | {
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} | 4 | 2020-11-25T10:58:25Z | 2020-11-25T21:56:10Z | 2020-11-25T21:55:53Z | NONE | null | Hey everybody,
so I ran into something odd last night.
I had two dataframes: dataframe_original and the dataframe_copy.
Whenever I changed the values in a column of dataframe_copy the code changed the values in dataframe_original.
I had the original one as a backup and assign it every iteration to a local copy.
I displayed the output of the original dataframe (so not the one I attempted to change) but it showed the changes were made on the original one, not the copied one. For assigning the original frame to the copied one I tried all these approaches:
1) local_df.loc[local_df.index, local_df.columns] = df.tail(l)
2) local_df = pd.DataFrame(data = df.tail(l))
3) local_df = pd.DataFrame(df.tail(l))
4) local_df = df
But no matter what, it did not actually copy the frame, it just created a reference to the original one.
I assume this is not how the constructor is supposed to work?
Thanks for your time.
---
#### Code Sample
```
import pandas as pd
#### Data
d = {'col1': [1, 1, 1], 'col2': [1, 1, 1], 'col3': [1, 1, 1]}
df = pd.DataFrame(data = d)
#### Limits for the iteration
xr = 2
yr = 2
#### Value to add
v = 1
#### Length
l = 2
#### Loop
local_df = pd.DataFrame(df.tail(l))
list_of_df = []
for x in range(xr):
for y in range(yr):
local_df.loc[local_df.index, local_df.columns] = df.tail(l)
local_df["col1"] = local_df["col1"] + v
list_of_df.append(local_df)
display(df)
```
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Versions & Sys:
macOS Mojave 10.14.6
Python 3.7.3
Pandas 1.1.4
ipython 7.4.0
_anaconda_depends 2019.03
anaconda-client .7.2
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https://api.github.com/repos/pandas-dev/pandas/issues/38062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38062/events | https://github.com/pandas-dev/pandas/pull/38062 | 750,766,455 | MDExOlB1bGxSZXF1ZXN0NTI3NDAxODA1 | 38,062 | DOC: move info docs to DataFrameInfo | {
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] | open | false | null | [] | null | 12 | 2020-11-25T12:12:54Z | 2021-10-04T14:11:41Z | null | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Precursor for #37320
Move ``info`` docstring from ``pandas/core/frame.py`` to class ``DataFrameInfo`` using ``doc`` decorator.
@simonjayhawkins need your help here as discussed previously. | {
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"description": "np.nan, ... | open | false | null | [] | null | 4 | 2020-11-25T12:37:42Z | 2021-08-14T05:55:16Z | null | NONE | null | ### Code Sample
This works as expected. Comparing nan with nan evaluates to False.
```python
>>> import pandas as pd
>>> import numpy as np
>>> s1 = pd.Series(np.nan)
>>> s1.eq(s1)
0 False
dtype: boolean
```
However, this does not work as expected. Comparing nan with nan evaluates to nan.
```python
>>> import pandas as pd
>>> import numpy as np
>>> s1 = pd.Series(np.nan).astype(pd.StringDtype())
>>> s1.eq(s1)
0 <NA>
dtype: boolean
```
### Problem description
As in other programming languages I would expect the comparison between nan values for equality evaluates to False. In the first case, this works as expected. However, for the second it does not. I read the docs for the [Nullable Boolean data type](https://pandas.pydata.org/pandas-docs/stable/user_guide/boolean.html). It implements the Kleene Logic for Boolean data type. So, the second case is expected behavior, too. The issue is even explicitly stated in the docs. It is just not very consistent between the two cases.
I searched for similar bug reports and was not sure if they are directly related. For reference please look here:
- [https://github.com/pandas-dev/pandas/issues/20442](https://github.com/pandas-dev/pandas/issues/20442)
- [https://github.com/pandas-dev/pandas/issues/36941](https://github.com/pandas-dev/pandas/issues/36941)
### Expected behavior
I would expect that the two cases behave consistently. In which way, is not so important for me.
### Output of pd.show_versions()
<details>
<summary>Details</summary>
```
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
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 : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
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} | 4 | 2020-11-25T14:19:30Z | 2020-11-26T12:34:02Z | null | NONE | null | #### Is your feature request related to a problem?
Yes. I would like to make a test of a function that is then applied on a rolling object. Making that test on just one window of the rolling object would be convenient.
#### Describe the solution you'd like
Someting similar to get_group for a groupby object. This would be something like : rolling.get_window(0) for first window for example.
#### API breaking implications
I do not know of any.
#### Describe alternatives you've considered
#### Additional context
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https://github.com/pandas-dev/pandas/milestone/79
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https://api.github.com/repos/pandas-dev/pandas/issues/38067 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38067/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38067/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38067/events | https://github.com/pandas-dev/pandas/issues/38067 | 750,998,859 | MDU6SXNzdWU3NTA5OTg4NTk= | 38,067 | DOC: Include missing holiday observance rules | {
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"id... | open | false | null | [] | null | 3 | 2020-11-25T17:25:35Z | 2021-08-14T05:55:56Z | null | NONE | null | #### Location of the documentation
[Timeseries Holiday](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#holidays-holiday-calendars) section in doc/source/user_guide/timeseries.rst.
#### Documentation problem
The list of available holiday observance rules is incomplete - several available observances are missing.
#### Suggested fix for documentation
The following observances should be added to the documentation because they are already available:
- next_workday
- previous_workday
- before_nearest_workday
- after_nearest_workday
Before I examined `holiday` via TAB-completion I had written my own version of weekend_to_monday; others may likewise re-invent these wheels if they're not documented.
(Updated to remove "weekend_to_monday" because it does the same thing as "next_monday") | {
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] | closed | false | null | [] | null | 2 | 2020-11-25T18:32:41Z | 2021-03-02T15:54:21Z | 2020-12-21T21:37:43Z | MEMBER | null | motivated by getting np.delete and np.repeat working, had to implement a few others to get the tests passing.
If we go down this path, I plan to incrementally add support for others, some of which (e.g. vstack) will avoid some object-dtype casting.
cc @TomAugspurger @shoyer @seberg any suggestions to clean the implementation, particularly the fallback and the unknown-args comments? | {
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} | 3 | 2020-11-25T19:57:28Z | 2020-11-25T22:25:55Z | 2020-11-25T21:06:43Z | NONE | null | It's currently quite difficult to rename a single column (or row) in a multi-indexed DataFrame. The problem is illustrated also in this [SO article](https://stackoverflow.com/questions/54693188).
The current approach requires at least 3 steps:
```
df = pd.DataFrame([[1,2,3],[3,4,5],[5,6,7], [7,8,9]])
df.columns = pd.MultiIndex.from_tuples([('i','a'),('i','b'),('ii','a')])
# Alternative 1
i = df.columns.get_loc(('i','b'))
cols = df.columns.values
cols[i] = ('i','c')
df.columns = pd.MultiIndex.from_tuples(cols)
# Alternative 2
df.columns = df.columns.to_flat_index()
df = df.rename(columns={('i','b'):('i','c')})
df.columns = pd.MultiIndex.from_tuples(df.columns)
```
#### Enhancement:
In presence of a MultiIndex, `rename()` should support the tupelized representation of a column.
```
df = pd.DataFrame([[1,2,3],[3,4,5],[5,6,7], [7,8,9]])
df.columns = pd.MultiIndex.from_tuples([('i','a'),('i','b'),('ii','a')])
# Rename using tuples:
df.rename(columns={('i','b'):('ii','b')})
# Rename using current column name (equivalent to 1a)
col = df[('i','b')]
df.rename(columns={col.name: ('ii','b')})
```
This would improve the consistency of the API, since many methods accept a tuple to address the name of a multi-index element:
```
df[('i','a')]
df.drop(('i','a'), axis=1)
df.pop(('i','a'), axis=1)
...
```
#### API breaking implications
I regard this improvement as an extension of the current API.
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} | 8 | 2020-11-25T20:11:11Z | 2020-12-03T22:14:42Z | 2020-12-02T23:28:05Z | MEMBER | null | - [x] closes #38051
- [x] closes #33494
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
@jbrockmendel This should deal with duplicates. For MultiIndex sometimes a slice with stepzize greater than zero was given, which dropped to many elements | {
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} | 2 | 2020-11-25T21:19:03Z | 2020-12-02T18:43:00Z | 2020-12-02T18:38:54Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This was easier than thought. Should I add a whatsnew note for this? | {
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} | 0 | 2020-11-25T22:15:45Z | 2020-11-26T18:48:31Z | 2020-11-26T17:57:57Z | MEMBER | null | Trying to clear up the confusing:
common.py defines Base
test_base.py imports Base from common
test_common does not
and try to get away from the create_index usage to just use the index fixture
Small steps.
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} | 7 | 2020-11-25T22:43:24Z | 2020-12-29T18:13:45Z | 2020-12-29T18:09:25Z | MEMBER | null | - [x] closes #37682
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This sits on top of #38021 | {
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} | 6 | 2020-11-25T23:10:32Z | 2020-11-26T15:35:34Z | 2020-11-26T15:35:30Z | CONTRIBUTOR | null | - [x] closes #35803
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry - N/A I believe
Get rid of an unnecessary copy. | {
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When working with data that contains a lot of references, pd.merge seems to be the best approach.
If you need to follow more than 1 reference to get where you need, this quickly gets verbose.
To solve this problem, I suggest a higher level wrapping for merge, that is focused around being able to follow multiple references. Some potential names are:
```py
pd.link([*dataframes]) # The one I use in the examples below
pd.follow([*dataframes])
pd.traverse([*dataframes])
```
#### Describe the solution you'd like
Example implementation below:
```py
from functools import reduce
import pandas as pd
def link(dataframes, how='left'):
"""
Follow references in between pandas dataframes and series.
Parameters:
-----------
`values` is an iterable of pd.DataFrame or pd.Series.
`how` decides how the internal pd.merge will be performed.
"""
def _link(x, y):
# Support for passing in pandas series
if type(x) == pd.Series:
x = x.to_frame()
if type(y) == pd.Series:
y = y.to_frame()
return pd.merge(x, y, left_on=list(x.columns), right_index=True, how=how)[y.columns]
return reduce(_link, dataframes)
```
### Example
#### And for the variables used:
<details><summary>user_to_session_id</summary>
</details>
<details><summary>session_id_to_article_id</summary>
</details>
<details><summary>article_id_to_price_usd</summary>
</details>
---
<details><summary>Code</summary>
```python
sessions = pd.DataFrame({
'session_id': ['a', 'b', 'c', 'd'],
'user': ['jack', 'jack', 'john', 'steve'],
})
user_to_session_id = sessions.set_index('user')
orders = pd.DataFrame({
'session_id': ['a', 'b', 'c', 'c'],
'article_id': ['x', 'x', 'y', 'x']
})
session_id_to_article_id = orders.set_index('session_id')
articles = pd.DataFrame({
'article_id': ['x', 'y'],
'price_usd': [25, 50]
})
article_id_to_price_usd = articles.set_index('article_id')
link([
user_to_session_id,
session_id_to_article_id,
article_id_to_price_usd
])
```
</details>
### Notes
This could be created to only support pd.Series - but then this prevents us from merging multi-index frames/series. For merging multi-index data, we need a multi-column frame to match the index. | {
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} | 1 | 2020-11-26T11:17:31Z | 2020-11-26T16:43:45Z | 2020-11-26T16:18:51Z | CONTRIBUTOR | null | `DataFrame.ix` is removed from the code base, so rephrase a section in the docs that was referencing it. | {
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} | 1 | 2020-11-26T13:07:29Z | 2020-11-26T13:49:10Z | null | CONTRIBUTOR | null | in #37864 cross join was added
let's add some benchmarks | {
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} | 2 | 2020-11-26T13:18:21Z | 2020-11-27T07:24:04Z | 2020-11-27T07:24:04Z | MEMBER | null | From https://pandas.pydata.org/speed/pandas/#timeseries.AsOf.time_asof_single?p-constructor='Series'&commits=52a17259-24e881d4&x-axis=commit&Cython=0.29.21&python=3.8
Snippet extracted from ASV:
```python
N = 10000
rng = pd.date_range(start="1/1/1990", periods=N, freq="53s")
s = pd.Series(np.random.randn(N), index=rng)
dates = pd.date_range(start="1/1/1990", periods=N * 10, freq="5s")
date = dates[0]
%timeit s.asof(date)
```
On pandas 1.1 this takes around 20µs, on master I get around 110µs
Commit range indicated by ASV is https://github.com/pandas-dev/pandas/compare/52a17259...24e881d4
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} | 2 | 2020-11-26T13:24:34Z | 2020-11-26T19:13:32Z | 2020-11-26T19:13:32Z | CONTRIBUTOR | null | Use the compiler setup from conda-forge inside a conda environment. The compilers in conda-forge can be different and slightly incompatible with the system ones causing some headaches. Also this hopefully helps with initial setup of new contributors.
Is this worth a whatsnew entry?
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-11-26T13:27:48Z | 2020-12-03T13:54:21Z | 2020-11-26T18:08:12Z | CONTRIBUTOR | null | - [ ] closes #37763
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2020-11-26T13:30:44Z | 2020-11-27T01:01:26Z | 2020-11-27T01:01:26Z | MEMBER | null | ASV shows a *gigantic* regression (14629.25x) in a certain `replace` benchmark: https://pandas.pydata.org/speed/pandas/#replace.ReplaceList.time_replace_list?python=3.8&Cython=0.29.21&p-inplace=True&commits=07559156-dbee8fae
The simplified case is:
```
In [5]: df = pd.DataFrame({"A": 0, "B": 0}, index=range(4 * 10 ** 7))
In [6]: %timeit df.replace([np.inf, -np.inf], np.nan)
100 ms ± 6.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # 1.1
1.2 s ± 31.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # master
```
Compared to 1.1, I don't see such a huge difference, but still a decent slowdown (x10).
Now, in this case, we have integer columns, but trying to replace infinity, which of course can never be present. So maybe before we had some shortcut for that.
This also seems quite a cornercase, though. So not sure how critical the regression is. | {
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} | 12 | 2020-11-26T13:34:40Z | 2020-12-01T16:41:28Z | 2020-11-27T20:03:58Z | CONTRIBUTOR | null | closes #33315
closes #33314
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2020-11-26T14:15:02Z | 2020-11-26T15:44:49Z | 2020-11-26T15:37:27Z | MEMBER | null | A follow-up on https://github.com/pandas-dev/pandas/pull/36179#discussion_r494830867 (cc @jbrockmendel). Attempt to clarify what is actually deprecated, remove the `:okwarning:` for several cases in the docs that were not actually deprecated, and updated to use the preferred `.loc` for some others. | {
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} | 4 | 2020-11-26T15:05:37Z | 2020-11-30T00:41:16Z | 2020-11-29T17:45:41Z | CONTRIBUTOR | null | - [x] closes #35269
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/38090 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38090/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38090/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38090/events | https://github.com/pandas-dev/pandas/issues/38090 | 751,685,949 | MDU6SXNzdWU3NTE2ODU5NDk= | 38,090 | CI/TST: suppress http debug from tests outputs | {
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} | 0 | 2020-11-26T15:43:03Z | 2020-11-28T17:33:56Z | 2020-11-28T17:33:56Z | CONTRIBUTOR | null | examples from https://travis-ci.org/github/pandas-dev/pandas/jobs/746072525
We have this output coming from our fixtures (we startup a http endpoint to test things), but the output is totally unecessary / distracting.
```
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /pandas-test/items.jsonl HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /pandas-test/simple_dataset.feather HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /cant_get_it/tips%231.csv HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /cant_get_it/tips.csv HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /cant_get_it/tips.csv.gz HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /cant_get_it/tips.csv.bz2 HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /cant_get_it/items.jsonl HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "PUT /cant_get_it/simple_dataset.feather HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "HEAD /pandas-test/tips.csv HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "GET /pandas-test/tips.csv HTTP/1.1" 206 -
.127.0.0.1 - - [26/Nov/2020 13:35:25] "GET /pandas-test?list-type=2&prefix=&delimiter=%2F&encoding-type=url HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "POST /pandas-test?delete HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "DELETE /pandas-test HTTP/1.1" 204 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "GET /cant_get_it?list-type=2&prefix=&delimiter=%2F&encoding-type=url HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [26/Nov/2020 13:35:25] "POST /cant_get_it?delete HTTP/1.1" 200 -
``` | {
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} | 2 | 2020-11-26T15:47:08Z | 2020-11-26T17:08:38Z | null | CONTRIBUTOR | null | from a recent build on master, these builds are *very* slow. Yes we have 140k tests, but 30m is a lot of time (esp considering the azure builds are 2x faster).
needs some investigation on what is happening.
- https://travis-ci.org/github/pandas-dev/pandas/jobs/746072524
- https://travis-ci.org/github/pandas-dev/pandas/jobs/746072525
cc @pandas-dev/pandas-core | {
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} | 0 | 2020-11-26T15:56:30Z | 2020-11-26T15:56:30Z | null | CONTRIBUTOR | null | ERROR: type should be string, got "https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=48611&view=logs&j=4b4d380c-92b5-58cf-5b80-2c6c9b1d5805&t=8eab1181-1335-58ed-7ed5-a271d9d86005\r\n\r\nwe want to see if this is a bug / add a test case\r\n\r\n```\r\n2020-11-26T11:02:32.6835667Z ##[section]Starting: Test\r\n2020-11-26T11:02:32.6968461Z ==============================================================================\r\n2020-11-26T11:02:32.6969827Z Task : Bash\r\n2020-11-26T11:02:32.6970534Z Description : Run a Bash script on macOS, Linux, or Windows\r\n2020-11-26T11:02:32.6971261Z Version : 3.177.2\r\n2020-11-26T11:02:32.6971826Z Author : Microsoft Corporation\r\n2020-11-26T11:02:32.6972383Z Help : https://docs.microsoft.com/azure/devops/pipelines/tasks/utility/bash\r\n2020-11-26T11:02:32.6973012Z ==============================================================================\r\n2020-11-26T11:02:32.9619865Z Generating script.\r\n2020-11-26T11:02:32.9824762Z [command]\"C:\\Program Files\\Git\\bin\\bash.exe\" --noprofile --norc -c pwd\r\n2020-11-26T11:02:33.0802003Z /d/a/_temp\r\n2020-11-26T11:02:33.0846216Z \r\n2020-11-26T11:02:33.0876225Z ========================== Starting Command Output ===========================\r\n2020-11-26T11:02:33.0885510Z [command]\"C:\\Program Files\\Git\\bin\\bash.exe\" --noprofile --norc /d/a/_temp/d7f72ca5-7653-4d39-b14e-c4a3a640dc91.sh\r\n2020-11-26T11:02:33.7933378Z pytest -m \"not slow and not network\" -n auto --dist=loadfile -s --strict --durations=30 --junitxml=test-data.xml pandas --ignore=pandas/tests/window/ --ignore=pandas/tests/plotting/\r\n2020-11-26T11:03:46.1230792Z ##[warning]Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\r\n2020-11-26T11:03:46.1290301Z ##[warning]Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\r\n2020-11-26T11:03:46.1292192Z ##[warning]Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\r\n2020-11-26T11:03:46.1293979Z ##[warning]Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\r\n2020-11-26T11:03:46.1295207Z ============================= test session starts =============================\r\n2020-11-26T11:03:46.1295831Z platform win32 -- Python 3.7.9, pytest-6.1.1, py-1.9.0, pluggy-0.13.1\r\n2020-11-26T11:03:46.1296368Z rootdir: D:\\a\\1\\s, configfile: setup.cfg, testpaths: pandas\r\n2020-11-26T11:03:46.1296847Z plugins: hypothesis-5.41.3, azurepipelines-0.8.0, forked-1.3.0, xdist-2.1.0\r\n2020-11-26T11:03:46.1297375Z gw0 I / gw1 I\r\n2020-11-26T11:03:46.1297683Z gw0 [98481] / gw1 [98481]\r\n2020-11-26T11:03:46.1297932Z \r\n2020-11-26T11:19:01.9006050Z Result Attachments will be stored in LogStore\r\n2020-11-26T11:19:01.9506376Z Run Attachments will be stored in LogStore\r\n2020-11-26T11:19:03.1155850Z ##[error]1 test(s) failed, 98481 test(s) collected.\r\n2020-11-26T11:19:03.1158156Z Skipping uploading of coverage data.\r\n2020-11-26T11:19:03.1158771Z \r\n2020-11-26T11:19:03.1159206Z ================================== FAILURES ===================================\r\n2020-11-26T11:19:03.1159714Z ____________________________ test_shift_across_dst ____________________________\r\n2020-11-26T11:19:03.1160220Z [gw1] win32 -- Python 3.7.9 C:\\Miniconda\\envs\\pandas-dev\\python.exe\r\n2020-11-26T11:19:03.1160827Z \r\n2020-11-26T11:19:03.1161077Z @given(gen_yqm_offset)\r\n2020-11-26T11:19:03.1161361Z > def test_shift_across_dst(offset):\r\n2020-11-26T11:19:03.1161564Z \r\n2020-11-26T11:19:03.1161828Z pandas\\tests\\tseries\\offsets\\test_offsets_properties.py:107: \r\n2020-11-26T11:19:03.1162225Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n2020-11-26T11:19:03.1163012Z C:\\Miniconda\\envs\\pandas-dev\\lib\\site-packages\\hypothesis\\core.py:661: in execute_once\r\n2020-11-26T11:19:03.1164457Z % (test.__name__, text_repr[0])\r\n2020-11-26T11:19:03.1164917Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n2020-11-26T11:19:03.1165284Z \r\n2020-11-26T11:19:03.1165669Z self = <hypothesis.core.StateForActualGivenExecution object at 0x000001EC3C0E6D08>\r\n2020-11-26T11:19:03.1166349Z message = 'Hypothesis test_shift_across_dst(offset=<-4 * BYearEnds: month=1>) produces unreliable results: Falsified on the first call but did not on a subsequent one'\r\n2020-11-26T11:19:03.1168377Z \r\n2020-11-26T11:19:03.1168728Z def __flaky(self, message):\r\n2020-11-26T11:19:03.1169153Z if len(self.falsifying_examples) <= 1:\r\n2020-11-26T11:19:03.1169544Z > raise Flaky(message)\r\n2020-11-26T11:19:03.1170217Z E hypothesis.errors.Flaky: Hypothesis test_shift_across_dst(offset=<-4 * BYearEnds: month=1>) produces unreliable results: Falsified on the first call but did not on a subsequent one\r\n2020-11-26T11:19:03.1170780Z \r\n2020-11-26T11:19:03.1171182Z C:\\Miniconda\\envs\\pandas-dev\\lib\\site-packages\\hypothesis\\core.py:866: Flaky\r\n2020-11-26T11:19:03.1171722Z --------------------------------- Hypothesis ----------------------------------\r\n2020-11-26T11:19:03.1172169Z Falsifying example: test_shift_across_dst(\r\n2020-11-26T11:19:03.1172581Z offset=<-4 * BYearEnds: month=1>,\r\n2020-11-26T11:19:03.1172904Z )\r\n2020-11-26T11:19:03.1173635Z Unreliable test timings! On an initial run, this test took 638.78ms, which exceeded the deadline of 500.00ms, but on a subsequent run it took 1.55 ms, which did not. If you expect this sort of variability in your test timings, consider turning deadlines off for this test by setting deadline=None.\r\n2020-11-26T11:19:03.1174495Z \r\n2020-11-26T11:19:03.1174995Z You can reproduce this example by temporarily adding @reproduce_failure('5.41.3', b'AAsAAQQA') as a decorator on your test case\r\n2020-11-26T11:19:03.1175561Z ============================== warnings summary ===============================\r\n2020-11-26T11:19:03.1176092Z C:\\Miniconda\\envs\\pandas-dev\\lib\\site-packages\\matplotlib\\__init__.py:107\r\n2020-11-26T11:19:03.1176625Z C:\\Miniconda\\envs\\pandas-dev\\lib\\site-packages\\matplotlib\\__init__.py:107\r\n```" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38093 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38093/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38093/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38093/events | https://github.com/pandas-dev/pandas/issues/38093 | 751,696,567 | MDU6SXNzdWU3NTE2OTY1Njc= | 38,093 | Extend check for inconsistent pandas namespace usage | {
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"following_url": "https://api.github.com/users/kevinetienne/following{/other_user}"... | null | 4 | 2020-11-26T15:59:04Z | 2021-02-14T14:00:35Z | 2021-02-14T14:00:35Z | MEMBER | null | xref #38076 - it can probably be extended beyond checking for classes.
Steps would be:
1. change `PATTERN` in `scripts/check_for_inconsistent_pandas_namespace.py` to
```
PATTERN = r"""
(
(?<!pd\.)(?<!\w) # check class_name doesn't start with pd. or character
(\w+)\( # match DataFrame but not pd.DataFrame or tm.makeDataFrame
.* # match anything
pd\.\2\( # only match e.g. pd.DataFrame
)|
(
pd\.(\w+)\( # only match e.g. pd.DataFrame
.* # match anything
(?<!pd\.)(?<!\w) # check class_name doesn't start with pd. or character
\4\( # match DataFrame but not pd.DataFrame or tm.makeDataFrame
)
"""
```
and save.
2. run `pre-commit run inconsistent-namespace-usage --all-files`, check the error messages, and fix them up
Here are the files which need fixing:
```
- [ ] pandas/tests/arrays/floating/test_to_numpy
- [ ] pandas/tests/extension/decimal/test_decimal
- [ ] pandas/tests/io/test_sql
- [ ] pandas/tests/series/test_constructors
- [ ] pandas/tests/arrays/test_datetimes
- [ ] pandas/tests/frame/test_stack_unstack
- [ ] pandas/tests/arrays/boolean/test_comparison
- [ ] pandas/tests/internals/test_internals
- [ ] pandas/tests/arrays/masked/test_arrow_compat
- [ ] pandas/tests/reshape/merge/test_multi
- [ ] pandas/tests/indexing/test_datetime
- [ ] pandas/tests/scalar/timestamp/test_unary_ops
```
This can probably all be one in a single PR.
3. To check you've fixed up a file completely, running
```
pre-commit run inconsistent-namespace-usage --files <file.py>
```
should return "PASSED". | {
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} | 2 | 2020-11-26T16:46:02Z | 2020-11-27T20:47:55Z | 2020-11-27T20:12:02Z | MEMBER | null | Closes #36003 | {
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} | 2 | 2020-11-26T19:18:41Z | 2020-11-27T18:42:48Z | 2020-11-26T23:04:36Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
It appears we are heading this general direction in pandas imports in tests. | {
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Confirming that this does the trick. | {
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closes #38086, this gets us back to (very slightly ahead of) 1.1.4 performance for the cases there, will leave to @jorisvandenbossche whether that is enough to close the issue. | {
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} | 1 | 2020-11-26T21:26:43Z | 2020-11-29T16:17:11Z | 2020-11-29T15:59:57Z | MEMBER | null | - [x] closes #14586
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The main thing being changed here is `CategoricalIndex._concat` to better match the behavior of all the other `FooIndex._concat` methods. Everything else being changed here is just cleanup that this makes possible.
cc @jorisvandenbossche | {
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For most Series types, this was properly raising the deprecation warning about 2D being deprecated, but for Series with datetimetz values, this started raising an AssertionError instead of only raising the warning.
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} | 3 | 2020-11-26T21:42:54Z | 2021-07-04T21:37:34Z | 2021-07-04T21:37:34Z | 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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
'num_wings': [2, 0, 0, 0],
'num_specimen_seen': [10, 2, 1, 8]},
index=['falcon', 'dog', 'spider', 'fish'])
rng = np.random.default_rng()
df.sample(random_state=rng)
```
Output:
```raw
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-34-d27bfa21cee7> in <module>
4 index=['falcon', 'dog', 'spider', 'fish'])
5 rng = np.random.default_rng()
----> 6 df.sample(random_state=rng)
C:\tools\miniconda3\envs\pandas-dev\lib\site-packages\pandas\core\generic.py in sample(self, n, frac, replace, weights, random_state, axis)
4914
4915 # Process random_state argument
-> 4916 rs = com.random_state(random_state)
4917
4918 # Check weights for compliance
C:\tools\miniconda3\envs\pandas-dev\lib\site-packages\pandas\core\common.py in random_state(state)
429 return np.random
430 else:
--> 431 raise ValueError(
432 (
433 "random_state must be an integer, array-like, a BitGenerator, "
ValueError: random_state must be an integer, array-like, a BitGenerator, a numpy RandomState, or None
```
#### Problem description
Best practice since Numpy 1.17 for creating random number generators is to use `numpy.random.default_rng`:
https://numpy.org/doc/stable/reference/random/index.html?highlight=random#quick-start. This returns a `numpy.random.Generator` object. It should not be necessary to extract the underlying `BitGenerator` or convert it manually to a [legacy](https://numpy.org/doc/stable/reference/random/legacy.html#legacy) `numpy.random.RandomState`.
Closest issues I can find are https://github.com/pandas-dev/pandas/issues/28440 (mentions performance only) and https://github.com/pandas-dev/pandas/issues/32503. Code is here:
https://github.com/pandas-dev/pandas/blob/78d149897201cb55b403f6f84fc541a3d7aa86ad/pandas/core/common.py#L366
Current simplest workaround is as follows. I've confirmed that it advances `rng`.
```python
df.sample(random_state=rng.bit_generator)
```
#### Expected Output
Code above should not throw an error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.6.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 : en_US.UTF-8
LOCALE : English_United Kingdom.1252
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
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 : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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} | 4 | 2020-11-26T21:44:00Z | 2020-11-29T16:04:01Z | 2020-11-29T15:59:11Z | MEMBER | null | - [x] closes #38053
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I think if we are at this place and len(values) is 1, we want to return a scalar instead of a Series. This happens only because loc is a slice (0,1) with one MultiIndex level instead of a scalar.
Should I create a file to move all MultiIndex at tests to the MultiIndex folder? | {
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"descript... | closed | false | null | [] | null | 2 | 2020-11-27T03:19:05Z | 2021-08-14T16:54:58Z | 2021-08-14T16:54:52Z | 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
>>> df1 = pd.DataFrame({'a':[1, 2, 3]})
>>> df1.to_parquet('abc/one.parquet')
>>> df2 = pd.DataFrame({'a':[4, 5, 6]})
>>> df2.to_parquet('abc/two.parquet')
>>> df3 = pd.DataFrame({'a':[7, 8, 9]})
>>> df3.to_parquet('abc/three.parquet')
>>> pd.read_parquet('abc/')
a
0 1
1 2
2 3
3 7
4 8
5 9
6 4
7 5
8 6
```
#### Problem description
When there are multiple parquet files with identical/non-identical RangeIndex objects I think doing `pd.read_parquet` should ideally be appending all the RangeIndex objects of each parquet file.
#### Expected Output
```python
>>> pd.read_parquet('abc/')
a
0 1
1 2
2 3
0 7
1 8
2 9
0 4
1 5
2 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
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 : 1.0.1
pytables : 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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} | 5 | 2020-11-27T04:11:58Z | 2020-12-02T02:35:02Z | 2020-12-02T01:51:11Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Index._is_comparable_dtype is limited because it misses a few cases: object dtype, and sub-dtypes within categorical. This implements Indes._should_compare, which handles those correctly. It then implements `_get_indexer_non_comparable` for cases in which we short-circuit non-comparable dtypes.
As a proof of concept, this then uses _should_compare and _get_indexer_non_comparable for `PeriodIndex.get_indexer`.
The behavior change, which this tests, is one that IIUC is a bug. That is, when we do get_indexer with a method and non-comparable dtypes, we should raise instead of return all minus-ones.
If implemented, we'll be able to use _should_compare to simplify all of the get_indexer, get_indexer_non_unique, and set op methods. | {
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} | 3 | 2020-11-27T05:06:06Z | 2020-12-01T22:45:17Z | 2020-12-01T22:05:06Z | MEMBER | null | - [x] closes #26796
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Picking up #31048 | {
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] | closed | false | null | [] | null | 2 | 2020-11-27T05:41:19Z | 2020-11-27T08:19:56Z | 2020-11-27T08:16:40Z | 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
# Your code here
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3]})
df["a"].attrs["key"] = "value"
display(df["a"].attrs)
# {'key': 'value'}
_ = df.copy()
display(df["a"].attrs)
# {}
```
#### Problem description
Calling copy on a dataframe probably shouldn't modify it or it's contents (especially those exposed through public attributes) in any way.
I would also ague the copy should keep these attributes, but that's more related to #35425
#### Expected Output
The second `display(df["a"].attrs)` should print: `{'key': 'value'}`
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.7.4
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.4
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
```
</details>
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-11-27T12:48:37Z | 2020-11-29T18:06:28Z | 2020-11-29T18:06:24Z | CONTRIBUTOR | null | - [x] closes #36850
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List of follow-up TODO items:
* [x] Update the `convert_dtypes` method to also return nullable floats (one question here: what with floats that are all "integer"-like? I think we want to keep returning nullable int for that, but we might want to add a parameter controlling that behaviour?) -> #38117
* [x] Arithmetic operations with IntegerArray/BooleanArray can now return a FloatingArray instead of numpy float array(eg division with Int64 should give Float64 -> #38178
* [ ] Ufuncs on IntegerArray/BooleanArray can return FloatingArray, ufuncs on mixture of IntegerArray/FloatArray
* [x] Consolidate some tests that are common for integer/boolean/float
* For example the `value_counts` test could easily be shared and moved to masked/ (see the ones I added in https://github.com/pandas-dev/pandas/pull/38178)
* [ ] Deduplicate more things between the Array classes and move to BaseMaskedArray (eg the arithmetic / comparison ops can probably be moved with a bit of refactoring)
* [x] `__from_arrow__` -> https://github.com/pandas-dev/pandas/pull/38411
* [ ] ufuncs -> https://github.com/pandas-dev/pandas/pull/38412
* [ ] `coerce_to_array`, but this will probably require some refactoring
* [ ] Honor the formatting options (eg precision) #39336
* [x] Fix membership test for np.nan (`np.nan in arr`), see https://github.com/pandas-dev/pandas/pull/37867#discussion_r531079439
And of course, we also still have the NA vs NaN discussion to further flesh out (https://github.com/pandas-dev/pandas/issues/32265), but this issue is for the direct practical TODO items.
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} | 2 | 2020-11-27T15:56:23Z | 2020-12-02T01:49:46Z | 2020-12-02T01:46:07Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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its going to take a few passes to get the behavior consistent across all subclasses. once done, we can de-duplicate the boilerplate. | {
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In this PR, I just refactored how the test cases are defined and didn't change any actual content. | {
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There is one potentially corner case: what with floats that are all "integer"-like? I think we want to keep returning nullable int for that, at least by default, and that is what I did now in this PR But we might want to add a parameter controlling that behaviour? (but that can also be added later on if there is demand for it) | {
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- [ ] closes #38121
- [ ] closes #38077
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/38118"
} |
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