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- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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No
#### Describe the solution you'd like
`pd.DataFrame.shift`/`pd.Series.shift` could alternatively accept a list of ints or a collection of numbers, and the result would be a dataframe with one column per shift. This would make it very convenient to e.g. create multiple lagged versions of a column for time series problems.
E.g.,
```
time_series_df[my_time_feature].shift([-3, -2, -1, 0, 1, 2, 3])
```
would give me a dataframe with 7 columns, each shifted versions of `time_series_df[my_time_feature]`.
#### API breaking implications
The first input to `df.shift` would be overloaded to also accept a list of ints.
#### Describe alternatives you've considered
Alternatives are very simple to do, you just iterate over the lags and concatenate or join.
This would just be a handy shortcut.
#### Additional context
I'd in principle be open to implementing this myself, although I've never contributed to pandas. | {
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- [x] tests added / passed
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} | 7 | "2021-11-13T18:34:43Z" | "2021-11-17T19:32:54Z" | null | CONTRIBUTOR | null | Transferring data from the 6th column to the first.
This should resolve issue #44379
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<s>Also fixed the FloatingArray.equals bug on older numpys</s>no longer needed, so reverted | {
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```
class InPlace(enum.Enum):
only = 0 # never copy; raise instead of casting
attempt = 1 # inplace if you can, cast on fallback
never = 2 # always make a copy
```
I think I've seen numpy discussions on doing something similar, forget which kwarg that was about. | {
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} | 1 | "2021-11-13T21:34:27Z" | "2021-11-19T21:31:24Z" | "2021-11-16T00:55:59Z" | MEMBER | null | - [x] closes #44021
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} | 1 | "2021-11-13T22:13:18Z" | "2021-11-15T07:32:43Z" | "2021-11-14T02:09:07Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
cc @jorisvandenbossche these were introduced in #14536 | {
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] | open | false | null | [] | null | 4 | "2021-11-13T22:21:24Z" | "2021-11-15T05:48:43Z" | null | NONE | null | ### pandas.core.groupby.GroupBy.rank does not have a numeric_only argument like DataFrame.rank()
I have a DataFrame with several statistics from baseball teams across different years. I want to rank each team in each statistic, grouped by season. Every column is numeric, except for the teamID column which is an object type containing the names of each team as a string. My code looks something like this
```python
stats = pd.read_csv('stats.csv')
ranked = stats.groupby('yearID').rank(method='average', ascending=False)
ranked['teamID'] = stats['teamID']
```
Since it is GroupBy.rank() I can't pass the `numeric_only` argument and that means I have to reassign `ranked['teamID']` to the original column. I also cannot do
```python
ranked = stats.groupby(['yearID', 'teamID']).rank(...)
```
because that would give everybody a rank of 1.
Is there a reason that `numeric_only` is included in DataFrame.rank() but not GroupBy.rank(). Could it be added to GroupBy.rank()?
Then I could code it like this, which would be easier.
```python
```python
stats = pd.read_csv('stats.csv')
ranked = stats.groupby('yearID').rank(method='average', ascending=False, numeric_only=False)
```
I am just a hobbyist and so I don't know much about the implementation of these methods which means there may be something I am completely ignoring, or another more efficient way to do it. If so I would appreciate some enlightenment about what I am missing. Thanks!
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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- [x] closes #44147
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} | 1 | "2021-11-14T00:03:35Z" | "2021-11-14T04:41:46Z" | "2021-11-14T02:11:15Z" | MEMBER | null | xref #44300. Since that added a .cpt data file, we should also exclude it from the manifest.
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} | 2 | "2021-11-14T04:11:49Z" | "2021-11-20T15:59:00Z" | "2021-11-20T15:58:54Z" | CONTRIBUTOR | null | - [x] closes #44079
- [x] tests added / passed
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} | 3 | "2021-11-14T04:59:24Z" | "2021-11-14T14:13:28Z" | "2021-11-14T10:48:44Z" | MEMBER | null | Please see bpo 20028. https://github.com/python/cpython/pull/28705.
This breaks our nightlies, but doesn't on regular CI. It should show up pretty soon in regular CI, once the conda-forge folks add it.
This means unfortuanately that I have no way to test this.
Can someone with with the perms on the MacPython repo please trigger the nightlies manually to see if that fixes the builds after this? Thanks.
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} | 5 | "2021-11-14T12:32:35Z" | "2021-11-17T06:48:35Z" | "2021-11-17T02:09:41Z" | CONTRIBUTOR | null | relevant for `DataFrame.to_csv` and `Series.to_csv` with `compression='zip'`
- [x] closes #39465
- also relevant for #26023
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
This fix is similar-in-spirit to #40387, which has been abandoned.
## Before / After
```py
from pandas import pd
df = pd.DataFrame()
df.to_csv('../test.csv.zip')
```
### Before
```
> unzip -l test.csv.zip
Archive: test.csv.zip
Length Date Time Name
--------- ---------- ----- ----
3 2021-11-14 13:39 ../test.csv.zip
--------- -------
3 1 file
```
Notice the `..` in the path - bad! And, of course, that the file _inside_ the zip file is also called `test.csv.zip`.
### After
```
> unzip -l test.csv.zip
Archive: test.csv.zip
Length Date Time Name
--------- ---------- ----- ----
3 2021-11-14 13:44 test.csv
--------- -------
3 1 file
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} | 3 | "2021-11-14T14:13:42Z" | "2021-11-15T14:56:55Z" | "2021-11-15T14:46:18Z" | CONTRIBUTOR | null | - [] closes #xxxx
- [] tests added / passed
- [] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [] whatsnew entry
From #28602
`pandas.testing.assert_categorical_equal: Parameter "check_dtype" description should finish with "."
pandas.testing.assert_categorical_equal: Parameter "obj" description should finish with "."` | {
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} | 2 | "2021-11-14T14:17:07Z" | "2021-11-14T16:28:49Z" | "2021-11-14T16:21:58Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
Followup to #44398. Should get docs build to green. cc @jbrockmendel
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Replaced Timestamp.now () with pd.Timestamp (2021, 1, 1, 12) | {
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} | 1 | "2021-11-14T15:13:14Z" | "2021-11-14T16:32:41Z" | "2021-11-14T16:32:38Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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have seen this on multiple PRs. Maybe something recently added causes this to timeout. | {
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} | 3 | "2021-11-14T18:51:57Z" | "2021-11-15T12:38:02Z" | "2021-11-15T12:12:19Z" | MEMBER | null | Reverts pandas-dev/pandas#44362
closes #44343 | {
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we should do the same | {
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} | 1 | "2021-11-15T02:44:11Z" | "2021-11-15T02:55:40Z" | "2021-11-15T02:55:40Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
def f(df):
f'{df}'
df[['c', 'd']]=np.zeros([len(df),2])
df=pd.DataFrame([[1,2],[1,3]],columns=['a','b'])
dfg=df.groupby(['a','b']).apply(f)
```
### Issue Description
1) groupby 2 columns and "apply" a function
2) set value to multiple columns in this "apply" function
3) in the second group invoking this "apply" function, on access of input df, it report an error:
```Length of values (2) does not match length of index (4)```
### Expected Behavior
groupby apply function should not keep changes of index into next iteration
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.9.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : zh_CN
LOCALE : Chinese (Simplified)_China.936
pandas : 1.3.4
numpy : 1.21.3
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 57.4.0
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 : 7.29.0
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 : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
idx_no_nan = pd.Index([1, 2], dtype=np.float64)
idx_nan = pd.Index([1, 2, np.nan])
print("dtype=np.float64")
target = pd.Index([1, np.nan], dtype=np.float64)
print(idx_no_nan.get_indexer(target))
print(idx_no_nan.get_indexer_non_unique(target))
print(idx_nan.get_indexer(target))
print(idx_nan.get_indexer_non_unique(target))
print("dtype='object'")
target = pd.Index([1, np.nan], dtype='object')
print(idx_no_nan.get_indexer(target))
print(idx_no_nan.get_indexer_non_unique(target))
print(idx_nan.get_indexer(target))
print(idx_nan.get_indexer_non_unique(target))
```
### Issue Description
Annotated script output
```
dtype=np.float64
[ 0 -1]
(array([ 0, -1]), array([1])) # ← expected
[0 2]
(array([ 0, -1]), array([1])) # ← would expect (array([0, 2]), array([], dtype=int64)
dtype='object'
[ 0 -1]
(array([0, 0, 1]), array([], dtype=int64)) # ← would expect (array([0, -1]), array([1]))
[0 2]
(array([0, 2]), array([], dtype=int64) # ← expected
```
### Expected Behavior
As the indexes and the targets are unique I would expect the output of `get_indexer()` and `get_indexer_non_unique()` to be equivalent.
```
dtype=np.float64
[ 0 -1]
(array([ 0, -1]), array([1]))
[0 2]
(array([0, 2]), array([], dtype=int64)
dtype='object'
[ 0 -1]
(array([0, -1]), array([1]))
[0 2]
(array([0, 2]), array([], dtype=int64)
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : a07561e5a33b78cb54d163e81dd7a4444ca41554
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-90-lowlatency
Version : #101-Ubuntu SMP PREEMPT Fri Oct 15 20:57:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.4.0.dev0+1117.ga07561e5a3
numpy : 1.21.4
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 58.5.3
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.24.2
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.2
lxml.etree : 4.6.4
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.11.0
fastparquet : 0.7.1
gcsfs : 2021.11.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 6.0.0
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.7.2
sqlalchemy : 1.4.26
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 1 | "2021-11-15T10:53:10Z" | "2021-11-15T15:33:58Z" | "2021-11-15T15:06:51Z" | MEMBER | null | Discovered while trying to fix test failures in https://github.com/pandas-dev/pandas/pull/40482. Currently normal division with a float scalar works, but floordiv doesn't (returns garbage):
```
In [42]: arr = pd.timedelta_range(0, periods=3)
In [43]: pd.Series(arr) / 2.0
Out[43]:
0 0 days 00:00:00
1 0 days 12:00:00
2 1 days 00:00:00
dtype: timedelta64[ns]
In [44]: pd.Series(arr) // 2.0
Out[44]:
0 0 days 00:00:00
1 55681 days 08:53:22.404319232
2 55733 days 11:53:22.031689728
dtype: timedelta64[ns]
In [45]: pd.__version__
Out[45]: '1.3.3'
```
It's only the scalar case; dividing with eg float array already works:
```
In [46]: pd.Series(arr) // np.array([2.0]*3)
Out[46]:
0 0 days 00:00:00
1 0 days 12:00:00
2 1 days 00:00:00
dtype: timedelta64[ns]
```
I suppose the current code uses the workaround of `asi8` and casting back to timedelta, because numpy didn't support floordiv in the past (didn't check which version started to implement this, will see what CI says)
- [ ] whatsnew entry
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] | open | false | null | [] | null | 4 | "2021-11-15T12:06:03Z" | "2021-11-21T04:23:28Z" | null | NONE | null | #### Is your feature request related to a problem?
This may be a quite big thing. But I's like pandas to be able to execute a MDX query to an OLAP server and read the results into a DataFrame.
#### Describe the solution you'd like
I imagine there could be a function similar to pd.read_sql
#### API breaking implications
May require a new library as a pandas dependency?
#### Describe alternatives you've considered
It's possible to actually embed a MDX query into a SQL and simply use read_sql. It's also possible to use a library like adodbapi and transform the output into a DataFrame myself. But both feel kind of cumbersome.
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} | 8 | "2021-11-15T15:07:54Z" | "2021-11-20T16:08:53Z" | "2021-11-20T16:08:53Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
from pandas.core.window.indexers import BaseIndexer
from typing import Union, Tuple, Optional
import numpy as np
import pandas as pd
class RollingFixedOffsetVariableWindowIndexer(BaseIndexer):
def get_window_bounds(self, num_values: int = 0, min_periods: Optional[int] = None, center: Optional[bool] = None,
closed: Optional[str] = None) -> Tuple[np.ndarray, np.ndarray]:
# Every hour
start = np.arange(0, num_values, 60 * 60)
# Every hour + day
end = start + 60 * 60 * 24
return start, end
ser = pd.Series(np.arange(100000), index=pd.date_range(start='2020', freq='1s', periods=100000))
idxr = RollingFixedOffsetVariableWindowIndexer(window_size=60 * 60 * 24)
out = ser.rolling(idxr, min_periods=10).mean()
```
### Issue Description
(Maybe related to #43267 ? Couldn't tell.)
I'm trying to create a custom `BaseIndexer` for a `.rolling` operation. The extraction of the window is quick and efficient, yet when I attempt to perform various operations on it I get the seg-fault error:
> Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
I have created an MWE for this behavior using a fixed-indexer (that carries the same name). The original indexer was supposed to produce a rolling-operation with overlaps, so the above indexer is similar in the sense that it creates a 1-day windows 1-hour apart for a one-second index.
### Expected Behavior
Hmmm... Not to have a seg-fault error?
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.8.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-77-generic
Version : #86~18.04.1-Ubuntu SMP Fri Jun 18 01:23:22 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.18.5
pytz : 2018.3
dateutil : 2.8.1
pip : 21.1.3
setuptools : 57.4.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
None
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
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} | 7 | "2021-11-15T15:33:16Z" | "2021-11-18T18:59:54Z" | "2021-11-18T18:59:51Z" | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/44466#discussion_r749430496
I still need to add a test that would actually catch this | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
df = pd.read_csv('filtered1.csv')
df = df.pivot_table(
index='X', columns='Y', values='T').reset_index()
df
```
### Issue Description
Some of the values in the pivot table aren't in the file used.
I attached the file I'm using and a screenshot of my current output
[filtered1.csv](https://github.com/pandas-dev/pandas/files/7540864/filtered1.csv)
### Expected Behavior
I should get a pivot table with only the values in the file.
### Installed Versions
<details>
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.2-zen1-1-zen
Version : #1 ZEN SMP PREEMPT Fri, 12 Nov 2021 19:22:07 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.3
dateutil : 2.8.2
pip : 20.3.4
setuptools : 57.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.4
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.29.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-11-15T18:36:51Z" | "2021-11-16T00:19:31Z" | "2021-11-16T00:07:26Z" | 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_codebase.html#pre-commit) for how to run them
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Looks like a pass to de-duplicate the test_business_day and test_custom_business_day files would be worthwhile | {
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} | 2 | "2021-11-15T20:25:34Z" | "2021-11-16T06:26:29Z" | "2021-11-15T23:57:08Z" | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/44450
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
Some of the current `param` definitions are causing the same benchmark to run more than once without changes. Reorganizing the benchmarks such that the `param` combinations avoid the redundancy.
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- [ ] tests added / passed
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Bit of a shot in the dark. | {
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} | 0 | "2021-11-15T23:59:31Z" | "2021-11-15T23:59:31Z" | null | CONTRIBUTOR | null | i think this is worth it to make the Check build very fast and then this build (at 30m is ok)
xref #44450 | {
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use typing.Union to combine type "FilePathOrBuffer[AnyStr]" and "None", instead of " | " can avoid it.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 0 | "2021-11-16T03:28:35Z" | "2021-11-17T02:42:20Z" | "2021-11-17T02:04:46Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
We have to run out of these eventually, right? right? | {
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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_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
Following PR #44331, I added a release note for the 1.4.0 release according to @simonjayhawkins recommendation.
Happy to move it into another paragraph if needed. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/44481 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44481/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44481/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44481/events | https://github.com/pandas-dev/pandas/issues/44481 | 1,054,832,627 | I_kwDOAA0YD84-33fz | 44,481 | BUG: get_indexer_non_unique() does not find NaNs. | {
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] | open | false | null | [] | null | 0 | "2021-11-16T12:37:36Z" | "2021-11-16T12:39:33Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
idx = pd.Index([1, 2, np.nan])
target = pd.Index([1, np.nan])
print(idx.get_indexer(target))
print(idx.get_indexer_non_unique(target))
```
### Issue Description
The script outputs:
```
[0 2]
(array([ 0, -1]), array([1]))
```
So `get_indexer()` correctly found the NaN value at position 2 but `get_indexer_non_unique()` has not found the NaN value anywhere.
This is one aspect of #44465 which needs to be split due to two different root causes.
### Expected Behavior
Expected output
```
[0 2]
(array([ 0, 2]), array([], dtype=int64))
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 700be617eb567fb4ab82aa8151d5c4ee02c22b95
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-90-lowlatency
Version : #101-Ubuntu SMP PREEMPT Fri Oct 15 20:57:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.4.0.dev0+1132.g700be617eb
numpy : 1.21.4
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 58.5.3
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.24.2
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.2
lxml.etree : 4.6.4
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.11.0
fastparquet : 0.7.1
gcsfs : 2021.11.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 6.0.0
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.7.2
sqlalchemy : 1.4.26
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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] | open | false | null | [] | null | 0 | "2021-11-16T12:51:37Z" | "2021-11-16T12:51:37Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
idx = pd.Index([1.0, 2.0])
target = pd.Index([1, np.nan], dtype='object')
print(idx.get_indexer(target))
print(idx.get_indexer_non_unique(target))
```
### Issue Description
The script outputs
```
[ 0 -1]
(array([0, 0, 1]), array([], dtype=int64))
```
It boils down to the fact that in `object` arrays with NaNs in them the are not sorted in as expected as discussed in numpy/numpy#15499.
This is one aspect of #44465 which needs to be split due to two different root causes.
### Expected Behavior
Expected output
```
[ 0 -1]
(array([ 0, -1]), array([1]))
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 700be617eb567fb4ab82aa8151d5c4ee02c22b95
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-90-lowlatency
Version : #101-Ubuntu SMP PREEMPT Fri Oct 15 20:57:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.4.0.dev0+1132.g700be617eb
numpy : 1.21.4
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 58.5.3
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.24.2
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.2
lxml.etree : 4.6.4
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.11.0
fastparquet : 0.7.1
gcsfs : 2021.11.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 6.0.0
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.7.2
sqlalchemy : 1.4.26
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/44483 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44483/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44483/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44483/events | https://github.com/pandas-dev/pandas/pull/44483 | 1,054,883,944 | PR_kwDOAA0YD84ul5yp | 44,483 | BUG: fix get_indexer_non_unique() with 'object' targets with NaNs (#4… | {
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} | [] | open | false | null | [] | null | 0 | "2021-11-16T13:29:33Z" | "2021-11-19T19:00:36Z" | null | CONTRIBUTOR | null | …4482)
numpy.searchsorted() does not handle NaNs in 'object' arrays as
expected (numpy/numpy#15499). Therefore we cannot search NaNs using binary
search. So we use binary search only for targets without NaNs.
- [x] closes #44482
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
This does also put the whatsnew of #44404 to the correct place. | {
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] | open | false | null | [] | null | 0 | "2021-11-16T15:20:32Z" | "2021-11-16T15:20:32Z" | null | MEMBER | null | Currently, the `to_numpy()` implementation on the datetimelike arrays is quite simple (basically `np.array(EA, dtype)`, which calls `__array__` which returns/casts the underlying numpy array). This means that it basically follows numpy's casting rules, while for `astype` we have a whole bunch of custom conversion rules.
Some examples:
```python
In [1]: arr = pd.date_range("2012-01-01", periods=3).array
In [2]: arr
Out[2]:
<DatetimeArray>
['2012-01-01 00:00:00', '2012-01-02 00:00:00', '2012-01-03 00:00:00']
Length: 3, dtype: datetime64[ns]
# conversion to string (-> different formatting)
In [3]: arr.astype(str)
Out[3]: array(['2012-01-01', '2012-01-02', '2012-01-03'], dtype=object)
In [4]: arr.to_numpy(str)
Out[4]:
array(['2012-01-01T00:00:00.000000000', '2012-01-02T00:00:00.000000000',
'2012-01-03T00:00:00.000000000'], dtype='<U48')
# conversion to float or timedelta (error vs working cast)
In [5]: arr.astype(float)
...
TypeError: Cannot cast DatetimeArray to dtype float64
In [6]: arr.to_numpy(float)
Out[6]: array([1.3253760e+18, 1.3254624e+18, 1.3255488e+18])
In [7]: arr.astype('timedelta64[ns]')
...
TypeError: Cannot cast DatetimeArray to dtype timedelta64[ns]
In [8]: arr.to_numpy('timedelta64[ns]')
Out[8]:
array([1325376000000000000, 1325462400000000000, 1325548800000000000],
dtype='timedelta64[ns]')
```
We might say: `to_numpy()` is for converting to a numpy array, so in that case it is fine to follow numpy's casting rules. But it would still be good to explicitly decide on this.
On the other hand it's also strange to have two different sets of rules (and the fact that it uses numpy's rules here is somewhat an implementation detail). | {
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] | open | false | null | [] | null | 0 | "2021-11-16T15:36:52Z" | "2021-11-16T15:39:03Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [x] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import numpy as np
import pandas as pd
def check_nan(df):
for entry in df.to_dict(orient='records'):
if any([isinstance(v, float) and np.isnan(v) for v in entry.values()]):
return True
return False
def generate_df():
return pd.DataFrame(dict(age=[5, 6, np.NaN],
born=[pd.NaT, pd.Timestamp('1939-05-27'),
pd.Timestamp('1940-04-25')],
name=['Alfred', 'Batman', ''],
toy=[None, 'Batmobile', 'Joker']))
def where_not_working():
df = generate_df()
df = df.where((pd.notnull(df)), None)
print('where not working', check_nan(df))
def replace_working():
df = generate_df()
df = df.replace({np.nan: None})
print('replace working', check_nan(df))
def replace_twice_not_working():
df = generate_df()
df = df.replace({np.nan: None})
df = df.replace({np.nan: None})
print('replace twice not working', check_nan(df))
def replace_odd_num_working():
df = generate_df()
df = df.replace({np.nan: None})
df = df.replace({np.nan: None})
df = df.replace({np.nan: None})
print('replace three times working', check_nan(df))
where_not_working()
replace_working()
replace_twice_not_working()
replace_odd_num_working()
```
### Issue Description
As shown in the example above:
dataframe.replace({np.nan: None}) works when it's applied to a dataframe once, 3 times, but not working (or "reverse" the change) when run twice, 4 times, etc.
### Expected Behavior
It should consistently have the result dataframe containing no np.nan, doesn't matter how many replace attempts have been made.
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-240.1.1.el8_3.x86_64
Version : #1 SMP Fri Oct 16 13:36:46 EDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None | {
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} | 2 | "2021-11-16T17:26:50Z" | "2021-11-19T17:33:15Z" | "2021-11-19T17:32:59Z" | NONE | null | This seems to be related to this [issue](https://github.com/pandas-dev/pandas/issues/28440) and this [merge](https://github.com/pandas-dev/pandas/pull/42243). But currently DataFrame.sample does not allow Generator objects for random_state.
## Rationale
As far as I can tell, the convention for numpy 1.21 random sampling, in their [documentation](https://numpy.org/doc/stable/reference/random/index.html) is to create Generators using numpy.random.default_rng, so creating BitGenerators directly seems a little awkward.
## Fix
This is a quick fix though because the generator has [field](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.bit_generator.html#numpy.random.Generator.bit_generator) to access the bit generator
`from numpy.random import default_rng`
`rng = default_rng()`
`bit_generator = rng.bit_generator` | {
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} | 2 | "2021-11-16T18:45:37Z" | "2021-11-20T21:14:07Z" | "2021-11-20T21:14:03Z" | CONTRIBUTOR | null | Hi!
This is extremely minor and i just quickly did it in codespaces but I ran into this a few times and I thought it might be helpful for others. | {
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- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
cc @stefansorgqc | {
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} | 0 | "2021-11-16T19:37:19Z" | "2021-11-20T07:28:42Z" | "2021-11-20T07:28:38Z" | CONTRIBUTOR | null | deprecation warning regarding ambiguous boolean of empty array (numpy)
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] | open | false | null | [] | null | 1 | "2021-11-16T20:08:31Z" | "2021-11-21T02:41:43Z" | null | NONE | null | #### Is your feature request related to a problem?
From what I've seen in python classes: Handling a huge data frame, especially beginners using `apply` on huge data frames don't know that it loads the whole frame while looping.
[Example](https://stackoverflow.com/a/62968313):
Consider following function and data frame
```python
import pandas as pd
def complex_function(x, y=0):
if x > 5 and x > y:
return 1
else:
return 2
df = pd.DataFrame(data={'col1': [1, 4, 6, 2, 7], 'col2': [6, 7, 1, 2, 8]})
```
For a greater data frame, consider performance differences with
```python
df['col1'] = df['col1'].apply(complex_function)
```
and the much less efficient
```
df['col1'] = df.apply(function(x) {complex_function(x['col1'])})
```
#### Describe the solution you'd like
Just a small warning that, if only one column is accessed within apply body. Something like:
```
Warning: Only one column seems to be accessed, causing performance issues
```
#### Describe alternatives you've considered
We just leave it as it is, no great differences for most users
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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_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 8 | "2021-11-17T00:01:27Z" | "2021-11-21T11:34:57Z" | "2021-11-20T22:42:54Z" | MEMBER | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
>>>-------------------------------------------------------------------------
Warning in /home/runner/work/pandas/pandas/doc/source/user_guide/io.rst at block ending on line 3033
Specify :okwarning: as an option in the ipython:: block to suppress this message
----------------------------------------------------------------------------
/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/charset_normalizer/api.py:91: UserWarning: Trying to detect encoding from a tiny portion of (11) byte(s).
warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))
<<<-------------------------------------------------------------------------
and
WARNING: failed to reach any of the inventories with the following issues:
intersphinx inventory 'https://www.statsmodels.org/devel/objects.inv' not fetchable due to <class 'requests.exceptions.ConnectionError'>: HTTPSConnectionPool(host='www.statsmodels.org', port=443): Max retries exceeded with url: /devel/objects.inv (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fef8dc72670>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution'))
```
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
This is yak-shaving the trails back to a bug in Series.where with nullable dtypes.
```
from pandas._libs.lib import *
arr = np.empty(10**6, dtype=bool).astype(object)
%timeit is_bool_array(arr)
8.87 ms ± 28.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
5.33 ms ± 11.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this issue exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this issue exists on the master branch of pandas.
### Reproducible Example
```
>>> import pandas as pd
>>> left = pd.DataFrame.from_dict({('A', 'a'): [0]})
>>> right = pd.DataFrame.from_dict({('B', 'b'): [1]})
>>> left.merge(right, how='cross')
PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
A B
a b
0 0 1
```
It is neither `left.columns` nor `right.columns` that has `_lexsort_depth == 0`. Some MultiIndex created during the merge is not sorted (and/or dropped without a level parameter). I don't know if this actually affects performance or is better described as a bug.
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-27-generic
Version : #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.4
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
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 : 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
### Prior Performance
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} | 1 | "2021-11-17T05:05:48Z" | "2021-11-20T18:02:43Z" | "2021-11-20T16:08:54Z" | MEMBER | null | - [x] closes #44470
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/44498 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44498/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44498/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44498/events | https://github.com/pandas-dev/pandas/issues/44498 | 1,055,895,726 | I_kwDOAA0YD84-77Cu | 44,498 | df[col].dtype==object reverts back to df[col].dtype==datetime upon df.replace({np.nan: None}) | {
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} | [] | open | false | null | [] | null | 0 | "2021-11-17T09:23:46Z" | "2021-11-17T09:23:46Z" | null | NONE | null | I explicitly coerce the dtype==datetime of a column containing NaT and datetimes (call it: DateTimeCol) to be of type object so that I can convert NaT to None (because Django complains about it). Then later during the workflow, when I have a need to replace all other NaN to None (because Django also compalins about it) using df.replace({np.nan: None}), the DateTimeCol is retransformed back into a type: datetime automatically without it explicit input and the NaT are present again which forces django to complain. Surely this is not a wanted behaviour.
`
# pre replace
0 None
1 None
2 None
3 None
4 None
...
163 2013-12-19 16:30:00+01:00
164 2013-12-19 16:30:00+01:00
165 2013-12-19 16:30:00+01:00
166 2013-12-19 16:30:00+01:00
167 2013-12-19 16:30:00+01:00
Name: Tempering DateTime, Length: 168, dtype: object
df.replace({np.nan: None})
# post replace
0 NaT
1 NaT
2 NaT
3 NaT
4 NaT
...
163 2013-12-19 16:30:00+01:00
164 2013-12-19 16:30:00+01:00
165 2013-12-19 16:30:00+01:00
166 2013-12-19 16:30:00+01:00
167 2013-12-19 16:30:00+01:00
Name: Tempering DateTime, Length: 168, dtype: datetime64[ns, Europe/Berlin]
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} | 2 | "2021-11-17T14:48:51Z" | "2021-11-20T16:15:51Z" | "2021-11-20T16:15:51Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({"a": ["x", "y", "z", "w"], "b": [True, False, False, True]}).astype({"b":"boolean"})
df.replace("x", "X")
```
### Issue Description
When trying to use `pandas.DataFrame.replace()` on a dataframe with several columns including boolean columns where the `boolean` (i.e. nullable) dtype has been set explicitly beforehand. When running the above snippet I get the following error:
```
File "C:\users\el_caminooooo\playground\venv/lib/python3.7/site-packages/pandas/core/arrays/boolean.py", line 265, in __init__
"values should be boolean numpy array. Use "
TypeError: values should be boolean numpy array. Use the 'pd.array' function instead
```
Is this a pandas bug?. Also please note both of the following work fine:
```python
import pandas as pd
df = pd.DataFrame({"a": ["x", "y", "z", "w"], "b": [True, False, False, True]}).astype({"b":"bool"}) # notice 'bool' instead of 'boolean'
df.replace("x", "X")
```
and:
```python
df = pd.DataFrame({"a": ["x", "y", "z", "w"], "b": [True, False, False, True]})
df.replace("x", "X")
```
### Expected Behavior
the replace should complete successfully
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.27.2.el7.x86_64
Version : #1 SMP Mon Jul 29 17:46:05 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.3.1
setuptools : 47.1.0
Cython : 0.29.23
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: 0.10.0
bs4 : None
bottleneck : None
fsspec : 2021.08.1
fastparquet : 0.7.0
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/44500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/44500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/44500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/44500/events | https://github.com/pandas-dev/pandas/pull/44500 | 1,056,250,690 | PR_kwDOAA0YD84uqQfi | 44,500 | TYP: changed variable new_pd_index to final_pd_index | {
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} | 3 | "2021-11-17T15:15:41Z" | "2021-11-17T17:41:11Z" | null | NONE | null | xref #37715
Changed variable new_pd_index to final_pd_index in line 2099
Handled mypy type ignore[assignment] error in pandas/io/pytables.py in line 2099
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} | 7 | "2021-11-17T17:30:53Z" | "2021-11-20T20:10:55Z" | "2021-11-20T20:10:36Z" | CONTRIBUTOR | null | - [x] xref #44341
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
Replacing Timestamp.now() in tests.
Left Timestamp.now () in the following tests:
```
scalar/timestamp/test_timestamp.py:290: compare(Timestamp.now(), datetime.now())
scalar/timestamp/test_timestamp.py:291: compare(Timestamp.now("UTC"), datetime.now(timezone("UTC")))
scalar/timestamp/test_timestamp.py:320: compare(Timestamp.now(), datetime.now())
scalar/timestamp/test_timestamp.py:321: compare(Timestamp.now("UTC"), datetime.now(tzutc()))
scalar/timestamp/test_constructors.py:480: ts_from_method = Timestamp.now()
scalar/timestamp/test_constructors.py:484: ts_from_method_tz = Timestamp.now(tz="US/Eastern")
```
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] | open | false | null | [] | null | 2 | "2021-11-17T22:06:04Z" | "2021-11-19T15:57:33Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
pd_td = pd.Timedelta(0)
np_td = np.timedelta64(0)
# One of these should be true
assert (pd_td == np_td and hash(pd_td) == hash(np_td)) or pd_td != np_td
```
### Issue Description
Ref: https://bugs.python.org/issue45832
I was prompted by @rhettinger to bring this up with you.
`pd.Timedelta` and `np.timedelta64` are in violation of the python [hash invariant](https://docs.python.org/3/glossary.html#term-hashable).
Specifically:
> Hashable objects which compare equal must have the same hash value.
### Expected Behavior
```py
# Should pass
assert (pd_td == np_td and hash(pd_td) == hash(np_td)) or pd_td != np_td
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.10.0.final.0
python-bits : 64
OS : Darwin
OS-release : 21.1.0
Version : Darwin Kernel Version 21.1.0: Wed Oct 13 17:33:23 PDT 2021; root:xnu-8019.41.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.1
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 58.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.29.0
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 : 6.0.0
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_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-11-18T01:58:29Z" | "2021-11-19T10:49:54Z" | "2021-11-19T10:49:42Z" | CONTRIBUTOR | null | - [x] closes #44304
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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} | 1 | "2021-11-18T02:34:08Z" | "2021-11-20T18:54:51Z" | "2021-11-20T18:14:50Z" | MEMBER | null | - [ ] closes #xxxx
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- [x] whatsnew entry
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} | 2 | "2021-11-18T07:24:31Z" | "2021-11-18T12:08:31Z" | "2021-11-18T11:58:36Z" | NONE | null | I have used pandas.to_sql statement to store data(200 rows X 8 columns) of csv file to postgres database but it just keeps on loading for a while, however it runs perfectly when I use if_exists='append'. I have checked everything but not able to find the issue.Can someone help ?
```
username = session['username']
user_path = os.path.join("user_data",username)
userdb = username + "DB.db"
con = sqlite3.connect(Path(user_path,userdb))
con.row_factory = sqlite3.Row
cur = con.cursor()
cur.execute("select * from data")
data = cur.fetchall()
con.close()
if request.method == 'POST':
uploadExcel = request.files['uploadExcel']
if uploadExcel.filename != '':
filepath = os.path.join(
user_path , uploadExcel.filename)
if os.path.exists(filepath):
flash("The file with the same name already exists !","danger")
else:
uploadExcel.save(filepath)
con = sqlite3.connect(Path(user_path,userdb))
cur = con.cursor()
cur.execute("insert into data(exceldata)values(?)",
(uploadExcel.filename,))
con.commit()
flash("Excel Sheet Uploaded Successfully", "success")
con = sqlite3.connect(Path(user_path,userdb))
con.row_factory = sqlite3.Row
cur = con.cursor()
cur.execute("select * from data")
data = cur.fetchall()
for val in data:
path = os.path.join(user_path, uploadExcel.filename)
print(path)
engine = sqlalchemy.create_engine("postgresql+psycopg2://postgres:falcon123@localhost:5432/SankovFAAS")
df = pd.read_csv(path,index_col=False)
print(df)
df.to_sql('user_data',engine,if_exists='replace', method='multi',index=False)
print("done")
con.close()
return render_template("ExcelUpload.html", data=data, username=session['username'], page_title=" Upload")
``` | {
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] | open | false | null | [] | null | 2 | "2021-11-18T08:13:34Z" | "2021-11-20T22:19:11Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame(columns = ["A", "B"])
col = df.columns.insert(-1, "C")
print(col)
df = pd.DataFrame( columns = col)
print(df.columns)
```
### Issue Description
the method index.insert does not allow to insert a value last.
`loc=-1` gives an insertion at the second last place
'loc=0=-0' gives an insertion at 1st place
### Expected Behavior
The method index.insert should follow the standard loc function and 'loc=-1' should insert the item last in the index
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.129+
Version : #1 SMP Wed Aug 18 19:58:18 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.3
pytz : 2021.3
dateutil : 2.8.2
pip : 20.0.2
setuptools : 45.2.0
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 : 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
None
</details>
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Changed variable name from vals to array_vals in pandas/core/index/multi.py: 705 and 706
To remove type: ignore[assignment] mypy error
Changed variable name from m to mapped_result in pandas/core/index/multi.py: 3121
Removed type: ignore[assignment] mypy error in line 3126 and 3131 | {
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
dates = pd.date_range("2020-01-01", "2020-01-02").values
availability = pd.DataFrame({'product': ['a', 'a', 'b', 'b'],
'timestamp': np.append(dates, dates),
'stock': [10, 5, 10, 10]
})
(availability.set_index('timestamp')
.groupby('product').diff()
)
```
### Issue Description
Using with `groupby(grouping_columns).diff()` leads to `grouping_columns` columns being missing in the output.
### Expected Behavior
Expected behaviour: having 'product' in the index.
```
stock
product timestamp
a 2020-01-01 NaN
2020-01-02 -5.0
b 2020-01-01 NaN
2020-01-02 0.0
```
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.8.10.final.0
pandas : 1.3.4
numpy : 1.20.3
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Changed variable name `repeats` to `ensured_repeats` pandas/core/indexes/multi.py: 2161 and 2167
Removed type[ignore] assignment and mypy error comment.
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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_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
Same yak-shaving as #44495 (which turned out to be a dead end for this particular yak, but still a perf bump) | {
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} | 2 | "2021-11-18T17:48:13Z" | "2021-11-18T18:33:25Z" | "2021-11-18T18:33:13Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame([NaN,*range(5),1000],columns=['col1'])
df.update(df.loc[:,df.columns != 'some_column'].pct_change(-1))
print(df)
```
### Issue Description
This code without `df.update` will make the last row of the dataset filled with `NaN` values, however when an `update` is made the row remains unchanged. is this a normal behavior of pandas ? shouldn't the row be updated to `Nan` ?
### Expected Behavior
return last row with `NaN`
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 21.1.0
Version : Darwin Kernel Version 21.1.0: Wed Oct 13 17:33:24 PDT 2021; root:xnu-8019.41.5~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.28.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
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
None
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] | closed | false | null | [] | null | 0 | "2021-11-18T19:06:52Z" | "2021-11-18T19:11:16Z" | "2021-11-18T19:11:16Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
Make a PR that launches the workflow
```
### Issue Description
I have been looking at updating some documentation and been pushing the PR for review. Although code was approved, there are still some checks that are not passing (and they are not for many other pull requests so my case does not seem to be isolated).
The error that appears is in the CI/Web and docs workflow:
```
/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
17 | #warning "Using deprecated NumPy API, disable it with " \
| ^~~~~~~
WARNING:
>>>-------------------------------------------------------------------------
Warning in /home/runner/work/pandas/pandas/doc/source/user_guide/io.rst at block ending on line 3033
Specify :okwarning: as an option in the ipython:: block to suppress this message
----------------------------------------------------------------------------
/home/runner/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/charset_normalizer/api.py:91: UserWarning: Trying to detect encoding from a tiny portion of (11) byte(s).
warn('Trying to detect encoding from a tiny portion of ({}) byte(s).'.format(length))
<<<-------------------------------------------------------------------------
```
which triggers a warning that is considered as an error since we pass the `--warnings-are-errors` in the `doc/make.py --warnings-are-errors | tee sphinx.log ; exit ${PIPESTATUS[0]}` command to build the documentation
### Expected Behavior
Tests should be passing without issues (has been passing in the past without issues, not sure what is breaking the tests now).
Latest changes on the file `doc/source/user_guide/io.rst` do not seem to impact this section
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : c2587f807cbaaf16585fa293e9b1ec41cef1b511
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+1158.gc2587f807c
numpy : 1.21.4
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 59.1.1
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.24.6
sphinx : 4.3.0
blosc : None
feather : None
xlsxwriter : 3.0.2
lxml.etree : 4.6.4
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.29.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.11.0
fastparquet : 0.7.1
gcsfs : 2021.11.0
matplotlib : 3.5.0
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 6.0.1
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.7.2
sqlalchemy : 1.4.27
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
nan = float('nan')
df = pd.DataFrame([
[1, 2, 1],
[1, 2, 2],
[2, nan, 3],
[2, nan, 4],
[3, 3, 5],],
columns=["a", "b", "c"])
gb = df.groupby(["a", "b"], dropna=False)
filtered = gb.filter(lambda x: len(x) > 1)
print(filtered)
# a b c
# 0 1 2.0 1
# 1 1 2.0 2
# Expecting:
# a b c
# 0 1 2.0 1
# 1 1 2.0 2
# 2 2 NaN 3
# 3 2 NaN 4
# Note that there is no problem with agg:
print()
agged = gb.agg(len)
print(agged)
# c
# a b
# 1 2.0 2
# 2 NaN 2
# 3 3.0 1
```
### Issue Description
When using `groupby()` and then applying a filter, if the group values include a `NaN`, those rows are *always dropped*! If you add `dropna=False` to the `filter` call, it just fills them with NaNs.
### Expected Behavior
`NaN`s should be treated as unique values rather than dropped automatically.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python : 3.9.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.14.16-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.4
numpy : 1.21.4
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 58.3.0
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 : 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
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} | 8 | "2021-11-18T21:11:02Z" | "2021-11-21T10:37:03Z" | "2021-11-20T21:12:08Z" | MEMBER | null | - [x] closes #42505
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
@simonjayhawkins where does the whatsnew for this go? | {
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Allows for sharing some more Block methods. Will be able to do the same for 'where' following the bugfix that comes after #44514. | {
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} | 0 | "2021-11-19T00:16:00Z" | "2021-11-20T00:11:39Z" | "2021-11-20T00:11:39Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
Create a PR the workflows will run and in the `Test experimental data manager (slow)` workflow it it will return an exit code 1
Launch `pytest pandas/tests/plotting/test_hist_method.py pandas/tests/plotting/test_series.py pandas/tests/plotting/frame/test_hist_box_by.py`
All tests used to pass a week or so ago, now it is failing (and failing in many different PRs, does not look like an isolate case).
```
### Issue Description
The following tests are consistently failing on local installation or in the CI/CD pipeline.
```
=========================== short test summary info ============================
FAILED pandas/tests/plotting/test_hist_method.py::TestDataFrameGroupByPlots::test_axis_share_x
FAILED pandas/tests/plotting/test_hist_method.py::TestDataFrameGroupByPlots::test_axis_share_y
FAILED pandas/tests/plotting/test_hist_method.py::TestDataFrameGroupByPlots::test_axis_share_xy
FAILED pandas/tests/plotting/test_series.py::TestSeriesPlots::test_area_sharey_dont_overwrite
FAILED pandas/tests/plotting/frame/test_frame.py::TestDataFramePlots::test_area_sharey_dont_overwrite
FAILED pandas/tests/plotting/frame/test_hist_box_by.py::TestHistWithBy::test_axis_share_x_with_by
FAILED pandas/tests/plotting/frame/test_hist_box_by.py::TestHistWithBy::test_axis_share_y_with_by
```
### Expected Behavior
Tests should pass
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 8c4bd0e0c3698ced1420c41d33bac9f0e8fb4916
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+1144.g8c4bd0e0c3.dirty
numpy : 1.21.4
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 59.1.1
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.24.6
sphinx : 4.3.0
blosc : None
feather : None
xlsxwriter : 3.0.2
lxml.etree : 4.6.4
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.29.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.11.0
fastparquet : 0.7.1
gcsfs : 2021.11.0
matplotlib : 3.5.0
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 6.0.1
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.7.2
sqlalchemy : 1.4.27
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 0 | "2021-11-19T00:41:30Z" | "2021-11-20T21:30:54Z" | "2021-11-20T20:46:44Z" | 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_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
Gives us the flexibility to consolidate the logic in `__add__` | {
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