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} | 1 | 2021-09-23T18:40:06Z | 2021-09-23T21:13:12Z | 2021-09-23T21:11:56Z | CONTRIBUTOR | null | It is stated in this file on line 13 that `# Aarch64(Python 3.9 requirements are the same as AMD64)`
However, the python 3.9 numpy requirement is excluding `aarch64`, therefore there is no `aarch64` numpy requirement defined at all for py3.9.
When one tries to build pandas from source on `aarch64` with py3.9 (such as alpine 3.14, which is musl based), there is no matching numpy found and therefore the build fails.
This commit sets the numpy version requirement for `aarch64` on py3.9 the same as amd64 as stated.
Here's a log of a failed build:
```
Collecting pandas==1.3.0
Downloading pandas-1.3.0.tar.gz (4.7 MB)
Installing build dependencies: started
Running command /usr/bin/python3 /tmp/pip-standalone-pip-y3p0bgt2/__env_pip__.zip/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-ha64_wsb/overlay --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple --find-links https://wheel-index.linuxserver.io/alpine/ -- 'setuptools>=38.6.0' wheel 'Cython>=0.29.21,<3' 'numpy==1.17.3; python_version=='"'"'3.7'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.18.3; python_version=='"'"'3.8'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.19.3; python_version>='"'"'3.9'"'"' and (platform_machine!='"'"'arm64'"'"' or platform_system!='"'"'Darwin'"'"') and platform_machine!='"'"'aarch64'"'"'' 'numpy==1.19.2; python_version=='"'"'3.7'"'"' and platform_machine=='"'"'aarch64'"'"'' 'numpy==1.19.2; python_version=='"'"'3.8'"'"' and platform_machine=='"'"'aarch64'"'"'' 'numpy>=1.20.0; python_version=='"'"'3.8'"'"' and platform_machine=='"'"'arm64'"'"' and platform_system=='"'"'Darwin'"'"'' 'numpy>=1.20.0; python_version=='"'"'3.9'"'"' and platform_machine=='"'"'arm64'"'"' and platform_system=='"'"'Darwin'"'"''
Looking in links: https://wheel-index.linuxserver.io/alpine/
Ignoring numpy: markers 'python_version == "3.7" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version >= "3.9" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.7" and platform_machine == "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "aarch64"' don't match your environment
Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
Ignoring numpy: markers 'python_version == "3.9" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
Collecting setuptools>=38.6.0
Using cached setuptools-58.1.0-py3-none-any.whl (816 kB)
Collecting wheel
Using cached wheel-0.37.0-py2.py3-none-any.whl (35 kB)
Collecting Cython<3,>=0.29.21
Downloading Cython-0.29.24-py2.py3-none-any.whl (979 kB)
Installing collected packages: wheel, setuptools, Cython
Successfully installed Cython-0.29.24 setuptools-58.1.0 wheel-0.37.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Installing build dependencies: finished with status 'done'
Getting requirements to build wheel: started
Running command /usr/bin/python3 /usr/lib/python3.9/site-packages/pip/_vendor/pep517/in_process/_in_process.py get_requires_for_build_wheel /tmp/tmpmg6hkkyk
Traceback (most recent call last):
File "/usr/lib/python3.9/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 349, in <module>
main()
File "/usr/lib/python3.9/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 331, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "/usr/lib/python3.9/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 117, in get_requires_for_build_wheel
return hook(config_settings)
File "/tmp/pip-build-env-ha64_wsb/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 154, in get_requires_for_build_wheel
return self._get_build_requires(
File "/tmp/pip-build-env-ha64_wsb/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 135, in _get_build_requires
self.run_setup()
File "/tmp/pip-build-env-ha64_wsb/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 258, in run_setup
super(_BuildMetaLegacyBackend,
File "/tmp/pip-build-env-ha64_wsb/overlay/lib/python3.9/site-packages/setuptools/build_meta.py", line 150, in run_setup
exec(compile(code, __file__, 'exec'), locals())
File "setup.py", line 18, in <module>
import numpy
ModuleNotFoundError: No module named 'numpy'
Getting requirements to build wheel: finished with status 'error'
WARNING: Discarding https://files.pythonhosted.org/packages/53/05/bf382e8bc60731906a2e7261648bcea3a6b309ad2b9952010737a1b9413e/pandas-1.3.0.tar.gz#sha256=c554e6c9cf2d5ea1aba5979cc837b3649539ced0e18ece186f055450c86622e2 (from https://pypi.org/simple/pandas/) (requires-python:>=3.7.1). Command errored out with exit status 1: /usr/bin/python3 /usr/lib/python3.9/site-packages/pip/_vendor/pep517/in_process/_in_process.py get_requires_for_build_wheel /tmp/tmpmg6hkkyk Check the logs for full command output.
ERROR: Could not find a version that satisfies the requirement pandas==1.3.0 (from versions: 0.1, 0.2, 0.3.0, 0.4.0, 0.4.1, 0.4.2, 0.4.3, 0.5.0, 0.6.0, 0.6.1, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.8.0, 0.8.1, 0.9.0, 0.9.1, 0.10.0, 0.10.1, 0.11.0, 0.12.0, 0.13.0, 0.13.1, 0.14.0, 0.14.1, 0.15.0, 0.15.1, 0.15.2, 0.16.0, 0.16.1, 0.16.2, 0.17.0, 0.17.1, 0.18.0, 0.18.1, 0.19.0, 0.19.1, 0.19.2, 0.20.0, 0.20.1, 0.20.2, 0.20.3, 0.21.0, 0.21.1, 0.22.0, 0.23.0, 0.23.1, 0.23.2, 0.23.3, 0.23.4, 0.24.0, 0.24.1, 0.24.2, 0.25.0, 0.25.1, 0.25.2, 0.25.3, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.0.5, 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.2.0, 1.2.1, 1.2.2, 1.2.3, 1.2.4, 1.2.5, 1.3.0, 1.3.1, 1.3.2, 1.3.3)
ERROR: No matching distribution found for pandas==1.3.0
```
- [ ] 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
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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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} | 1 | 2021-09-23T19:39:42Z | 2021-09-24T12:20:44Z | 2021-09-24T11:31:17Z | CONTRIBUTOR | null | Backport PR #43634 on branch 1.3.x | {
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} | 0 | 2021-09-23T19:50:06Z | 2021-09-27T15:07:27Z | 2021-09-27T13:11:54Z | MEMBER | null | ```
from asv_bench.benchmarks.groupby import *
self2 = GroupByMethods()
self2.setup('uint', 'quantile', 'direct', 1)
%timeit self2.time_dtype_as_group('uint', 'quantile', 'direct', 1)
1.03 ms ± 46.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master
328 µs ± 17.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
``` | {
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} | 0 | 2021-09-23T21:12:23Z | 2021-09-23T23:29:04Z | 2021-09-23T23:29:04Z | NONE | null | Backport PR #43722: BUG: set numpy requirement for aarch64 and py3.9 | {
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] | closed | false | null | [] | null | 1 | 2021-09-24T00:45:33Z | 2021-09-24T00:57:24Z | 2021-09-24T00:57:15Z | 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
start_date = pd.to_datetime('2019-01-01')
end_date = pd.to_datetime('2019-01-05')
data = {
'code': ['X333']*5,
'date': pd.date_range(start_date, periods=5),
'float': np.random.randn(5),
}
df = (
pd.DataFrame(data)
.set_index('date')
.groupby('code')
.apply(
lambda x: x.reindex(pd.date_range(start_date, end_date, name='date'))
)
)
print(df)
```
### Issue Description
pandas=1.3.3
Group by key `code` is removed from the multi-index:
```
code float
date
2019-01-01 X333 -0.732025
2019-01-02 X333 0.251650
2019-01-03 X333 0.301808
2019-01-04 X333 -0.585017
2019-01-05 X333 0.067119
```
### Expected Behavior
pandas=1.3.0
Group by key `code` is kept in the multi-index:
```
code float
code date
X333 2019-01-01 X333 -0.971248
2019-01-02 X333 0.701875
2019-01-03 X333 1.222485
2019-01-04 X333 0.315564
2019-01-05 X333 0.836289
```
### Installed Versions
<details>
<br>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.7.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 0, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
fsspec : 2021.08.1
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.24
tables : None
tabulate : None
xarray : 0.19.0
xlrd : None
xlwt : None
numba : 0.53.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43728/events | https://github.com/pandas-dev/pandas/pull/43728 | 1,005,984,210 | PR_kwDOAA0YD84sN5MO | 43,728 | REF: unwrap PandasArray earlier | {
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} | 2 | 2021-09-24T01:17:18Z | 2021-09-25T18:11:55Z | 2021-09-25T16:57:41Z | 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
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} | 18 | 2021-09-24T02:13:08Z | 2021-10-17T16:22:43Z | 2021-10-13T10:40:35Z | MEMBER | null | - [ ] xref #43715
- [ ] closes #40307
- [ ] 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 | 2021-09-24T04:31:05Z | 2021-09-25T15:39:49Z | 2021-09-25T15:20:15Z | MEMBER | null | ```
from asv_bench.benchmarks.groupby import *
self = GroupByMethods()
self.setup("datetime", "count", "direct", 2)
%timeit self.time_dtype_as_field('datetime', 'count', 'direct', 2)
73.7 µs ± 982 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
48.7 µs ± 473 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
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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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} | 4 | 2021-09-24T12:11:21Z | 2021-11-12T03:26:01Z | 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
import datetime, pytz
# t is a date after 2262 that should be coerced into NaT
t = datetime.datetime(3000, 1, 1, 0, 0, 0, 0, pytz.UTC)
# lists with many such dates
l50 = [t] * 50
l51 = [t] * 51
# pd.to_datetime crashes if the list is longer that 50
# this is fine
print(pd.to_datetime(l50, utc=True, errors='coerce'))
# this crashes
print(pd.to_datetime(l51, utc=True, errors='coerce'))
```
### Issue Description
Background (irrelevant for reproducing the bug, but good to know):
A google bigquery gives me dataframe with a column with datetime values with year=9999,
and I store the result as a parquet file. When I read it back, `read_parquet` crashes.
I traced it down to the following problem in pd.to_datetime.
The issue:
if pd.datetime needs to convert more than 50 datetime values with tzinfo set, and those values are above year 2262 (the maximal date for pd.Timestamp) and should be coerced into NaT, then pd.to_datetime crashes with a raised exception. It works fine if the series is smaller than 51 items.
### Expected Behavior
The expected behavior is that pd.to_datetime should return a list of NaT values also for Sequences longer than 50 items.
### Installed Versions
<details>
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-17-cloud-amd64
Version : #1 SMP Debian 4.19.194-3 (2021-07-18)
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : C.UTF-8
LOCALE : None.None
pandas : 1.3.3
numpy : 1.19.5
pytz : 2021.1
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 : 2.11.3
IPython : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 2021.08.1
fastparquet : None
gcsfs : 2021.08.1
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.54.0
</details>
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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
# !pip install -U pandas
import pandas as pd
import numpy as np
# Create arbitrary 3-channel 8-bit image
data = np.random.randint(0, 256, size=(50,50, 3), dtype='uint8')
# Replicate this image and treat each image as a row of pixel features
rows = [data.reshape(-1)]*2
assert all(r.dtype == np.uint8 for r in rows)
print(pd.DataFrame(rows).dtypes)
# int8???
print(pd.DataFrame(np.vstack(rows)).dtypes)
# uint8 (expected)
```
### Issue Description
Apologies if this issue already exists -- type-related issues are hard for me to navigate in github since there are too many to easily parse.
`uint8` subarray dtypes are silently converted to `int8` when constructing a dataframe from lists.
I tried testing on master but Windows has a build error for me (stack trace below),
<details>
```
building 'pandas._libs.algos' extension
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
----------------------------------------
ERROR: Failed building wheel for pandas
Failed to build pandas
ERROR: Could not build wheels for pandas which use PEP 517 and cannot be installed directly
```
</details>
### Expected Behavior
The dtype of each unit in each column is a `uint8`, so I would expect `uint8` resulting columns
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.3.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.15
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
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} | 2 | 2021-09-24T15:59:09Z | 2021-10-16T15:37:51Z | 2021-10-16T15:37:51Z | 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
df1 = pd.DataFrame(data={("lvl0","lvl1-a"): ["1","2","3"], ("lvl0","lvl1-b"): ["4","5","6"]}, dtype=str)
df2 = pd.DataFrame(data={("lvl0","lvl1-a"): ["1","2","3"], ("lvl0","lvl1-c"): ["7","8","9"]}, dtype=pd.StringDtype())
result = pd.merge(left=df1, right=df2, on=[("lvl0","lvl1-a")])
```
### Issue Description
I am trying to make a merge between two data frames with a `MultiIndex` for the columns. The first data frame has an object data type (`str` in my example) and the second an extension array data type (`pd.StringDtype()` in my example). In this case, in an internal method, an `assign` method is used but unfortunately, the `name` of the column is not a string but a tuple...
### Expected Behavior
I would have expected the merge to happen as it is with two `str` dtype data frames or two `pd.StirngDtype()` dtype data frames.
```
lvl0
lvl1-a lvl1-b lvl1-c
0 1 4 7
1 2 5 8
2 3 6 9
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.0-8-amd64
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machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
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
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} | 3 | 2021-09-24T19:59:15Z | 2021-09-29T13:28:11Z | 2021-09-29T13:28:07Z | CONTRIBUTOR | null | - [X] closes #38025
- [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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] | open | false | null | [] | null | 8 | 2021-09-24T21:25:23Z | 2021-11-07T22:16:46Z | null | MEMBER | null | Part of #43678
- [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
Replaces the list-like implementation for `.agg` used by Series, DataFrame, SeriesGroupBy, DataFrameGroupBy, Window, and Resample. This will transpose agg results, and swap multiindex levels as well as the order from within the levels. Adds the new implementation behind the option "new_udf_methods".
Docs: [future_udf_behavior.zip](https://github.com/pandas-dev/pandas/files/7368201/future_udf_behavior.zip)
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} | 1 | 2021-09-25T11:07:44Z | 2021-09-25T16:50:42Z | 2021-09-25T15:33:56Z | CONTRIBUTOR | null | When tooltips were developed there were no hidden_rows or hidden_columns and no max_rows or max_columns.
This adds some benchmarks and a minimal change to improve performance. Scope to do a bit better here in future also.
```
before after ratio
[cd13e3a4] [7766f055]
<master> <styler_tooltips_perf>
- 52.5±2ms 47.4±0.7ms 0.90 io.style.Render.time_apply_format_hide_render(24, 120)
- 196±0.6ms 173±0.9ms 0.88 io.style.Render.time_tooltips_render(36, 120)
- 78.0±2ms 67.0±0.4ms 0.86 io.style.Render.time_tooltips_render(12, 120)
- 140±2ms 120±0.3ms 0.85 io.style.Render.time_tooltips_render(24, 120)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
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} | 6 | 2021-09-25T11:22:43Z | 2021-11-17T19:06:51Z | 2021-11-06T02:45:43Z | CONTRIBUTOR | null | - [x] closes #38482
- [ ] 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
- [x] whatsnew: Added sentence to show that unmatched values return -1 in get_indexer method
In [1]: python make.py --single pandas.Index.get_indexer
Out [1]:
In [2]: pre-commit run --files pandas/core/indexes/base.py
Out [2] : 
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} | 8 | 2021-09-25T16:08:52Z | 2021-11-07T15:16:04Z | null | MEMBER | null | Ref: https://github.com/pandas-dev/pandas/pull/41475, https://github.com/pandas-dev/pandas/issues/39993#issuecomment-926281200
To keep the API consistent between groupby / non-groupby UDF methods (apply, agg, transform), we should not allow silent failure in `core.apply`.
- [ ] transform_dict_like #43743
- [ ] agg_list_like - #43741 | {
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} | 2 | 2021-09-25T16:26:42Z | 2021-09-30T16:05:01Z | 2021-09-29T13:10:46Z | MEMBER | null | Part of #43740
- [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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] | open | false | null | [] | null | 0 | 2021-09-25T17:21:37Z | 2021-10-06T15:12:13Z | null | CONTRIBUTOR | null | Pyright has [many rules](https://github.com/microsoft/pyright/blob/main/docs/configuration.md), but currently most of them are disabled.
Here is a list of all currently disabled rules and in bracket the number of violations (grep -c) to have a rough idea how much effort enabling a certain rule is:
"Core" Typing:
- [ ] reportGeneralTypeIssues (2322)
- [ ] reportFunctionMemberAccess (5)
- [ ] reportIncompatibleMethodOverride (313)
- [ ] reportIncompatibleVariableOverride (11)
- [ ] reportMissingTypeArgument (369)
- [ ] reportOptionalCall (8)
- [ ] reportOptionalIterable (6)
- [ ] reportOptionalMemberAccess (478)
- [ ] reportOptionalOperand (7)
- [ ] reportOptionalSubscript (34)
- [ ] reportPropertyTypeMismatch (1)
Third-Party/Conventions/Other:
- [ ] reportConstantRedefinition (21)
- [ ] reportImportCycles (40)
- [ ] reportMissingModuleSource (2)
- [ ] reportMissingTypeStubs (102)
- [ ] reportPrivateImportUsage (3)
- [ ] reportPrivateUsage (765)
- [ ] reportUnboundVariable (70)
- [ ] reportUnknownArgumentType (11458)
- [ ] reportUnknownLambdaType (304)
- [ ] reportUnknownMemberType (21661)
- [ ] reportUnknownParameterType (10035)
- [ ] reportUnknownVariableType (12947)
- [ ] reportUnnecessaryComparison (35)
- [ ] reportUnnecessaryIsInstance (86)
- [ ] reportUnsupportedDunderAll (1)
- [ ] reportUntypedBaseClass (18)
- [ ] reportUntypedFunctionDecorator (3)
- [ ] reportUnusedClass (2): used for compatibility in pickle
- [ ] reportUnusedFunction (6): needs a discussion whether these private functions should either be moved or made public
- [ ] reportUnusedImport (292): could be enabled if `__all__` or redundant import were used
- [ ] reportUnusedVariable (67): needs consensus to start unused variables with _.
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} | 0 | 2021-09-25T17:22:27Z | 2021-09-26T18:46:34Z | 2021-09-26T01:50:07Z | MEMBER | null | Part of #43740
- [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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] | open | false | null | [] | null | 1 | 2021-09-25T17:45:35Z | 2021-10-06T11:32:42Z | null | MEMBER | null | We might want to consider running the sdist checks less. They take around 10-15 minutes each and personally, I have only seen them fail once or twice ever(which were on PRs that changed build stuff).
We could try moving them to only run on PRs that are labeled "Build" and on merged commits first, and then maybe look to move them to nightly.
cc @fangchenli @simonjayhawkins | {
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} | 0 | 2021-09-25T19:02:05Z | 2021-10-19T03:45:31Z | 2021-09-25T22:23:16Z | MEMBER | null | - [x] closes #39178
- [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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} | 12 | 2021-09-26T02:03:07Z | 2021-10-05T12:11:56Z | 2021-09-26T12:21:14Z | CONTRIBUTOR | null | The github action running the pre-commit checks does not install all necessary python packages for type checking. This triggered a few unnecessary `reportOverlappingOverload` by pyright because numpy was not installed.
Keep pyright in the pre-commit but disable it on the CI (SKIP=pyright). Instead, run pyright together with mypy in code_checks.sh.
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] | open | false | null | [] | null | 3 | 2021-09-26T02:30:10Z | 2021-10-04T16:51:57Z | 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({'foo_1': ['a', 'b', 'a', 'h', 'g'], 'foo_2': ['b', 'a', 'c', 'g', 'c'],
'foo_3': ['e', 'c', 'f', 'c', 'h'],'foo_4': ['f', 'e', 'b', 'a', 'c']})
cols = df.columns
foo = pd.get_dummies(df,prefix=['']*len(cols),prefix_sep='')
# This works
foo.agg({'a' : 'sum', 'b' : ['min']})
# This works
foo.agg({'a' : ['sum'], 'b' : 'min'})
# This doesn't
foo.agg({'a' : 'sum', 'b' : 'min'})
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-e185a0f152e3> in <module>()
----> 1 foo.agg({'a' : 'sum', 'b' : 'min'})
16 frames
/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py in aggregate(self, func, axis, *args, **kwargs)
8549
8550 op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
-> 8551 result = op.agg()
8552
8553 if relabeling:
/usr/local/lib/python3.7/dist-packages/pandas/core/apply.py in agg(self)
698 result = None
699 try:
--> 700 result = super().agg()
701 except TypeError as err:
702 exc = TypeError(
/usr/local/lib/python3.7/dist-packages/pandas/core/apply.py in agg(self)
159
160 if is_dict_like(arg):
--> 161 return self.agg_dict_like()
162 elif is_list_like(arg):
163 # we require a list, but not a 'str'
/usr/local/lib/python3.7/dist-packages/pandas/core/apply.py in agg_dict_like(self)
458 axis = 0 if isinstance(obj, ABCSeries) else 1
459 result = concat(
--> 460 {k: results[k] for k in keys_to_use}, axis=axis, keys=keys_to_use
461 )
462 elif any(is_ndframe):
/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
309 stacklevel=stacklevel,
310 )
--> 311 return func(*args, **kwargs)
312
313 return wrapper
/usr/local/lib/python3.7/dist-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
305 )
306
--> 307 return op.get_result()
308
309
/usr/local/lib/python3.7/dist-packages/pandas/core/reshape/concat.py in get_result(self)
506
507 index, columns = self.new_axes
--> 508 df = cons(data, index=index, copy=self.copy)
509 df.columns = columns
510 return df.__finalize__(self, method="concat")
/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
612 elif isinstance(data, dict):
613 # GH#38939 de facto copy defaults to False only in non-dict cases
--> 614 mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
615 elif isinstance(data, ma.MaskedArray):
616 import numpy.ma.mrecords as mrecords
/usr/local/lib/python3.7/dist-packages/pandas/core/internals/construction.py in dict_to_mgr(data, index, columns, dtype, typ, copy)
463
464 return arrays_to_mgr(
--> 465 arrays, data_names, index, columns, dtype=dtype, typ=typ, consolidate=copy
466 )
467
/usr/local/lib/python3.7/dist-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)
122
123 # don't force copy because getting jammed in an ndarray anyway
--> 124 arrays = _homogenize(arrays, index, dtype)
125
126 else:
/usr/local/lib/python3.7/dist-packages/pandas/core/internals/construction.py in _homogenize(data, index, dtype)
569 # Forces alignment. No need to copy data since we
570 # are putting it into an ndarray later
--> 571 val = val.reindex(index, copy=False)
572
573 val = val._values
/usr/local/lib/python3.7/dist-packages/pandas/core/series.py in reindex(self, index, **kwargs)
4578 )
4579 def reindex(self, index=None, **kwargs):
-> 4580 return super().reindex(index=index, **kwargs)
4581
4582 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in reindex(self, *args, **kwargs)
4817 # perform the reindex on the axes
4818 return self._reindex_axes(
-> 4819 axes, level, limit, tolerance, method, fill_value, copy
4820 ).__finalize__(self, method="reindex")
4821
/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4841 fill_value=fill_value,
4842 copy=copy,
-> 4843 allow_dups=False,
4844 )
4845
/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
4887 fill_value=fill_value,
4888 allow_dups=allow_dups,
-> 4889 copy=copy,
4890 )
4891 # If we've made a copy once, no need to make another one
/usr/local/lib/python3.7/dist-packages/pandas/core/internals/managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)
668 # some axes don't allow reindexing with dups
669 if not allow_dups:
--> 670 self.axes[axis]._validate_can_reindex(indexer)
671
672 if axis >= self.ndim:
/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py in _validate_can_reindex(self, indexer)
3783 # trying to reindex on an axis with duplicates
3784 if not self._index_as_unique and len(indexer):
-> 3785 raise ValueError("cannot reindex from a duplicate axis")
3786
3787 def reindex(
ValueError: cannot reindex from a duplicate axis
```
### Issue Description
The issue seems to be having multiple columns with the same name. I tested with other dataframe without multiple columns with the same name and it works fine.
Tested in colab with the latest pandas and also local with anaconda with 1.3.2
### Expected Behavior
a b
a a a b b b
sum 2.0 1.0 1.0 NaN NaN NaN
min NaN NaN NaN 0.0 0.0 0.0
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.7.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.4.0
Cython : 0.29.24
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.23
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.tseries.offsets.BYearEnd.html
### Documentation problem
A “s” is missing in the word “offset” `from pandas.tseries.offset import BYearEnd`
### Suggested fix for documentation
`from pandas.tseries.offsets import BYearEnd`
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} | 0 | 2021-09-26T09:45:10Z | 2021-09-26T10:42:26Z | 2021-09-26T10:42:26Z | CONTRIBUTOR | null | - [ ] closes #43749
- [ ] Fixes example for BYearEnd
- [ ] pandas/pandas/_libs/tslibs/offsets.pyx file updated
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} | 6 | 2021-09-26T15:15:18Z | 2021-09-28T15:18:23Z | 2021-09-28T15:05:03Z | CONTRIBUTOR | null | Found by pyright's `reportUnused*`.
Unused functions/variables are not directly related to typing, but pyright warns about these cases because it is asked to do unnecessary work. | {
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} | 0 | 2021-09-26T16:52:19Z | 2021-09-26T17:33:31Z | 2021-09-26T17:33:31Z | CONTRIBUTOR | null | - [x] closes #43749
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} | 10 | 2021-09-26T17:13:02Z | 2021-10-03T14:14:03Z | 2021-10-03T14:14:03Z | NONE | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isin.html
### Documentation problem
In the last example, the text introducing the code snippet reads:
> When values is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in df2.
However, "df2" does not exist in the included code snippet (it is instead called "other").
Additionally, I am not comfortable enough with pandas to assert this, but it seems like the text should incorrectly states that "falcon" does not match, since "falcon" indeed does match for both columns, see the relevant portion of the code snippet:
```
>>> df.isin(other)
num_legs num_wings
falcon True True
dog False False
```
### Suggested fix for documentation
Text should at least read:
> When `values` is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in other.
or the variable in the code snippet should be renamed to "df2".
Additionally, something may need to be done to address the potential discrepancy in saying that "falcon" does not match. | {
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} | 1 | 2021-09-26T18:55:11Z | 2021-09-30T19:52:21Z | 2021-09-30T19:46:48Z | CONTRIBUTOR | null | - [x] closes #43701
- [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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} | 0 | 2021-09-26T19:37:27Z | 2021-09-30T19:44:55Z | 2021-09-30T19:44:46Z | CONTRIBUTOR | null | Adding `--doctest-cython` to `ci/run_tests.sh` to cover doctests in cython files.
Lots of them are failing at the moment. But I think few of them (like now, today), can be skipped using `# doctest: +SKIP`.
Below is the current list of failures atm.
```python
===================================================================================== short test summary info =====================================================================================
FAILED pandas/_libs/hashtable.cpython-38-darwin.so::pandas._libs.hashtable.Int64Factorizer.factorize
FAILED pandas/_libs/hashtable.cpython-38-darwin.so::pandas._libs.hashtable.ObjectFactorizer.factorize
FAILED pandas/_libs/hashtable.cpython-38-darwin.so::pandas._libs.hashtable.__test__.Int64Factorizer.factorize (line 134)
FAILED pandas/_libs/hashtable.cpython-38-darwin.so::pandas._libs.hashtable.__test__.ObjectFactorizer.factorize (line 88)
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.__test__.infer_dtype (line 1299)
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.__test__.is_iterator (line 242)
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.__test__.is_list_like (line 1051)
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.__test__.is_scalar (line 149)
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.infer_dtype
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.is_iterator
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.is_list_like
FAILED pandas/_libs/lib.cpython-38-darwin.so::pandas._libs.lib.is_scalar
FAILED pandas/_libs/tslibs/dtypes.cpython-38-darwin.so::pandas._libs.tslibs.dtypes.Resolution.from_attrname
FAILED pandas/_libs/tslibs/dtypes.cpython-38-darwin.so::pandas._libs.tslibs.dtypes.Resolution.get_reso_from_freq
FAILED pandas/_libs/tslibs/dtypes.cpython-38-darwin.so::pandas._libs.tslibs.dtypes.__test__.Resolution.from_attrname (line 223)
FAILED pandas/_libs/tslibs/dtypes.cpython-38-darwin.so::pandas._libs.tslibs.dtypes.__test__.Resolution.get_reso_from_freq (line 238)
FAILED pandas/_libs/tslibs/nattype.cpython-38-darwin.so::pandas._libs.tslibs.nattype.NaTType.now
FAILED pandas/_libs/tslibs/nattype.cpython-38-darwin.so::pandas._libs.tslibs.nattype.NaTType.today
FAILED pandas/_libs/tslibs/nattype.cpython-38-darwin.so::pandas._libs.tslibs.nattype.NaTType.utcfromtimestamp
FAILED pandas/_libs/tslibs/nattype.cpython-38-darwin.so::pandas._libs.tslibs.nattype.NaTType.utcnow
FAILED pandas/_libs/tslibs/period.cpython-38-darwin.so::pandas._libs.tslibs.period._Period.strftime
FAILED pandas/_libs/tslibs/period.cpython-38-darwin.so::pandas._libs.tslibs.period.__test__._Period.strftime (line 2243)
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.Timestamp.now
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.Timestamp.today
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.Timestamp.utcfromtimestamp
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.Timestamp.utcnow
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps._Timestamp.to_period
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.__test__.Timestamp.now (line 1048)
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.__test__.Timestamp.today (line 1075)
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.__test__.Timestamp.utcfromtimestamp (line 1115)
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.__test__.Timestamp.utcnow (line 1101)
FAILED pandas/_libs/tslibs/timestamps.cpython-38-darwin.so::pandas._libs.tslibs.timestamps.__test__._Timestamp.to_period (line 905)
FAILED pandas/_libs/tslibs/timezones.cpython-38-darwin.so::pandas._libs.tslibs.timezones.__test__.tz_standardize (line 375)
FAILED pandas/_libs/tslibs/timezones.cpython-38-darwin.so::pandas._libs.tslibs.timezones.tz_standardize
================================================================================= 34 failed, 106 passed in 1.10s ================================================================================== | {
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} | 0 | 2021-09-26T20:01:39Z | 2021-10-30T17:31:38Z | 2021-10-30T17:31:38Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
When Styler was developed it had `hide_index`, which hid the whole index, and `hide_columns`, which performed a separate function of hiding specific columns of data whilst still showing the column headers.
After extending the functions in 1.3.0, 1.4.0, both methods now have the same signature and function. They can hide the display of the indexes, or hide specific rows or columns of data, or hide specific levels in a multiindex.
#### Describe the solution you'd like
Should we deprecate both of these methods and wrap their function into a generic `Styler.hide(axis=.., *args)`?
Seems to optimise code and probably user functionality.
This might also make it easier to add a `show()` method for styler similar to `head` and `tail` for dataframes?
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} | 3 | 2021-09-26T20:25:27Z | 2021-09-27T07:19:02Z | 2021-09-26T22:20:23Z | 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({
"letter": ["a", "b", "c", "d", "e", "f"],
})
map_letter_to_num = {
"a": 1,
"d": 4,
}
# ----- Map without fill -----
df_mapped = df.copy()
df_mapped["number"] = df["letter"].map(map_letter_to_num)
print(df_mapped)
# letter number
# 0 a 1.0
# 1 b NaN
# 2 c NaN
# 3 d 4.0
# 4 e NaN
# 5 f NaN
# I would expect column "numbers" to be floats, which they also are
# (NumPy treats NaN as a float)
print(df_mapped.dtypes)
# letter object
# number float64
# dtype: object
# ----- Map with integer fill -----
df_mapped_filled = df.copy()
df_mapped_filled["number"] = df["letter"].map(map_letter_to_num).fillna(1)
print(df_mapped_filled)
# letter number
# 0 a 1.0
# 1 b 1.0
# 2 c 1.0
# 3 d 4.0
# 4 e 1.0
# 5 f 1.0
# I would expect column "number" to be integers, since I'm both mapping filling with only integers
print(df_mapped_filled.dtypes)
# letter object
# number float64
# dtype: object
```
### Issue Description
I have a dataframe with a column, let's call it `"letters"`. I want to insert a new column, let's call it `"numbers"`, by mapping via a `dict`. The dict has letters as keys and integers as values.
Since I might not always have all the letter keys present in my dict, I want to fill the rest with a default value. For this, I'm using the `.fillna()` method with an integer as fill value.
### Expected Behavior
So seen "from the outside", I'm building a new column for which I supply values for all rows. All values are integers. Therefore, I would expect the resulting column to have the type `int`, or `int64`.
Instead, the column comes out as `int64`.
I think I understand why this happens, hence the phrasing "from the outside" used above.
The mapping is performed in a two-step process, first with `.map()` and then chained with `.fillna()`. The `.map()` returns a dataframe with the `"numbers"` column as `float64`, which it should. Then, `fillna()` operates on that and inserts integers into a float column. Inserting integers into a column of type `float64` converts the integers to `float64` as well.
Is that correctly interpreted?
I was hoping to circumvent this by supplying a `fillna` parameter in the `map()` method, to let Pandas know upfront that I have only integers to supply. However, `map()` does not take such a parameter.
For anyone looking for a solution, I could solve this by simple conversion after having debugged it.
```python
import numpy as np
df_mapped_filled["number"] = df_mapped_filled["number"].astype(np.int64)
```
### Installed Versions
The newest version of Pandas as of now is 1.3.3. I'm using 1.3.2 and release notes for 1.3.3 do not mention anything related to this issue.
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Danish_Denmark.1252
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | 2021-09-26T21:07:45Z | 2021-09-28T14:49:41Z | 2021-09-27T22:17:14Z | MEMBER | null | ```
from asv_bench.benchmarks.groupby import *
self = Cumulative()
self.setup("int64", "cumsum")
%timeit self.setup("int64", "cumsum"); self.time_frame_transform("int64", "cumsum")
107 ms ± 7.57 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
74.6 ms ± 2.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
```
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"name": "Timedelta",
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] | open | false | null | [] | null | 5 | 2021-09-27T07:28:34Z | 2021-11-03T18:07:26Z | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [ ] 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
res1 = (pd.Timestamp(1001) == pd.Timedelta(seconds=1e-9) + pd.Timestamp(1000)) # False, but should be True
res2 = (pd.Timestamp(1001) == pd.Timestamp(1) + pd.Timedelta(seconds=1000e-9)) # True
res1 == res2 # False, should be True
# this sum works properly `pd.Timestamp(1) + pd.Timedelta(nanoseconds=1)`
```
### Issue Description
Adding `Timedelta`s that have nanoseconds defined as a float do not work properly for timedelta values in the [1-999] range.
This report is a result of [this SO question](https://stackoverflow.com/questions/69321347/pandas-summing-nanoseconds-time-delta-to-timestamp?noredirect=1#comment122560569_69321347)
### Expected Behavior
`all([pd.Timestamp(1000 + x*1e-9) == pd.Timedelta(seconds=x*1e-9) + pd.Timestamp(1000)) for x in range(1000)])` should be `True`
Or any equivalent not using the explicit default nanoseconds unit or the `nanoseconds` keyword as in: `pd.Timedelta(1)` or `pd.Timedelta(nanoseconds=1)`
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 2 | 2021-09-27T12:22:36Z | 2021-10-01T23:06:02Z | 2021-10-01T23:05:53Z | NONE | null | ###
- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
### Link to question on StackOverflow
https://stackoverflow.com/questions/69345845/why-does-pandas-dateoffset-rollback-not-work-the-way-i-expect-it-to-with-days
### Question about pandas
I'm trying to move an arbitrary input `pd.Timestamp` back to the correct start offset unless it is already on offset.
The following code works with `BusinessMonthEnd`, `MonthEnd`, `MonthStart`, and so on:
```python
import pandas as pd
from pandas.tseries.offsets import *
def to_start(t: pd.Timestamp, freq: pd.DateOffset) -> pd.Timestamp:
return freq.rollback(t)
assert to_start(pd.to_datetime("2021-09-27"), MonthEnd()) == pd.to_datetime("2021-08-31")
assert to_start(pd.to_datetime("2021-09-27"), MonthBegin()) == pd.to_datetime("2021-08-31")
assert to_start(pd.to_datetime("2021-08-27"), BMonthEnd()) == pd.to_datetime("2021-07-30")
assert to_start(pd.to_datetime("2021-08-27"), YearBegin()) == pd.to_datetime("2021-01-01")
assert to_start(pd.to_datetime("2021-08-27"), BYearBegin()) == pd.to_datetime("2021-01-01")
# also it works nicely with holiday calendars
from pandas.tseries.holiday import USFederalHolidayCalendar
us_fed_biz_days = CustomBusinessDay(calendar=USFederalHolidayCalendar())
memorial_day = pd.to_datetime("2021-05-31")
the_friday_before_memorial_day = pd.to_datetime("2021-05-28")
assert to_start(memorial_day, us_fed_biz_days) == the_friday_before_memorial_day
```
However (and this is driving me crazy) it doesn't seem to work for `Day`, `BusinessDay`, `Week`, `Hour` etc:
```python
assert to_start(pd.to_datetime("2021-08-27 05:00"), Day()) == pd.to_datetime("2021-08-27")
assert to_start(pd.to_datetime("2021-08-27 05:00"), BDay()) == pd.to_datetime("2021-08-27")
assert to_start(pd.to_datetime("2021-08-27 05:15"), Hour()) == pd.to_datetime("2021-08-27 05:00")
assert to_start(pd.to_datetime("2021-08-26"), pd.tseries.frequencies.to_offset("W-MON")) == pd.to_datetime("2021-08-24")
```
I have also tried this:
```python
def to_start(t: pd.Timestamp, freq: pd.DateOffset) -> pd.Timestamp:
return pd.Period(t, freq=freq).start_time
```
which ironically works with the second set of assertions but not the first.
Is my expectation of the above assertions not failing unreasonable, and if so what am I missing, please? | {
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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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] | open | false | null | [] | null | 0 | 2021-09-27T13:36:44Z | 2021-10-02T00:33:02Z | 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 datetime as dt
df = pd.DataFrame([(dt.date.today(), "b", 12)], columns=["date", "b", "count"])
df["date"] = pd.to_datetime(df["date"])
df = df[df["count"] == 1] # uncomment this line to make the dataframe empty and so reset_index raising an exception
df2 = df.set_index('date').groupby(['b']).resample('M').sum().reset_index()
```
### Issue Description
When you run the code above, df won’t be empty and the code will run correctly. But if you uncomment the line that makes the dataframe empty, the reset index will raise a ValueError(f"cannot insert b, already exists").
This is a regression as it was working in pandas 1.2.x
### Expected Behavior
It’s expected for the reset_index to apply correctly on an empty dataframe too.
### Installed Versions
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.2
setuptools : 57.0.0
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
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 : 1.7.1
sqlalchemy : 1.4.25
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
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} | 5 | 2021-09-27T13:57:00Z | 2021-09-30T19:51:54Z | 2021-09-30T19:44:46Z | CONTRIBUTOR | null | - [x] closes #43757
Skipped some of the tests which were failing | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43769 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43769/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43769/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43769/events | https://github.com/pandas-dev/pandas/issues/43769 | 1,008,231,472 | I_kwDOAA0YD848GGQw | 43,769 | BUG: rolling aggregation with on kwarg and multiple functions per column raises an exception | {
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] | open | false | null | [] | null | 0 | 2021-09-27T14:35:54Z | 2021-10-02T00:33:34Z | 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
df = pd.DataFrame([{'A': 1, 'B': 2}, {'A': 3, 'B': 4}])
# Following three lines works as expected:
df.rolling(window=1, on='A').agg({'B': 'min'})
df.rolling(window=1, on='A').agg({'B': 'max'})
df.set_index('A').rolling(window=1).agg({'B': ['min', 'max']})
# Following line raises ValueError
df.rolling(window=1, on='A').agg({'B': ['min', 'max']})
# ValueError: invalid on specified as A, must be a column (of DataFrame), an Index or None
```
### Issue Description
Using `DataFrames`'s rolling with an `on` keyword argument and then `agg` method with multiple functions per column results in `ValueError`. While the same code without `on` parameter, or just single function per column works fine (see "Reproducible Example" for further details)
### Expected Behavior
No `ValueError` raised
### Installed Versions
<details>
```
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-36-generic
Version : #40~20.04.1-Ubuntu SMP Sat Sep 18 02:14:19 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.3
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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] | open | false | null | [] | null | 2 | 2021-09-27T19:27:06Z | 2021-10-02T18:14:49Z | 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
data = pd.Series(np.arange(20).reshape(10, 2).dot([1, 2j]))
print(data)
data.groupby(data.index % 2).median()
0 0.00+2.00j
1 2.00+6.00j
2 4.0+10.0j
3 6.0+14.0j
4 8.0+18.0j
5 10.0+22.0j
6 12.0+26.0j
7 14.0+30.0j
8 16.0+34.0j
9 18.0+38.0j
dtype: complex128
/Volumes/SD/pandas-debnathshoham/pandas/core/groupby/ops.py:192: ComplexWarning: Casting complex values to real discards the imaginary part
values = ensure_float64(values)
Out[14]:
0 8.0
1 10.0
dtype: float64
```
### Issue Description
`groupby.median()` returns absurd result with complex numbers.
xref - #43756
### Expected Behavior
Two possibilities:
1. raise with something like "median not defined for complex"
2. calculate the median separately for real and complex components (which happens on `df.median`)
```python
In [11]: data
Out[11]:
0 0.00+2.00j
1 2.00+6.00j
2 4.0+10.0j
3 6.0+14.0j
4 8.0+18.0j
5 10.0+22.0j
6 12.0+26.0j
7 14.0+30.0j
8 16.0+34.0j
9 18.0+38.0j
dtype: complex128
In [12]: data.median()
Out[12]: (9+20j)
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 619cbf8b3edc409d3aecda19414119938fcf6532
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.4.0.dev0+771.g619cbf8b3e
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.14.1
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : 2021.05.0
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : 1.0.8
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.4.20
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 | 2021-09-27T20:36:06Z | 2021-10-30T17:31:45Z | 2021-10-30T17:31:38Z | CONTRIBUTOR | null | - [x] closes #43758: addresses the comments raised there.
- [x] tests added / passed
- [x] whatsnew entry
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} | 10 | 2021-09-27T21:21:58Z | 2021-10-08T23:27:23Z | 2021-10-06T12:27:59Z | 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
Blocker for MacOS/Windows Python 3.10 wheels. We probably want to migrate these onto Azure as soon as Python 3.10 is available there for load balancing reasons.
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] | closed | false | null | [] | null | 2 | 2021-09-27T22:17:15Z | 2021-10-12T23:07:51Z | 2021-10-12T23:07:51Z | NONE | null | #### Is your feature request related to a problem?
I use pandas to query a database, do some manipulations on the dataframe, then return the result to as a json on a flask api. There are some null values in the dataframe, which makes the to_json() method return a json which can only be interpreted as a string by javascript on the receiving end. This took me quite a while to figure out this was the reason. I replaced the null values with empty strings and then it was received as json javascript array on the receiving end of the API instead of a json string.
#### Describe the solution you'd like
DataFrame.to_json should be able to handle null values or offer a replacement for null such as the fillna method
#### API breaking implications
shouldnt affect api if its an additional feature of to_json?
#### Additional context
Flask/pandas 'working' code snipit, notice the fillna, it returns in browser console as:
```
{data: Array(22), status: 200, statusText: 'OK', headers: {…}, config: {…}, …}
```
```python
@cache.memoize(timeout=120)
def get():
# this query returns null values for some columns , as expected
query = text("""
select
*
from mytable
join mytable2 on x=y
join mytable3 on x=y
where u=i
""")
df= pd.read_sql(query, db.get_engine(app, 'database'), params=params)
sequence = pd.pivot_table(df, index=indexcolumns, columns='z',
values='y', aggfunc=lambda x: ''.join(x))
sequence = pd.DataFrame(sequence.to_records()).fillna(value='')
return {'items': sequence.to_dict(orient='records') } #
```
without the .fillna(value='') it returns as:
```
{data: '{\n "x": [\n {\n "redacted",\n "redacted": "redacted...redacted"\n }\n ]\n}\n', status: 200, statusText: 'OK', headers: {…}, config: {…}, …}
```
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} | 2 | 2021-09-27T23:50:16Z | 2021-09-28T02:58:03Z | null | CONTRIBUTOR | null | ```
grep -r fused pandas/_libs/ --include '*.pyx'
pandas/_libs/writers.pyx:ctypedef fused pandas_string:
pandas/_libs/reshape.pyx:ctypedef fused reshape_t:
pandas/_libs/join.pyx:ctypedef fused join_t:
pandas/_libs/join.pyx:ctypedef fused asof_t:
pandas/_libs/join.pyx:ctypedef fused by_t:
pandas/_libs/algos.pyx:ctypedef fused algos_t:
pandas/_libs/algos.pyx:ctypedef fused rank_t:
pandas/_libs/algos.pyx:ctypedef fused diff_t:
pandas/_libs/algos.pyx:ctypedef fused out_t:
pandas/_libs/groupby.pyx:ctypedef fused add_t:
pandas/_libs/groupby.pyx:ctypedef fused rank_t:
pandas/_libs/groupby.pyx:ctypedef fused groupby_t:
```
cc @mzeitlin11 @jbrockmendel | {
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} | 2 | 2021-09-28T00:54:07Z | 2021-09-28T15:04:17Z | 2021-09-28T15:04:16Z | CONTRIBUTOR | null | Default values in stub files are not used by type checkers (and they might get out of sync with the implementation). | {
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} | 0 | 2021-09-28T03:52:33Z | 2021-09-28T23:50:19Z | 2021-09-28T23:47:38Z | MEMBER | null | Could probably be pushed further to avoid densifying doe `step != 1`, but this seemed like a good starting point as the most common case
```
before after ratio
[21c29919] [5622f636]
<master> <sparse_getitem_slice>
- 12.2±1ms 35.2±3μs 0.00 sparse.GetItem.time_slice
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https://api.github.com/repos/pandas-dev/pandas/issues/43778 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43778/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43778/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43778/events | https://github.com/pandas-dev/pandas/issues/43778 | 1,009,187,954 | I_kwDOAA0YD848Jvxy | 43,778 | DOC: Broken import in snippet (pandas.testing.assert_frame_equal) | {
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} | 0 | 2021-09-28T04:25:48Z | 2021-10-03T19:09:45Z | 2021-10-03T19:09:45Z | CONTRIBUTOR | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
I have checked that the issue still exists on the latest versions of the docs on master: https://pandas.pydata.org/docs/dev/reference/api/pandas.testing.assert_frame_equal.html
"pandas.testing.assert_frame_equal"
### Documentation problem
Example snippet for `assert_frame_equal` has a leading underscore in the import statement (for testing). I think this is unnecessary and breaks execution when attempted by a user.
```
from pandas._testing import assert_frame_equal
df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
```
### Suggested fix for documentation
Remove the underscore in question:
```
from pandas.testing import assert_frame_equal
df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
```
This should fix execution. | {
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} | 2 | 2021-09-28T04:43:38Z | 2021-10-03T19:10:08Z | 2021-10-03T19:09:45Z | CONTRIBUTOR | null | - [x ] closes #43778
Super small fix; removed single underscore in import statement. | {
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} | 1 | 2021-09-28T07:41:58Z | 2021-11-19T18:50:11Z | 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
import numpy as np
mi = pd.MultiIndex.from_tuples([(('a1','a2'),('b1','b2')), (('a1','a2'),('c1','c2'))])
df = pd.DataFrame(data=np.arange(2).reshape(1,2), columns=mi)
# This line works
df[(('a1','a2'),('b1','b2'))]
# This line would throw exception
df[[(('a1','a2'),('b1','b2'))]]
```
### Issue Description
When a data frame has columns of MultiIndex, and each level is a tuple. Selecting columns with array of column labels would raise `ValueError`.
> ValueError: Buffer has wrong number of dimensions
But selecting with a single label works.
### Expected Behavior
Return columns with no error.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.11.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1051-azure
Version : #53~18.04.1-Ubuntu SMP Fri Jun 18 22:32:58 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : None
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 : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
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 : 1.7.1
sqlalchemy : 1.4.23
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43781 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43781/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43781/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43781/events | https://github.com/pandas-dev/pandas/issues/43781 | 1,009,480,620 | I_kwDOAA0YD848K3Os | 43,781 | QST: Is there a function in pandas to do this composite time-indexed merge operation? | {
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- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
### Link to question on StackOverflow
https://stackoverflow.com/q/69348014/7317733
### Question about pandas
I have dataframes looking like this,
df1 :
```
X1 X2 X3 X4
2021-02-23 15:44:20.100 NaN 0 1 Nan
2021-02-23 15:44:20.300 NaN 0 1 Nan
2021-02-23 15:44:20.500 NaN 0 1 Nan
2021-02-23 15:44:20.700 NaN 0 1 Nan
2021-02-23 15:44:20.900 NaN 0 1 Nan
2021-02-23 15:44:21.100 NaN 0 1 Nan
2021-02-23 15:44:21.300 NaN 0 1 Nan
2021-02-23 15:44:21.500 NaN 0 1 Nan
2021-02-23 15:44:21.700 NaN 0 1 Nan
2021-02-23 15:44:21.900 NaN 0 1 Nan
```
df2 :
```
X1 X2 X3 X4
2021-02-23 15:44:20.000 0 0 1 Nan
2021-02-23 15:44:20.200 3 0 1 Nan
2021-02-23 15:44:20.400 NaN 0 1 Nan
2021-02-23 15:44:20.600 NaN 0 1 Nan
2021-02-23 15:44:20.800 NaN 0 1 3
2021-02-23 15:44:21.000 NaN 0 1 Nan
2021-02-23 15:44:21.200 NaN 0 1 3
2021-02-23 15:44:21.400 NaN 0 1 Nan
2021-02-23 15:44:21.600 NaN 0 1 4
2021-02-23 15:44:21.800 NaN 0 1 Nan
2021-02-23 15:44:22.000 NaN 0 1 Nan
2021-02-23 15:44:22.200 NaN 0 1 Nan
2021-02-23 15:44:22.400 NaN 0 1 Nan
2021-02-23 15:44:22.600 NaN 0 1 Nan
```
I want to perform a merge/join/concatenate operation on the values from columns of the left DataFrame (df1) to the Right Dataframe (df2). This would be searching for a row wise timedelta in the time index and replacing NaNs which might already be there...
Intuition: Kinda like a merge_asof but not creating _x and _y columns, instead replacing the values from the left df to the right.
Please note:
- The columns in the right dataframe are always exactly equal to the columns on the left dataframe.
- In the intuition.. `pandas.Dataframe.merge_asof()` does a left join but my case is different it is more like an outer join.
Resulting joined/merged DataFrame:
```
X1 X2 X3 X4
2021-02-23 15:44:20.100 0 0 1 Nan
2021-02-23 15:44:20.300 3 0 1 Nan
2021-02-23 15:44:20.500 NaN 0 1 Nan
2021-02-23 15:44:20.700 NaN 0 1 Nan
2021-02-23 15:44:20.900 NaN 0 1 3
2021-02-23 15:44:21.100 NaN 0 1 Nan
2021-02-23 15:44:21.300 NaN 0 1 3
2021-02-23 15:44:21.500 NaN 0 1 Nan
2021-02-23 15:44:21.700 NaN 0 1 4
2021-02-23 15:44:21.900 NaN 0 1 Nan
2021-02-23 15:44:22.000 NaN 0 1 Nan
2021-02-23 15:44:22.200 NaN 0 1 Nan
2021-02-23 15:44:22.400 NaN 0 1 Nan
2021-02-23 15:44:22.600 NaN 0 1 Nan
```
## What I have tried:
1. One method i tried is to resample the df1 and df2 with the frequency set as 'T' which basically ensures both the data-frames would have same keys if any. Then performing joins would be straight forward.
- Problem: Downsampling a df in pandas drops NaN rows by default and i am not aware of a technique to prevent it. I do not want to drop any data.. just replace.
2. merge_asof or traditional merges create duplicate columns with suffixes, which i do not want.
3. join and append just adds the rows of the df2 to df1 which is also not what i want.
## What i propose might work:
1. checking the second Dataframe manually for a row mathcing the timedelta and iterate over every row. This also is infeasible in my case as the dataframes in question are very large.
```
df1.shape -> (12200317 rows x 41 columns)
df2.shape -> (10000023 rows x 41 columns)
```
I also have multiple dataframes to go through. (approximately 50)
If there are any duplicates of this question .. I couldn't find one..
Thanks for any help. :)
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} | 4 | 2021-09-28T12:50:32Z | 2021-09-30T12:44:18Z | 2021-09-28T18:45:03Z | CONTRIBUTOR | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/index.html
### Documentation problem
It is difficult to access the documentation of prior pandas versions. https://pandas.pydata.org/docs/index.html has no links to prior versions docs or they are difficult to find.
When working with legacy code using older pandas versions, access to the docs of a specific version, especially the API reference, can be useful.
While it if possible to find the URL, e.g. https://pandas.pydata.org/pandas-docs/version/0.24.0/ with a bit of trial and error, a list of links to the prior versions docs would be useful.
### Suggested fix for documentation
Add a page in the docs with a list of links to prior versions docs. | {
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] | open | false | null | [] | null | 0 | 2021-09-28T14:37:42Z | 2021-10-02T00:36:49Z | null | NONE | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/development/extending.html
### Documentation problem
When you create a custom Dtype with Pandas and you need to use the to_sql (write to a sqlite database), it is unclear how you can handle the custom dtypes. If at all from the documentation.
This is the error you get with custom datatypes:
```File "pandas_libs\lib.pyx", line 1350, in pandas._libs.lib.infer_dtype
File "pandas_libs\lib.pyx", line 1229, in pandas._libs.lib._try_infer_map
AttributeError: 'MyCustomType' object has no attribute 'base'
```
So does something on the Dtype or on the ExtensionArray need to define `base` and what is base?
### Suggested fix for documentation
Explain if custom dtypes is supported with to_sql. | {
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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
pd.DatetimeIndex([pd.to_datetime('2020-10-25').tz_localize('Europe/Berlin')]) + pd.DateOffset(hours=3)
returns
2020-10-25 03:00:00+01:00
while
pd.to_datetime('2020-10-25').tz_localize('Europe/Berlin') + pd.DateOffset(hours=3)
returns
Timestamp('2020-10-25 02:00:00+0100', tz='Europe/Berlin')
```
### Issue Description
Mismatch between the outputs.
[Here](https://stackoverflow.com/questions/69364849/combine-first-at-dst-changing-day-fails/69365229?noredirect=1#comment122602688_69365229) is the stackoverflow post describing in detail the issue.
### Expected Behavior
Returning same datetime
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.3.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.4
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : 1.0.8
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.7
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 1 | 2021-09-28T17:50:58Z | 2021-10-18T11:52:43Z | 2021-10-16T15:37:51Z | CONTRIBUTOR | null | - [x] closes #43734
- [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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- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
### Link to question on StackOverflow
https://stackoverflow.com/questions/69366483/calculate-percentage-of-occurences-of-a-value-in-a-dataframe-column-based-on-ano
### Question about pandas
**Calculate percentage of occurences of a value in a dataframe column based on another column value.**
So my dataframe looks something like below :
<img width="660" alt="Screenshot 2021-09-28 at 11 27 23 PM" src="https://user-images.githubusercontent.com/1067818/135141118-ef6a9966-0d5f-4c21-a80c-ea080fc42a81.png">
I need to calculate percentage of "contestants" (one of the value in Result column) that are still "operating" (one of the values in OperatingState column). How can derive this in pandas ?
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} | 3 | 2021-09-28T20:17:45Z | 2021-10-06T18:11:42Z | 2021-10-06T01:02:47Z | CONTRIBUTOR | 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
- [ ] whatsnew entry
This PR improves performance of read_csv with memory_map=True and a UTF-8-encoded file by eliminating unnecessary `decode()` call. The code below demonstrates the speed improvement:
```
import timeit
import pandas as pd
def perftest_readcsv_memmap_utf8():
lines = []
for lnum in range(0x20, 0x10080, 0x80):
line = "".join([chr(c) for c in range(lnum, lnum + 0x80)]) + "\n"
try:
line.encode("utf-8")
except UnicodeEncodeError:
continue
lines.append(line)
df = pd.DataFrame(lines)
df = pd.concat([df for n in range(1000)], ignore_index=True)
fname = "test_readcsv_utf8.csv"
df.to_csv(fname, index=False, header=False, encoding="utf-8")
ti_rep = 5
ti_num = 10
time_dfmem = timeit.repeat(f'dfnomem = pd.read_csv("{fname}", header=None, memory_map=True, engine="c")',
setup='import pandas as pd',
repeat=ti_rep, number=ti_num)
print(f"Read CSV (memory_map=True), repeat={ti_rep}: {time_dfmem}")
print(f"Median: {pd.Series(time_dfmem).median()}")
perftest_readcsv_memmap_utf8()
```
On my machine the results are:
Without patch:
```
Read CSV (memory_map=True), repeat=5: [8.107480439008214, 8.100845866953023, 8.135483622085303, 8.090781628969125, 8.068992758984677]
Median: 8.100845866953023
```
With patch:
```
Read CSV (memory_map=True), repeat=5: [5.280414769076742, 5.290980814024806, 5.127453051973134, 5.150275847059675, 5.276113986037672]
Median: 5.276113986037672
```
The improved code runs in ~65% of time of the current code.
The spedup depends on the contents of the file; the test code above creates a 182 MB file containing almost all of Unicode Plane 0 and a several of Plane 1 characters UTF-8-encoded in 1, 2, 3 and 4 bytes; in this respect, it is a worst case. I also tested this patch on my 8.8 GB CSV file consisting of 1 and 2-byte encoded characters and the code ran in ~75% of time of the current code.
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} | 0 | 2021-09-28T22:10:20Z | 2021-09-28T23:37:37Z | 2021-09-28T23:35:11Z | 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
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} | 1 | 2021-09-28T22:14:56Z | 2021-09-30T21:24:40Z | 2021-09-29T18:03:01Z | MEMBER | null | Small step towards #43774
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https://api.github.com/repos/pandas-dev/pandas/issues/43790 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43790/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43790/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43790/events | https://github.com/pandas-dev/pandas/pull/43790 | 1,010,297,841 | PR_kwDOAA0YD84sa27y | 43,790 | TYP: enable reportMissingImports | {
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} | 10 | 2021-09-28T23:13:37Z | 2021-10-06T01:05:05Z | 2021-10-06T01:04:58Z | CONTRIBUTOR | null | reportMissingImports checks whether all libraries can be imported. This requires all dependencies to be installed on the CI that runs pyright.
This flag should probably NOT be enabled, as it would force all contributors to install all dependencies. I'm enabling it temporarily to see whether the CI has all dependencies installed.
edit:
The typing CI has not all packages installed:
```
/home/runner/work/pandas/pandas/pandas/io/clipboard/__init__.py
/home/runner/work/pandas/pandas/pandas/io/clipboard/__init__.py:150:14 - error: Import "qtpy.QtWidgets" could not be resolved (reportMissingImports)
/home/runner/work/pandas/pandas/pandas/io/clipboard/__init__.py:155:18 - error: Import "PyQt4.QtGui" could not be resolved (reportMissingImports)
/home/runner/work/pandas/pandas/pandas/io/clipboard/__init__.py:537:20 - error: Import "AppKit" could not be resolved (reportMissingImports)
/home/runner/work/pandas/pandas/pandas/io/clipboard/__init__.py:538:20 - error: Import "Foundation" could not be resolved (reportMissingImports)
/home/runner/work/pandas/pandas/pandas/io/clipboard/__init__.py:557:20 - error: Import "qtpy" could not be resolved (reportMissingImports)
/home/runner/work/pandas/pandas/pandas/io/clipboard/__init__.py:564:28 - error: Import "PyQt4" could not be resolved (reportMissingImports)
/home/runner/work/pandas/pandas/pandas/io/excel/_pyxlsb.py
/home/runner/work/pandas/pandas/pandas/io/excel/_pyxlsb.py:36:14 - error: Import "pyxlsb" could not be resolved (reportMissingImports)
/home/runner/work/pandas/pandas/pandas/io/excel/_pyxlsb.py:41:14 - error: Import "pyxlsb" could not be resolved (reportMissingImports)
``` | {
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} | 5 | 2021-09-28T23:22:31Z | 2021-11-14T02:24:15Z | null | NONE | null | - [x] closes #43732
- [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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} | 0 | 2021-09-29T04:14:58Z | 2021-09-29T16:39:02Z | 2021-09-29T12:59:54Z | MEMBER | null | Only reached for non-unique indexes. Makes a big difference for small indexes.
Room for de-duplication in follow-ups.
```
import numpy as np
import pandas as pd
np.random.seed(90)
arr = np.random.randn(4).repeat(2)
idx = pd.Index(arr)
assert not idx.is_unique
key = arr[4]
engine = idx._engine
%timeit engine.get_loc(key)
6.6 µs ± 50.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- master
2.62 µs ± 127 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
``` | {
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} | 6 | 2021-09-29T05:53:42Z | 2021-10-16T17:30:44Z | 2021-10-16T17:30:44Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
I whish the [pandas drop function](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html) would let me drop combination of rows (or columns) in multiindex dataframes.
E.g., I have a multiindex dataframe like this
| | | Column 1 | Column 2 |
| ------- | ------- | -------- | -------- |
| Index 1 | Index 2 | | |
| A | a | 1 | 2 |
| | b | 3 | 4 |
| B | a | 5 | 6 |
| | b | 7 | 8 |
And I want to drop only the Index combination ``(B, a)``.
With the current implementation of the drop function, this is not possible.
#### Describe the solution you'd like
Maybe adding an additional parameter called ``combination``, which is a boolean value, could solve the problem.
When setting ``combination=True``, the list-like ``label`` parameter could be recognized as the combination to drop, which would then result in
```python
df = df.drop(labels=["B", "a"], axis=0, combination=True)
df
```
| | | Column 1 | Column 2 |
| ------- | ------- | -------- | -------- |
| Index 1 | Index 2 | | |
| A | a | 1 | 2 |
| | b | 3 | 4 |
| B | b | 7 | 8 |
#### API breaking implications
I think adding the parameter would not break the API.
#### Describe alternatives you've considered
I have not considered alternatives
#### Additional context
For my problem, I implemented a function similar to the one below
```python
def drop(df: pd.DataFrame, index1: Optional[str]=None, index2: Optional[int]=None) -> pd.DataFrame:
if index1 is None:
if index2 is None:
# if no index1 and index2 is defined, return the original dataframe
return df
else:
return df.drop(index2, level="Index2").sort_index()
else:
if index2 is None:
return df.drop(index1, level="Index1").sort_index()
else:
a = df.drop(index1, level="Index1")
b = pd.concat([df.loc[index1].drop(index2, level="Index2")], keys=[index1], names=["Index1"])
return pd.concat([a, b]).sort_index()
```
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] | closed | false | null | [] | null | 2 | 2021-09-29T08:14:34Z | 2021-10-04T21:16:02Z | 2021-10-04T21:16:02Z | NONE | null | Question:
In older version of pandas, I got this:
df1
value
index
10 100
20 200
30 300
df2
value
index
40 100
50 300
60 600
by merge on "value" and set "left_index=True" I got
df3
value
index
10 100
30 300
but after update the library, it's illegal to do this,
if I set left_index=True, there won't be any intersection,
if I use "on" key word, I lost the original index
what is the solution?
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] | closed | false | null | [] | null | 4 | 2021-09-29T08:21:50Z | 2021-09-30T16:44:43Z | 2021-09-30T16:44:43Z | NONE | null | #### Is your feature request related to a problem?
Not really, there are ways around (as always).
#### Describe the solution you'd like
Allow `df.pipe()` to accept strings for pd.DataFrame built-in methods . For example, it would be very helpful to simply call
a pipeline as follows:
`df.pipe(f, arg_1=1).pipe("rank", ascending=True)`
I know I can call explicitly invoke the method
`df.pipe(f, arg_1=1).rank(ascending=True)`
but this feature could enable a more general usage of pipelines as outlined below:
```
pipes= [
(fun_1, dict(arg_1=0)),
(fun_2, dict(arg_1=1)),
("rank", dict(ascending=True))
]
for fun, kwargs in pipes:
df = df.pipe(fun, **kwargs)
```
#### API breaking implications
Not sure what to add here.
#### Describe alternatives you've considered
I can re-write most methods as standalone function but why if the built-in is so reach in features.
#### Additional context
see example above. | {
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} | 1 | 2021-09-29T08:35:54Z | 2021-09-29T13:14:11Z | 2021-09-29T12:55:28Z | CONTRIBUTOR | null | @jreback request to bring forwards part of #42947
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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
df = pd.DataFrame( {'timestamp': ['2021-03-28T00:37:28.952+01:00','2021-03-28T04:09:36.106+02:00'],'data': [1, 2]})
df = df.set_index('timestamp')
df.index = pd.to_datetime(df.index)
df.resample('H').size()
```
### Issue Description
`pandas.DataFrame.resample()` fails when index crosses a daylight savings boundary. Error as below
`TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'
`
### Expected Behavior
```
timestamp
2021-03-28 00:00:00+01:00 1
2021-03-28 01:00:00+01:00 0
2021-03-28 02:00:00+01:00 0
2021-03-28 03:00:00+02:00 0
2021-03-28 03:00:00+02:00 0
2021-03-28 04:00:00+02:00 1
Freq: H, dtype: int64
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-17-cloud-amd64
Version : #1 SMP Debian 4.19.194-2 (2021-06-21)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
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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
pd.read_csv("pandas_check.csv", engine="python")
# pandas_check.csv
id,name,description
ID001,Task_001,Task_001 – here for the testing
ID002,"Task_002" this is a test,Task_002 – here for the testing
```
### Issue Description
When the python engine is used in `read_csv` and a column contains a `"text" more text` after the delimiter then a parser error is thrown.
```
Traceback (most recent call last):
File "/home/development/my_project/.venv/lib/python3.8/site-packages/pandas/io/parsers/python_parser.py", line 742, in _next_iter_line
return next(self.data)
_csv.Error: ',' expected after '"'
```
### Expected Behavior
I would expect the column to be parsed without error as it is in the C engine
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.246-187.474.amzn2.x86_64
Version : #1 SMP Tue Sep 7 21:48:11 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.3
setuptools : 49.2.1
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
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jinja2 : 3.0.1
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pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
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gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
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 | 2021-09-29T17:06:54Z | 2021-10-03T22:31:01Z | 2021-10-03T22:30:51Z | CONTRIBUTOR | null | with numpy<1.21.0 pyright (and mypy?) cannot determine some types which leads to overlapping overloads. See https://github.com/pandas-dev/pandas/pull/43747#issuecomment-930343454
@attack68
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} | 2 | 2021-09-29T17:33:18Z | 2021-09-30T01:17:25Z | 2021-09-30T01:15:23Z | MEMBER | null | Perf-neutral, makes code-sharing slightly easier for the EA (in particular Nullable) Engine in the works. | {
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} | 7 | 2021-09-29T20:45:19Z | 2021-10-01T14:33:35Z | 2021-09-30T12:54:17Z | MEMBER | null | - [x] closes #43314
- [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 should cover the issue since the other pr did not make any progress.
Is there a fixture providing all scalar dtypes?
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} | 1 | 2021-09-29T20:45:49Z | 2021-09-30T01:06:12Z | 2021-09-29T22:55:43Z | 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
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] | open | false | null | [] | null | 2 | 2021-09-29T20:51:44Z | 2021-10-11T17:22:48Z | 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
.
```
### Issue Description
I tried to create a new development environment today.
Resolving the dependencies using `pip install -r requirements-dev.txt` took two hours before I cancelled it.
I then tried to use `pip-compile` from `pip install pip-tools` and I received the message:
```
There are incompatible versions in the resolved dependencies:
botocore>=1.11 (from -r requirements-dev.in (line 35))
botocore>=1.12.201 (from moto==2.2.8->-r requirements-dev.in (line 37))
botocore<1.22.0,>=1.21.51 (from boto3==1.18.51->-r requirements-dev.in (line 34))
botocore<1.20.107,>=1.20.106 (from aiobotocore==1.4.1->-r requirements-dev.in (line 72))
```
Having pinned `boto3>=1.17.106,<1.17.107` since that is what `aiobotocore` packages with, the next error was:
```
There are incompatible versions in the resolved dependencies:
fsspec<2021.6.0,>=0.7.4 (from -r requirements-dev.in (line 73))
fsspec==2021.09.0 (from gcsfs==2021.9.0->-r requirements-dev.in (line 74))
fsspec==2021.09.0 (from s3fs==2021.9.0->-r requirements-dev.in (line 71))
fsspec (from fastparquet==0.7.1->-r requirements-dev.in (line 67))
fsspec>=0.6.0 (from dask==2021.9.1->-r requirements-dev.in (line 26))
```
So then I pinned `gcsfs==2021.5.0` and `s3f2==2021.5.0`.
It then compiled and I was able to install packages.
(adding a third complaint: new pyright checks need numpy >1.21.0 but numba needs less than that)
### Expected Behavior
consistent deps.
### Installed Versions
master as of 29-sep-21. python 3.8, mac os. | {
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} | 11 | 2021-09-29T21:03:43Z | 2021-09-30T22:44:45Z | 2021-09-30T19:43:54Z | CONTRIBUTOR | null | `f"{val}"` is faster than `str(val)`. Utilizing this in the tight string conversion loop in `ensure_string_array` gives a nice performance boost.
```python
>>> int_arr = np.arange(1_000_000)
>>> %timeit pd.Series(int_arr, dtype="string")
850 ms ± 8.65 ms per loop # master
455 ms ± 8.65 ms per loop # this PR
>>> ser = pd.Series(int_arr)
>>> %timeit ser.astype("string")
890 ms ± 8.65 ms per loop # master
505 ms ± 8.65 ms per loop # this PR
```
The same improvement is given when we use `dtype=str`.
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} | 1 | 2021-09-29T22:08:11Z | 2021-09-30T20:13:26Z | 2021-09-30T19:42:32Z | 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
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} | 4 | 2021-09-29T22:37:26Z | 2021-09-30T19:49:48Z | 2021-09-30T19:49:48Z | 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
When I run mypy locally (this has happened for a while and I've been ignoring it) I get
pandas/_libs/tslibs/timestamps.pyi:160: error: Return type "Tuple[int, int, int]" of "isocalendar" incompatible with return type "_IsoCalendarDate" in supertype "datetime" [override]
pandas/_libs/tslibs/timestamps.pyi:160: error: Return type "Tuple[int, int, int]" of "isocalendar" incompatible with return type "_IsoCalendarDate" in supertype "date" [override]
```
```
### Issue Description
N/A
### Expected Behavior
N/A
### Installed Versions
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} | 0 | 2021-09-29T22:47:46Z | 2021-09-30T07:41:23Z | 2021-09-30T07:41:23Z | NONE | null | Backport PR #43213: BUG: groupby agg fails silently with mixed dtypes | {
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} | 5 | 2021-09-30T00:03:24Z | 2021-09-30T13:19:03Z | 2021-09-30T12:43:50Z | CONTRIBUTOR | null | Add a "Previous versions" block with link to https://pandas.pydata.org where links to the documentation of prior versions are available. See preview below.
- [x] closes #43782
- [ ] 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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} | 14 | 2021-09-30T08:00:06Z | 2021-11-08T14:44:08Z | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [ ] 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.DataFrame(
{
"temp_playlist": [0, 0, 0, 0],
"objId": ["o1", np.nan, "o1", np.nan],
"x": [1, 2, 3, 4],
}
)
agg_df = df.groupby(by=['temp_playlist', 'objId'], dropna=False)["x"].agg(list)
print(agg_df.loc[agg_df.index[-1]]) # KeyError: because it is (0, np.nan), wanted to get [2, 4]
```
### Issue Description
This issue is a follow-up of the discussion in this [SO question](https://stackoverflow.com/questions/69373456/get-values-from-dataframe-with-multiindex-index-containg-nans?noredirect=1#comment122618699_69373456).
It appears to be a bug, but if not, meaning, if this is desired behavior it should be documented.
As shown in the `Reproducible Example`, after grouping `x` data on the `temp_playlist` and `objId` columns, there is a MultiIndex `(0, nan)`. This index is meaningful and I wanted to access the data via it as I can perform with any other index from `agg_df.index` as `agg_df.loc[<index_pos>]`. This is not possible for the index containing the `nan` (`agg_info_df.loc[agg_info_df.index[-1]]`). However, it does work if that same index is provided in a list of indices. So this seems at least inconsistent if not a bug entirely.
For more info, please consult the SO question, especially [this answer](https://stackoverflow.com/a/69375568/14094460).
### Expected Behavior
`agg_info_df.loc[(0, np.nan)]` should return `[2, 4]`
### Installed Versions
python 3.8.5, pandas 1.3.1, numpy 1.20.3 | {
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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
### Notebook Cell 1 ###
import pandas as pd
data = pd.DataFrame([i for i in range(100)], index=pd.date_range(start="2000-01-01", freq="1H", periods=100), columns=["data_col_1"])
### Notebook Cell 2 ###
data
### Notebook Call 3 ###
data = data.at_time("10:00")
data["data_col_2"] = 0
data.loc[data["data_col_1"] > 0.0, "data_col_2"] = 1
```
### Issue Description
If I run the 3 notebook cells above, I get the following pandas warning:
_A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead_
However, if I only run cell 1 and 3, no warning is triggered. The only difference is cell 2 where we just print the DataFrame. To be more specific, running `print(data)` instead of just `data` also does not trigger the warning.
I do not believe this to be a jupyter notebook bug but it seems that by simply running a cell with `data`, the state of the DataFrame is in some way modified which is definitely not expected behavior. Probably this bug is due to some pandas styling functions that are triggered in the background when a cell is run on a DataFrame.
As a side note, the bug also does not appear when all the code is run in one single cell (probably because no DataFrame is printed) or when the line `data = data.at_time("10:00")` is omitted.
### Expected Behavior
The triggering of the warning should not be dependent on whether you run cell 2 or not.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-37-generic
Version : #41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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} | 2 | 2021-09-30T14:32:07Z | 2021-10-03T14:14:10Z | 2021-10-03T14:14:03Z | CONTRIBUTOR | null | - [🗸] closes #43755
- [🗸] 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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] | closed | false | null | [] | null | 4 | 2021-09-30T15:14:30Z | 2021-11-19T17:24:02Z | 2021-11-19T17:24:01Z | NONE | null | I have two dataframes:
The first one looks like this:
| | | variable |
|:----: |:------: | :-----:|
| entry | subentry | |
| 0 | 1 | X |
| | 2 | Y |
| | 3 | Z |
and the second one looks like:
| | | variable |
|:----: |:------: | :-----:|
| entry | subentry | |
| 0 | 1 | A |
| | 2 | B |
I would like to merge the two dataframe such that I get:
| | | variable |
|:----: |:------: | :-----:|
| entry | subentry | |
| 0 | 1 | X |
| | 2 | Y |
| | 3 | Z |
| 1 | 1 | A |
| | 2 | B |
Simply using ```df1.append(df2, ignore_index=True)``` gives
| |variable |
|:----: |:-----:|
| | |
| 0 |X |
| 1 | Y |
| 2 | Z |
| 3 | A |
| 4 | B |
In other words, it appends on the highest level (level 1) in the dataframes. I think it important to allow the user dealing with mutli-index dataframes to specify which level of the dataframe to append on. In the simple example above, this would allow me to specify "entry" as the index to append on.
Here is a code sinppet that will reproduce the problem:
```
arrays = [
np.array([0,0,0]),
np.array([0,1,2]),]
arrays_2 = [
np.array([0,0]),
np.array([0,1]),]
df1 = pd.DataFrame(np.random.randn(3, 1), index=arrays)
df2 = pd.DataFrame(np.random.randn(2, 1), index=arrays_2)
df = df1.append(df2, ignore_index=True)
print(df)
```
In practice, I am looking ao combine N dataframes, each with a different number of "entry" rows. So I am looking for an approach that will not rely on me knowing the exact of the dataframes I am combining. | {
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} | 0 | 2021-09-30T16:50:27Z | 2021-09-30T20:22:43Z | 2021-09-30T19:42:00Z | MEMBER | null | ```
import numpy as np
from pandas._libs.internals import BlockPlacement
arr = np.arange(5, dtype=np.intp)
%timeit BlockPlacement(arr)
93.3 ns ± 0.203 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <- PR
1.71 µs ± 40.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <- master
```
```
import numpy as np
import pandas as pd
np.random.seed(92342)
arr = np.random.randn(100, 100)
df = pd.DataFrame(arr)
mgr = df._mgr
%timeit mgr._rebuild_blknos_and_blklocs()
6.47 µs ± 132 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
13.4 µs ± 703 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- master
``` | {
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"html_url": "https://github.com/pandas-dev/pandas/pull/43822",
"merged_at": "2021-09-30T19:42:00Z",
"patch_url": "https://github.com/pandas-dev/pandas/pull/43822.patch",
"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/43822"
} |
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