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MDU6SXNzdWU2MTI5ODg4NjQ=
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BUG:Dtypes changed from Int to Float when merging for different colunm names with Left_on Right_on outter join
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| 2020-09-04T13:11:53Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
#Different label names 'A' and 'a'
testa = pd.DataFrame([{"A":1,"b":2}])
testb = pd.DataFrame([{"a":2,"c":2}])
result = pd.merge(testa, testb, left_on='A', right_on='a', how="outer")
result.dtypes
Out[1]:
A float64
b float64
a float64
c float64
dtype: object
#Same lable name with on[''] methond looks fine
testa = pd.DataFrame([{"a":1,"b":2}])
testb = pd.DataFrame([{"a":2,"c":2}])
result1 = pd.merge(testa, testb, on=["a"], how="outer")
result1.dtypes
Out[2]:
a int64
b float64
c float64
dtype: object
```
#### Problem description
I was merging some df with the zipcode, so I found this might be a problem. Maybe I am wrong about this, and probably pandas designed this way for some reasons? I don't know. If anyone knows the reasons why it had built this way, please share.
I also came across with this post: https://github.com/pandas-dev/pandas/issues/9958
#### Expected Output
I expecte there's no data type changes after merging.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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| 34,018
|
PERF: Reindexing ffill DatetimeIndexes with different timezones
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| 2020-05-06T07:16:09Z
| 2021-08-07T05:47:33Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
idx = pd.date_range("2020/01/01", freq="s", periods=500000, tz="America/New_York")
idx_naive = pd.date_range("2020/01/01", freq="s", periods=500000, tz=None)
idx_same_tz = pd.date_range("2020/01/01", freq="s", periods=400000, tz="America/New_York")
idx_diff_tz = pd.date_range("2020/01/01", freq="s", periods=400000, tz="Asia/Tokyo")
print(idx.dtype, idx_naive.dtype, idx_same_tz.dtype, idx_diff_tz.dtype)
# fast:
In [11]: %%time
...: idx.get_indexer(idx_same_tz)
...:
...:
CPU times: user 15.6 ms, sys: 0 ns, total: 15.6 ms
Wall time: 20.3 ms
Out[11]: array([ 0, 1, 2, ..., 399997, 399998, 399999])
# ~400x slower:
In [12]: %%time
...: idx_naive.get_indexer(idx_diff_tz)
...:
...:
CPU times: user 5.38 s, sys: 188 ms, total: 5.56 s
Wall time: 5.57 s
Out[12]: array([-1, -1, -1, ..., -1, -1, -1])
In [13]: %%time
...: idx.get_indexer(idx_diff_tz)
...:
...:
CPU times: user 7.94 s, sys: 188 ms, total: 8.12 s
Wall time: 8.12 s
Out[13]: array([ -1, -1, -1, ..., 349597, 349598, 349599])
```
#### Problem description
Re-indexing two `DatetimeIndex` with different time-zones, e.g. `pd.Series(index=idx, dtype="float64").reindex(idx_diff_tz, method="ffill")`, is significantly slower than when the two indexes have the same timezone. This is due to a cast of both inputs to `"object":
```python
In [22]: %load_ext line_profiler
In [23]: %lprun -f pd.core.indexing.Index.get_indexer idx.get_indexer(idx_diff_tz)
Total time: 10.3548 s
File: $CONDA_PREFIX/lib/python3.8/site-packages/pandas/core/indexes/base.py
Function: get_indexer at line 2706
Line # Hits Time Per Hit % Time Line Contents
==============================================================
2706 @Appender(_index_shared_docs["get_indexer"] % _index_doc_kwargs)
2707 def get_indexer(self, target, method=None, limit=None, tolerance=None):
# .. snipped for brevity
2725 2 54.0 27.0 0.0 if not is_dtype_equal(self.dtype, target.dtype):
2726 1 4924773.0 4924773.0 47.6 this = self.astype(object)
2727 1 3741150.0 3741150.0 36.1 target = target.astype(object)
```
Relatively minor: its reasonable to expect users to ensure their data is in the same timezone, and its easy to workaround -- `idx.tz_convert(idx_diff_tz.tz)` would suffice. Still, it could be a bit of a gotcha if a user is using `.reindex()` to convert data (e.g. from across different API boundaries) to the same timezone, so thought it was worth filing an issue.
Potential fix could be:
* checking if `this` and `target` are both `datetime64[??]` in the above snippet and casting `this = this.astype(target.dtype)`
* doing something similar in `Index._possibly_promote()`
#### Expected Output
Similar performance between reindexing with same and different timezones
#### Output of ``pd.show_versions()``
Confirmed performance discrepancy from pandas 0.17 through 1.0.3:
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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BUG: Multiindex.nunique raises NotImplementedError
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| 2020-05-06T08:42:04Z
| 2021-08-07T05:48:05Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
pd.DataFrame([[1,2],[1,2]]).set_index([0,1]).index.nunique()
```
```python
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-80-09716b89b157> in <module>
----> 1 pd.DataFrame([[1,2],[1,2]]).set_index([0,1]).index.nunique()
/opt/conda/lib/python3.7/site-packages/pandas/core/base.py in nunique(self, dropna)
1284 uniqs = self.unique()
1285 n = len(uniqs)
-> 1286 if dropna and isna(uniqs).any():
1287 n -= 1
1288 return n
/opt/conda/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in isna(obj)
124 Name: 1, dtype: bool
125 """
--> 126 return _isna(obj)
127
128
/opt/conda/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _isna_new(obj)
136 # hack (for now) because MI registers as ndarray
137 elif isinstance(obj, ABCMultiIndex):
--> 138 raise NotImplementedError("isna is not defined for MultiIndex")
139 elif isinstance(obj, type):
140 return False
NotImplementedError: isna is not defined for MultiIndex
```
exactly same error raised by pandas 0.25.3
#### Problem description
The method exists, but always throws NotImplementedError. I can use len(df.index.unique()) as a substitute. In the previous version of pandas, documentation have Multiindex.nunique entry (https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.MultiIndex.unique.html), but it has disappeared now. So I think it might be possible that this is not a bug, but an intended exception.
#### Expected Output
should be 1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3.post20200325
Cython : 0.29.17
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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REGR: Series.update raises ValueError with tz-aware types
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| 3
| 2020-05-06T11:10:28Z
| 2020-06-15T11:44:42Z
| 2020-06-15T11:44:41Z
|
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 of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print(pd.__version__)
dti = pd.date_range("1/1/2000", periods=3, tz="Europe/London")
a = pd.Series([dti[0], None, dti[2]])
b = pd.Series([None, dti[1], None])
a.update(b)
print(a)
```
#### Problem description
on master
```
1.1.0.dev0+1468.g3cebe3024
Traceback (most recent call last):
File "C:/Users/simon/test.py", line 10, in <module>
a.update(b)
File "c:\users\simon\pandas\pandas\core\series.py", line 2855, in update
self._mgr = self._mgr.putmask(mask=mask, new=other)
File "c:\users\simon\pandas\pandas\core\internals\managers.py", line 544, in putmask
return self.apply(
File "c:\users\simon\pandas\pandas\core\internals\managers.py", line 397, in apply
applied = getattr(b, f)(**kwargs)
File "c:\users\simon\pandas\pandas\core\internals\blocks.py", line 1604, in putmask
new_values[mask] = new
File "c:\users\simon\pandas\pandas\core\arrays\datetimelike.py", line 609, in __setitem__
self._data[key] = value
ValueError: NumPy boolean array indexing assignment cannot assign 3 input values to the 1 output values where the mask is true
```
regression in #27428
d717ad8157d2e0713cde89e10743cf4362294027 is the first bad commit
commit d717ad8157d2e0713cde89e10743cf4362294027
Author: jbrockmendel <jbrockmendel@gmail.com>
Date: Tue Jul 23 16:55:40 2019 -0700
CLN/REF: stop allowing iNaT in DatetimeBlock (#27428)
cc @jbrockmendel
#### Expected Output
on 0.25.3
```
0.25.3
0 2000-01-01 00:00:00+00:00
1 2000-01-02 00:00:00+00:00
2 2000-01-03 00:00:00+00:00
dtype: datetime64[ns, Europe/London]
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
|
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MDExOlB1bGxSZXF1ZXN0NDE0MDMxMDIw
| 34,021
|
TST: query with timezone aware index & column
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| 5
| 2020-05-06T11:14:28Z
| 2020-05-20T05:47:18Z
| 2020-05-20T05:40:37Z
|
CONTRIBUTOR
| null |
- [X] closes #29463
- [X] tests added / passed
- [X] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #33513 on branch 1.0.x (BUG: Fix Categorical.min / max bug)
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|
MEMBER
| null |
xref #33513
regression in #27929 (i.e. 1.0.0) https://github.com/pandas-dev/pandas/issues/33450#issuecomment-624625326
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Backport PR #33089 on branch 1.0.x (BUG: Don't cast nullable Boolean to float in groupby)
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| 0
| 2020-05-06T13:34:58Z
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|
MEMBER
| null |
xref #33089
regression in 1.0.0, xref https://github.com/pandas-dev/pandas/issues/32194#issuecomment-604664466
# NOTE: same code fix, different location
cc @dsaxton
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MDExOlB1bGxSZXF1ZXN0NDE0MTI4MDUz
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CLN: remove _typ checks in Timestamp methods
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Backport PR #32905 on branch 1.0.x (Fix to _get_nearest_indexer for p…
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MEMBER
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…ydata/xarray#3751)
xref #32905
regression in 1.0.2 (backport of #31511)
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MDU6SXNzdWU2MTMzNjc0OTU=
| 34,026
|
DOC: Series.str.rstrip() and Series.str.lstrip() have incorrect description at the top
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| 2020-05-06T14:32:01Z
| 2020-05-16T19:13:46Z
| 2020-05-16T19:13:46Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.rstrip.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.lstrip.html
#### Documentation problem
At the top of the documentation for the `rstrip()` and `lstrip()` methods, it says "Remove leading and trailing characters."
#### Suggested fix for documentation
It should say "Remove trailing characters" for `rstrip()` and "Remove leading characters" for `lstrip()`.
This is due to the way that the shared docs are set up between `str.lstrip()`, `str.rstrip()` and `str.strip()`
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MDExOlB1bGxSZXF1ZXN0NDE0MTQwOTE2
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|
Backport PR #33693 on branch 1.0.x (BUG: Fix memory issues in rolling…
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| 2020-05-06T14:41:33Z
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MEMBER
| null |
….min/max)
xref https://github.com/pandas-dev/pandas/pull/33693#issuecomment-619536995
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MDU6SXNzdWU2MTM0NTQ1OTY=
| 34,028
|
What is the best way to normalize_json before read_json for the file with gigabytes size?
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[] | null | 1
| 2020-05-06T16:33:59Z
| 2020-05-08T21:27:34Z
| null |
NONE
| null |
Hello! Thank you for pandas! It's great!
Is there an elegant way to do `normalize_json` before `read_json` in stream/chunks (to avoid loading a big gygabyte file to the memory and then normalizing and then read_json)?
Thanks!
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MDU6SXNzdWU2MTM0NjA4OTA=
| 34,029
|
BUG: unpicking panda data frame requires "latin1" encoding
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[] | null | 2
| 2020-05-06T16:44:21Z
| 2021-08-07T05:51:16Z
| 2021-08-07T05:51:16Z
|
NONE
| null |
- [Y ] I have checked that this issue has not already been reported.
- [ N] I have confirmed this bug exists on the latest version of pandas.
- [ N] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
#!/usr/bin/env python3
import pandas as pd
import pickle
df = pd.read_csv("Factory_Entries_AWS.csv")
with open("Factory_Entries_AWS_p3.pkl","wb") as f:
pickle.dump(df, f)
with open("Factory_Entries_AWS_p3.pkl","rb") as f:
df1 = pickle.load(f)
pd.testing.assert_frame_equal(df.sort_index(axis=1), df1.sort_index(axis=1), check_names=True)
with open("Factory_Entries_AWS_p2.pkl","rb") as f:
df2 = pickle.load(f, encoding='bytes')
pd.testing.assert_frame_equal(df.sort_index(axis=1), df2.sort_index(axis=1),check_names=True)
```
#### Problem description
The above code fails when unpickling data frame from pickle file created with python2 with the following error:
```
# python3 p3.py
Traceback (most recent call last):
File "p3.py", line 18, in <module>
df2 = pickle.load(f, encoding='bytes')
File "/usr/local/lib/python3.6/site-packages/pandas-1.0.1-py3.6-linux-x86_64.egg/pandas/core/indexes/base.py", line 174, in _new_Index
return cls.__new__(cls, **d)
TypeError: __new__() keywords must be strings
```
[this should explain **why** the current behavior is a problem and why the expected output is a better solution]
replacing `'bytes'` with `'latin1'` solves the problem. The prescribed method of unpicking python2 objects in python3 is to use `'bytes'` encoding. Why does it fail here?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.3
setuptools : 46.1.3
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDE0MjE3ODU1
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TST: Add Series.update ExtensionArray tests
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| 4
| 2020-05-06T16:58:23Z
| 2020-05-10T17:39:22Z
| 2020-05-10T17:09:38Z
|
MEMBER
| null |
cc @simonjayhawkins
xref https://github.com/pandas-dev/pandas/issues/25744 https://github.com/pandas-dev/pandas/issues/34020
I think the dtype issue for DataFrame.update with categorical columns mentioned in the first issue is still unaddressed, so likely don't want to only close and lose that (perhaps break into its own issue)?
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CLN: Use to_numpy directly in cov / corr
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PERF: use is_tick_offset to check for Tick
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MDExOlB1bGxSZXF1ZXN0NDE0MjU4ODcy
| 34,033
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CLN: address TODOs and FIXMEs
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MDU6SXNzdWU2MTM1NTUxNTg=
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setting values in a dataframe with duplicated keys
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| 13
| 2020-05-06T19:23:19Z
| 2020-05-11T00:33:39Z
| 2020-05-11T00:33:39Z
|
NONE
| null |
I have a dataframe with duplicated keys, and I need to assign values to some column of that dataframe, for some different keys. Dataframe indexes support duplicated keys, so I assume that this kind of work should be easy, if not I don't see why dataframes should be allowed to have duplicated keys. Anyway, setting the values like this:
df.loc[key, 'column']= vector
gives me the following error:
ValueError: Must have equal len keys and value when setting with an iterable
even if the number of times `key` appears in the index of `df` is equal to the length of `vector`. I think this should be fixed.
Thank you,
c. foschi
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MDU6SXNzdWU2MTM2MDU4MTE=
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BUG: wrong join with nan in multiindex
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| 2020-05-06T20:52:37Z
| 2020-09-04T13:12:22Z
| 2020-09-04T13:12:22Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
df1 = pd.DataFrame([['1.','3.','4.'],
['1.',pd.NA,'5.']],
columns=['i1', 'i2', 'v1']).set_index(['i1', 'i2'])
df2 = pd.DataFrame([['1.',pd.NA,'6.']],
columns=['i1', 'i2', 'v2']).set_index(['i1', 'i2'])
df1.join(df2, how='outer').loc[('1.', '3.')]['v2']
```
#### Problem description
the result is '6.'
so, the value of v2 in df2 appears as the value of v2 for index ('1.', '3.') after joining
this is unexpected for me, because the index is different from that in df2 ('1.',pd.NA)
this problem is also there with np.nan instead of pd.NA
and also in version 0.25.3 (when values are strings, but not when values are floats!)
#### Expected Output
NaN
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.44.1
</details>
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CI: Pin Cython on Numpy Dev
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| 1
| 2020-05-06T21:19:23Z
| 2020-05-06T22:04:33Z
| 2020-05-06T22:04:29Z
|
CONTRIBUTOR
| null |
- [x] ref https://github.com/pandas-dev/pandas/issues/34014
Until we fix the above issue with cython 3.0a4
cc @jreback
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MDU6SXNzdWU2MTM2NDA0NDg=
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BUG: "min_period" and "min_periods" problem in df.groupby().rolling
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| 2020-05-06T22:01:08Z
| 2021-08-17T00:20:32Z
| 2021-08-17T00:20:32Z
|
NONE
| null |
- [y] I have checked that this issue has not already been reported.
- [y] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
name_l = ["Alice"] * 5 + ["Bob"] * 5
val_l = [np.nan,np.nan,1,2,3]+[np.nan,1,2,3,4]
test_df = pd.DataFrame([name_l,val_l]).T
test_df.columns = ["name","val"]
# correct one
test_df.groupby("name")["val"].rolling(window=2,min_periods=1).sum()
# wrong one with "min_period” parameter
test_df.groupby("name")["val"].rolling(window=2,min_period=1).sum()
```
#### Problem description
The one with min_period does not work the same as min_periods and should not be allowed.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
The one with min_period as parameter should produce an error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-7629-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.7.3
pip : 20.1
setuptools : 46.1.3
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : None
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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MDU6SXNzdWU2MTM2NDk3Mjk=
| 34,038
|
BUG: Pandas Cython pd.sort_values() function, ignore_index DOESN'T WORK
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[] | null | 7
| 2020-05-06T22:22:27Z
| 2020-05-14T18:40:00Z
| 2020-05-14T18:40:00Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
yes
- [ ] I have confirmed this bug exists on the latest version of pandas.
yes
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
idk
<img width="646" alt="Capture d’écran 2020-05-07 à 00 12 24" src="https://user-images.githubusercontent.com/60808981/81234096-635e3080-8ff8-11ea-9f53-3c9709a66f34.png">
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
The code works on python3, but not on Cython. It works when I delete "ignore_index=True" from my code, but then the output is obviously wrong :(
(sorry for censoring, I don't want to leak anything)
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
ignore_index should work on Cython
#### Output of ``pd.show_versions()``
1.0.3
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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| 613,662,085
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MDExOlB1bGxSZXF1ZXN0NDE0MzczNzU0
| 34,039
|
CLN: spelling fixes in docstrings
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| 3
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| 2020-05-08T17:36:22Z
| 2020-05-08T17:35:40Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDE0NDAwOTcy
| 34,040
|
PERF: use isinstance(obj, Foo) instead of ABCFoo
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| 1
| 2020-05-07T00:38:35Z
| 2020-05-09T19:31:07Z
| 2020-05-09T19:21:20Z
|
MEMBER
| null |
per discussion in #27353, isinstance checks with the ABCFoo are about 4x as expensive as the Foo checks. Individually they are still very small, but they add up.
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MDExOlB1bGxSZXF1ZXN0NDE0NDAxNTk2
| 34,041
|
CLN: remove normalize_date, but really this time
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| 2020-05-07T00:41:29Z
| 2020-05-07T03:01:41Z
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| null |
#34016 removed all the references to it, but I forgot the actual function
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MDExOlB1bGxSZXF1ZXN0NDE0NDE2MTEz
| 34,042
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REF: use unpack_zerodim_and_defer on EA methods
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| 2021-11-20T23:22:41Z
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MDExOlB1bGxSZXF1ZXN0NDE0NDIxOTIz
| 34,043
|
PERF: cdef UTC
|
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| 0
| 2020-05-07T02:13:31Z
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|
MEMBER
| null |
This won't make a big difference, but it will avoid needing to go to python-space for these lookups.
|
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MDU6SXNzdWU2MTM4OTY5NTk=
| 34,044
|
BUG: maximum recursion depth in eval when using bool object
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| 2020-05-07T09:09:04Z
| 2020-06-17T07:26:21Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"col1": [True] * 50, "col2": [2] * 50})
df["col1"] = df["col1"].astype("object")
df.eval("col1 & (col2!=2)")
```
#### Problem description
```
RecursionError: maximum recursion depth exceeded while calling a Python object
```
"col1" in the example is casted to object bool since in my real example I have nan.
Maybe similar to #27639
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.15-200.fc31.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.2
dateutil : 2.7.5
pip : 20.1
setuptools : 41.6.0
Cython : 0.29.15
pytest : 4.0.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.3.0
pandas_datareader: 0.8.0
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.2
tabulate : 0.8.5
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.1.2
xlsxwriter : None
numba : 0.48.0
</details>
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MDU6SXNzdWU2MTM5NTI0NTM=
| 34,045
|
BUG: eval not working with fillna
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[] | null | 4
| 2020-05-07T10:37:59Z
| 2020-05-17T18:54:13Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
import pandas as pd
d = pd.DataFrame([1, 2], columns=['a'])
d.eval('((a-1)/(a-1)).fillna(0)')
```
#### Problem description
With eval, fillna function raises an obscure error
```python
>>> d.eval('(a-1)/(a-1)')
0 NaN
1 1.0
Name: a, dtype: float64
>>> d.eval('((a-1)/(a-1)).fillna(0)')
Traceback (most recent call last):
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\frame.py", line 3346, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\eval.py", line 332, in eval
parsed_expr = Expr(expr, engine=engine, parser=parser, env=env)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 764, in __init__
self.terms = self.parse()
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 781, in parse
return self._visitor.visit(self.expr)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 375, in visit
return visitor(node, **kwargs)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 381, in visit_Module
return self.visit(expr, **kwargs)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 375, in visit
return visitor(node, **kwargs)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 384, in visit_Expr
return self.visit(node.value, **kwargs)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 375, in visit
return visitor(node, **kwargs)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 622, in visit_Call
res = self.visit_Attribute(node.func)
File "C:\Users\ErenAkbiyik\.conda\envs\work\lib\site-packages\pandas\core\computation\expr.py", line 607, in visit_Attribute
resolved = self.visit(value).value
AttributeError: 'Div' object has no attribute 'value'
```
#### Expected Output
```python
>>> d.eval('(a-1)/(a-1)')
0 0.0
1 1.0
Name: a, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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ENH: piped alias for .merge(left_index=True,right_index=True,how='outer')
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[] | null | 4
| 2020-05-07T10:41:10Z
| 2020-09-04T13:13:38Z
| 2020-09-04T13:13:37Z
|
NONE
| null |
#### Is your feature request related to a problem?
The way to merge two dataframes by index and default 'outer' is to use concat(axis=1), but this does not work if I want to follow a piped workflow. I would like to do the equivalent of df.merge(left_index=True,right_index=True,how='outer') but in a less verbose way.
#### Describe the solution you'd like
```python
def merge_byindex(self,df2,how='outer',**kwargs):
"""Merge input DataFrame by index.
It is equivalent to doing pd.DataFrame.merge(left_index=True,right_index=True,how='outer',**kwargs).
"""
return self.merge(df2,left_index=True,right_index=True,how='outer',**kwargs)
```
#### API breaking implications
no breaking implications
#### Describe alternatives you've considered
The natural alternative is to just keep doing .merge(left_index=True,right_index=True,how='outer')
#### Additional context
The reasons for having this as a separate function are the following:
1. The level of verbosity required to do this simple operation seems unreasonable.
2. Seems that maybe this particular operation could be more efficient than a general merge.
3. It would greatly simplify the documentation for this particular operation
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BUG: cummin, max, sum not valid when used with agg + NaN not skipped.
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| 2
| 2020-05-07T13:44:07Z
| 2021-07-28T01:56:44Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
from random import seed, randint
# Data
kp = pd.period_range(start='2020-01-01 00:00', end='2020-01-01 00:25', freq='5T')
sp = pd.period_range(start='2020-01-01 00:00', end='2020-01-01 00:25', freq='1h')
seed(1)
values = [randint(0,10) for p in kp]
dft = pd.DataFrame({'Values' : values}, index=kp)
dft.loc[kp[-2]] = np.nan
# Trouble 1: `cummin`, `cummax`, `cumsum` not available through `agg`?
resampler = dft.resample(sp.freqstr)
progress = resampler.agg('cummin')
```
Get (same with `cummax` and `cumsum`):
AttributeError: 'cummin' is not a valid function for 'PeriodIndexResampler' object.
```
# Trouble 2: `cummin`, `cummax`, `cumsum` appear to work when used in a dict,
# but not skipna parameter. Whatever `skipna` (`True` or `False`) result is the same.
resampler = dft.resample(sp.freqstr)
progress = resampler.agg({('Values','cummin')},skipna=False)
```
#### Output obtained
progress
Values
Values
2020-01-01 00:00 2.0
2020-01-01 00:05 2.0
2020-01-01 00:10 1.0
2020-01-01 00:15 1.0
2020-01-01 00:20 NaN
2020-01-01 00:25 1.0
#### Problem description
It appears `cumsum`, `cummin`, `cumsum` cannot be used directly with `agg`. The documentation appears to state differently:
"Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply."
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.resample.Resampler.aggregate.html
#### Expected Output
For trouble 1 :
`progress` (with `cummin`)
Values
2020-01-01 00:00 2.0
2020-01-01 00:05 2.0
2020-01-01 00:10 1.0
2020-01-01 00:15 1.0
2020-01-01 00:20 NaN
2020-01-01 00:25 1.0
For trouble 2:
`progress` (with `cummin` & `skipna=False`)
Values
2020-01-01 00:00 2.0
2020-01-01 00:05 2.0
2020-01-01 00:10 1.0
2020-01-01 00:15 1.0
2020-01-01 00:20 NaN
2020-01-01 00:25 NaN
As a side question, is it possible to have an additional parameter `fill_value=0` to have then following output?
`progress` (with `cummin` & `fill_value=0`)
Values
2020-01-01 00:00 2.0
2020-01-01 00:05 2.0
2020-01-01 00:10 1.0
2020-01-01 00:15 1.0
2020-01-01 00:20 1.0
2020-01-01 00:25 1.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-51-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.0.3
numpy : 1.16.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDE0NzAzMzQ3
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|
Bug in DataFrame.replace casts columns to ``object`` dtype if items in ``to_replace`` not in values
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- [ ] closes #32988
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Bug in Series.groupby would raise ValueError when grouping by PeriodIndex level
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MEMBER
| null |
- [ ] closes #34010
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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|
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CI: check_freq=False
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| 5
| 2020-05-07T16:36:37Z
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| null |
Aimed at a test that has been periodically failing in the CI for a week or so.
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|
BUG: DataFrameGroupBy.sum ignores min_count for boolean data type
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| 1
| 2020-05-07T17:16:15Z
| 2020-05-11T18:53:16Z
| 2020-05-11T18:53:16Z
|
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 of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Behavior is from master:
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 2], "b": pd.array([True, True])})
df.groupby("a").sum(min_count=2)
```
gives
```python
b
a
1 True
2 True
```
but expected output is
```python
b
a
1 <NA>
2 <NA>
```
It looks to me like there's an attempt to compute a Cythonized result which fails, after which point the min_count argument is forgotten.
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PERF: Use Indexers to implement groupby rolling
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| 10
| 2020-05-07T18:26:48Z
| 2020-08-04T16:40:50Z
| 2020-05-25T17:26:20Z
|
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| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Currently, `grouby.rolling` is implemented essentially as `groupby.apply(lambda x: x.rolling())` which can be potentially slow.
This PR implements `groupby.rolling` by calculating bounds with a `GroupbyRollingIndxer` and using the rolling aggregations in cython to compute the results.
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more informative error message with np.min or np.max on unordered Categorical
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| 1
| 2020-05-07T18:44:23Z
| 2020-05-26T09:27:21Z
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|
MEMBER
| null |
- [ ] closes #33115
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDE0ODk4Mzg3
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REF: implement ABCTimestamp, ABCTimedelta, ABCTick
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| 0
| 2020-05-07T20:17:41Z
| 2020-05-07T22:46:42Z
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|
MEMBER
| null |
Motivation: to make more progress on getting DateOffset into cython, we need to be able to import Timestamp into liboffsets. ATM that would induce circular dependencies.
This allows us to merge c_timestamp back into timestamps.pyx; the initial motivation for that was to be able to cimport _Timestamp into libconversion.
In a follow-up we can use this pattern to make a faster `is_interval` check (that doesnt have to go through python-space)
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Standardize datetimelike casting behavior where/setitem/searchsorted/comparison
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| 0
| 2020-05-07T20:36:58Z
| 2020-05-08T17:16:06Z
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MEMBER
| null |
- [ ] closes #xxxx
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- [ ] whatsnew entry
Addresses #30699 for eq/ne, but not for inequalities.
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BUG: Make nullable booleans numeric
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| 2020-05-11T18:59:57Z
| 2020-05-11T18:53:16Z
|
MEMBER
| null |
- [x] closes #34051
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Also a decent speedup since this is now going through Cython space:
```python
df = pd.DataFrame({"a": [1] * 50_000 + [2] * 50_000, "b": pd.array([True] * 100_000)})
# master
%timeit df.groupby("a").sum()
# 5.15 ms ± 18 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# branch
%timeit df.groupby("a").sum()
# 1.94 ms ± 10.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
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DOC: Missing examples of from_records.
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| 1
| 2020-05-07T21:25:53Z
| 2020-05-12T11:08:25Z
| 2020-05-12T11:08:25Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
`pandas.DataFrame.from_records`: https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.from_records.html
#### Documentation problem
Examples are provided for constructors like `DataFrame.from_dict` but no examples are present for `DataFrame.from_records`, which makes it slightly harder to tell whether it's appropriate for a desired use case.
#### Suggested fix for documentation
Add examples.
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DOC: Examples for DataFrame.from_records
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| 5
| 2020-05-07T21:49:59Z
| 2020-05-12T16:40:38Z
| 2020-05-12T11:08:25Z
|
CONTRIBUTOR
| null |
This PR adds examples to the documentation for `pandas.DataFrame.from_records`. It also rewords a couple sentences to clarify that the input data can be a sequence of dicts or sequence of tuples (but not a sequence of DataFrames).
- [x] closes #34057
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 11
| 2020-05-07T21:53:34Z
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|
CONTRIBUTOR
| null |
- [x] closes #34026
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 614,401,513
|
MDU6SXNzdWU2MTQ0MDE1MTM=
| 34,060
|
BUG: df.to_excel returns an error
|
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| 2020-05-07T23:17:07Z
| 2020-05-13T06:53:54Z
| 2020-05-11T18:41:18Z
|
NONE
| null |
Hello,
I run the sample script in pandas [to_excel documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_excel.html). However, it throws an error.
#### Code Sample, a copy-pastable example
```python
# Your code here
df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
index=['row 1', 'row 2'],
columns=['col 1', 'col 2'])
df1.to_excel("output.xlsx")
```
#### Problem description
Here is the screenshot of the issue:
Openpyxl installed while installing pandas through pip.
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| 34,061
|
BUG: using a list in "selector" for set_table_styles produces wrong rules
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| 2
| 2020-05-08T02:38:02Z
| 2021-02-27T19:44:58Z
| 2021-02-27T19:44:58Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
Example: I want to apply a style like this:
`df.style.set_table_styles([{'selector': 'th.col_heading,th.blank', 'props': [('display', 'none')]}])`
Note a list (`th.col_heading,th.blank`) as selector. This produces wrong CSS, because UUID of the table is only prepended once to this selector:
#T_table_UUID th.col_heading, th.blank {
display: none;
}
i.e. __all__ `th.blank` elements in all tables on the page are hidden. This is not exactly intuitive, although the workaround is simple: you need to write
`set_table_styles([{'selector': 'th.col_heading', 'props': [('display', 'none')]}, {'selector': 'th.blank', 'props': [('display', 'none')]}])`
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.10-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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REF: move apply_wraps up into liboffstes
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| 2
| 2020-05-08T04:10:18Z
| 2020-05-10T15:36:24Z
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MEMBER
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DOC: Dataframe.skew invalid function signature for "skipna"
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| 10
| 2020-05-08T08:57:17Z
| 2021-10-16T17:28:54Z
| 2021-10-16T17:28:54Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.skew.html
#### Documentation problem
In the function signature it says `skipna=None` even though the parameter documentation underneath says `skipna bool, default True`.
#### Suggested fix for documentation
The `skipna=None` in function signature should be changed to `skipna=True`.
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MDU6SXNzdWU2MTQ2NTIwNDA=
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BUG: Pandas named aggregation not working with resample agg
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[] | null | 1
| 2020-05-08T10:21:05Z
| 2020-05-18T11:08:36Z
| 2020-05-18T11:08:35Z
|
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 of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
{"group": ["a"], "col": [1.0]}, index=[pd.to_datetime("2019-11-04 10:32:09.737")]
)
df.groupby("group").resample("1D").agg(open=pd.NamedAgg("col", "first"))
```
#### Problem description
This returns
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-34479fc13c88> in <module>
----> 1 df.groupby('group').resample('1D').agg(open=pd.NamedAgg("col", "first"))
TypeError: aggregate() missing 1 required positional argument: 'func'
```
#### Expected Output
I think this would be
```python
group open
group
a 2019-11-04 a 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3ed7dff48bb4e8c7c0129283ff51eccea3a0f861
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-51-generic
Version : #44~18.04.2-Ubuntu SMP Thu Apr 23 14:27:18 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0.dev0+1502.g3ed7dff48
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 3.0.0
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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DOC: use the sphinx-panels extension for cards instead of raw html
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This is an alternative for https://github.com/pandas-dev/pandas/pull/32065, using the https://sphinx-panels.readthedocs.io/en/latest/ extension (which was created inspired on how we are using this in our docs, but then nicely packaged in a sphinx extension to avoid the use of raw html).
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BUG: ValueError in read_csv when dtype='string' and parse_dates is present
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| 4
| 2020-05-08T11:22:41Z
| 2021-03-24T18:02:35Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import datetime
df = pd.DataFrame({'a': [1], 'b': [datetime.datetime.now()]})
df.to_csv('test.csv', index=None)
pd.read_csv('test.csv', dtype='string', parse_dates=['b'])
```
#### Problem description
`ValueError: not all elements from date_cols are numpy arrays`
#### Expected Output
Data frame with all columns as strings except ones specified in parse_dates optional argument.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-22-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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|
BUG : Series.to_timestamp and Series.to_period raise user-facing AssertionError
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| 8
| 2020-05-08T12:52:34Z
| 2020-05-20T14:05:42Z
| 2020-05-20T14:05:37Z
|
CONTRIBUTOR
| null |
- [X] closes #33327
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
|
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MDU6SXNzdWU2MTQ3MzU5MDg=
| 34,068
|
BUG: str.contains() doesn't apply AND and OR properly
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| 1
| 2020-05-08T13:14:52Z
| 2020-05-08T13:42:48Z
| 2020-05-08T13:42:37Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
test_df = pd.DataFrame({'col1' : ['ab','bc']})
print(test_df['col1'].str.contains('a' or 'b'))
print(test_df['col1'].str.contains('a' and 'b'))
```
#### Problem description
Using logical 'and' in str.contains without regex applies a logical 'or'.
Using logical 'or' in str.contains without regex applies a logical 'and'.
#### Expected Output
The code above returns :
0 True
1 False
Name: col1, dtype: bool
0 True
1 True
Name: col1, dtype: bool
But it should be :
0 True
1 True
Name: col1, dtype: bool
0 True
1 False
Name: col1, dtype: bool
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 10.0.1
setuptools : 39.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : 1.3.0
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDE1MjYwODcz
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|
ENH: Implement groupby.sample
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| 1
| 2020-05-08T14:23:50Z
| 2020-06-14T19:05:08Z
| 2020-06-14T15:22:16Z
|
MEMBER
| null |
- [x] closes #31775
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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| 614,790,624
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MDExOlB1bGxSZXF1ZXN0NDE1MjczMjUw
| 34,070
|
DOC: clarify boxplot whiskers extension
|
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| 6
| 2020-05-08T14:49:49Z
| 2020-05-21T18:17:42Z
| 2020-05-21T18:17:36Z
|
CONTRIBUTOR
| null |
As I'm getting started with box plots, I found confusing that the `DataFrame.boxplot` docstring didn't mention that whiskers extend _at most_ to 1.5 * IQR away from the box's edges.
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry -> is it required for such a minor change ?
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MDExOlB1bGxSZXF1ZXN0NDE1MjgyMzU0
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|
add test for setitem from duplicate axis
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| 2020-05-11T07:38:44Z
| 2020-05-11T00:33:39Z
|
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| null |
- [x] closes #34034
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: update imports, avoid hasattr checks
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TST/CLN: Break out groupby function tests
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| 1
| 2020-05-08T16:53:10Z
| 2020-05-09T21:51:36Z
| 2020-05-09T19:37:03Z
|
MEMBER
| null |
tests/groupby/test_function.py is still huge (1000+ lines) but this breaks out a few pieces (all copy / paste with no logic changes)
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BUG: Multijoining indexes always returns indexers, ignores function parameters
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| 2020-05-08T17:06:16Z
| 2020-05-11T18:57:16Z
| 2020-05-11T18:57:16Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
>>>frame = pd.DataFrame([(1,2,3,4),(4,5,6,7)],columns=["i1", "i2", "i3","val"]).set_index(["i1","i2","i3"])
>>>frame2 = pd.DataFrame([(1,2,3),(4,5,6)],columns=["i1", "i2", "val"]).set_index(["i1","i2"])
>>>frame.index.join(frame2.index,return_indexers=False)
(MultiIndex([(1, 2, 3),
(4, 5, 6)],
names=['i1', 'i2', 'i3']), None, None)
```
#### Problem description
Parameter return_indexers is ignored in some cases of __join_multi
#### Expected Output
```python
>>>frame.index.join(frame2.index,return_indexers=False)
MultiIndex([(1, 2, 3),
(4, 5, 6)],
names=['i1', 'i2', 'i3'])
```
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MDExOlB1bGxSZXF1ZXN0NDE1MzQyMjEw
| 34,075
|
BUG: Fixed __join_multi always returning indexers (#34074)
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| 2
| 2020-05-08T17:25:19Z
| 2020-05-11T18:57:21Z
| 2020-05-11T18:57:17Z
|
CONTRIBUTOR
| null |
- [x] closes #34074
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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| 614,909,367
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MDExOlB1bGxSZXF1ZXN0NDE1MzY5NDU0
| 34,076
|
REF: share code for scalar validation in datetimelike array methods
|
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| 1
| 2020-05-08T18:32:29Z
| 2020-05-12T16:16:38Z
| 2020-05-12T15:31:46Z
|
MEMBER
| null |
The user-facing effect here is in the exception messages we show to users, where we may want to be more verbose than this. Thoughts @gfyoung?
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BUG: DatetimeIndex.slice_locs() doesn't hande date objects not in index
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| 1
| 2020-05-08T18:51:54Z
| 2020-09-02T21:36:38Z
| 2020-09-02T21:36:38Z
|
CONTRIBUTOR
| null |
I would expect [7] below to return the same result as [5] (just as [6] returns the same result as [4]).
```
In [1]: import pandas as pd, datetime as dt
In [2]: pd.__version__
Out[2]: '1.0.3'
In [3]: a = pd.DatetimeIndex(['2010-01-01', '2010-01-03'])
In [4]: a.slice_locs(dt.datetime(2010, 1, 1), dt.datetime(2010, 1, 3))
Out[4]: (0, 2)
In [5]: a.slice_locs(dt.datetime(2010, 1, 1), dt.datetime(2010, 1, 2))
Out[5]: (0, 1)
In [6]: a.slice_locs(dt.date(2010, 1, 1), dt.date(2010, 1, 3))
Out[6]: (0, 2)
In [7]: a.slice_locs(dt.date(2010, 1, 1), dt.date(2010, 1, 2))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2645 try:
-> 2646 return self._engine.get_loc(key)
2647 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine._date_check_type()
KeyError: datetime.date(2010, 1, 2)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
714 try:
--> 715 return Index.get_loc(self, key, method, tolerance)
716 except (KeyError, ValueError, TypeError):
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2647 except KeyError:
-> 2648 return self._engine.get_loc(self._maybe_cast_indexer(key))
2649 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine._date_check_type()
KeyError: datetime.date(2010, 1, 2)
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1262390400000000000
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2645 try:
-> 2646 return self._engine.get_loc(key)
2647 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
KeyError: Timestamp('2010-01-02 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1262390400000000000
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
727 stamp = stamp.tz_localize(self.tz)
--> 728 return Index.get_loc(self, stamp, method, tolerance)
729 except KeyError:
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2647 except KeyError:
-> 2648 return self._engine.get_loc(self._maybe_cast_indexer(key))
2649 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
KeyError: Timestamp('2010-01-02 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_slice_bound(self, label, side, kind)
4840 try:
-> 4841 slc = self.get_loc(label)
4842 except KeyError as err:
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in get_loc(self, key, method, tolerance)
729 except KeyError:
--> 730 raise KeyError(key)
731 except ValueError as e:
KeyError: datetime.date(2010, 1, 2)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-7-f3a1babc1d65> in <module>
----> 1 a.slice_locs(dt.date(2010, 1, 1), dt.date(2010, 1, 2))
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in slice_locs(self, start, end, step, kind)
4929 end_slice = None
4930 if end is not None:
-> 4931 end_slice = self.get_slice_bound(end, "right", kind)
4932 if end_slice is None:
4933 end_slice = len(self)
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_slice_bound(self, label, side, kind)
4842 except KeyError as err:
4843 try:
-> 4844 return self._searchsorted_monotonic(label, side)
4845 except ValueError:
4846 # raise the original KeyError
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/base.py in _searchsorted_monotonic(self, label, side)
4793 def _searchsorted_monotonic(self, label, side="left"):
4794 if self.is_monotonic_increasing:
-> 4795 return self.searchsorted(label, side=side)
4796 elif self.is_monotonic_decreasing:
4797 # np.searchsorted expects ascending sort order, have to reverse
~/.conda/envs/build/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py in searchsorted(self, value, side, sorter)
858 elif not isinstance(value, DatetimeArray):
859 raise TypeError(
--> 860 "searchsorted requires compatible dtype or scalar, "
861 f"not {type(value).__name__}"
862 )
TypeError: searchsorted requires compatible dtype or scalar, not date
```
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MDU6SXNzdWU2MTQ5MTkwMTA=
| 34,078
|
BUG: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
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[] | null | 1
| 2020-05-08T18:52:17Z
| 2020-08-21T17:05:37Z
| 2020-05-08T20:31:55Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import gensim
from gensim.models import FastText
from sklearn.feature_extraction.text import TfidfVectorizer
from difflib import SequenceMatcher
#Loading Query Text for Corpus Building
qt=pd.read_csv('C:/Demo/Query_Text.csv')
qt.shape
#Loading QueryText for comparing
convid =pd.read_csv('C:/Demo/ConvId_May4th.csv')
convid.shape
sentences = qt
convid = convid.sort_values(['User_PUID','EventInfo_Time'], ascending=[True,True])
convid = convid.reset_index()
convid['FastTextResult'] =float()
convid['Tfidf'] = float()
convid['TfidfWc'] = float()
model_ted = FastText(qt, window=1, min_count=1, sg=0)
for i in range(len(convid['Query_Text'])):
print('i',i)
if(i == len(convid)-1):
break
#print("The FastText Output")
ft=model_ted.wv.similarity(str(convid['Query_Text'][i]).lower(),str(convid['Query_Text'][i+1]).lower())
ft=round(ft,3)
#print(ft)
convid['FastTextResult'][i]=ft;
#print("vector output ")
vectorizer = TfidfVectorizer(decode_error='ignore',strip_accents='unicode',stop_words='english',min_df=1,analyzer='word',vocabulary=qt)
tfidf= vectorizer.fit_transform([str(convid['Query_Text'][i]).lower(),str(convid['Query_Text'][i+1]).lower()])
product=(tfidf *tfidf.T).A
pro = product[0,1]
pro = round(pro,3)
#print(pro)
convid['Tfidf'][i]=pro;
#print("widlchar")
wildchar_value = SequenceMatcher(str(convid['Query_Text'][i]).lower(),str(convid['Query_Text'][i+1]).lower()).ratio()
wildchar_value = round(wildchar_value,3)
convid['TfidfWc'][i]=wildchar_value;
#print(wildchar_value)
convid.to_csv('C:/Demo/ConvIdOutput_May4th.csv')
```
#### Problem description
when i am trying to execute the above python script , getting the following error:
C:\Anaconda3\lib\site-packages\ipykernel_launcher.py:11: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
#### Expected Output
The above code should
#### Output of ``pd.show_versions()``
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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CLN: de-duplicate ops code
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MDExOlB1bGxSZXF1ZXN0NDE1NDM4MzU3
| 34,080
|
Simplify Reducer.get_result
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| 2020-05-08T21:19:13Z
| 2020-06-05T23:05:32Z
| 2020-06-05T23:05:32Z
|
MEMBER
| null |
by using nditer this makes some progress towards #34014
Benchmarks for frame_apply looks as follows:
```sh
before after ratio
[3ed7dff4] [5e77e1ed]
<master> <reduce-simplify>
! 9.39±0.3ms failed n/a frame_methods.Apply.time_apply_ref_by_name
- 8.52±0.2ms 2.87±0.2ms 0.34 frame_methods.Apply.time_apply_lambda_mean
- 9.32±0.2ms 2.96±0.3ms 0.32 frame_methods.Apply.time_apply_np_mean
```
I'm not quite clear yet why the one benchmark is failing. Running it over a thousands loops in the REPL seems fine, but maybe CI will help detect something
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|
REF: do length-checks in boilerplate decorator
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NDE1NDQ3NTM5
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|
CLN: Delete no-op check
|
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| 1
| 2020-05-08T21:46:34Z
| 2020-05-10T17:48:50Z
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MEMBER
| null |
Maybe the note is somehow useful, otherwise I think this can be cleaned?
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CLN: .values -> ._values
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CLN: avoid _typ checks in _libs
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Sits on top of #34072
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BUG: read_csv skips leading space where it shouldn't
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| 2021-08-07T05:57:46Z
| null |
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
I wasn't able to isolate a (more) minimal example, so I'll just share what I was working on. `nltk` here is version `3.4.5`.
```python
import csv
import string
import nltk
import pandas as pd
UNFORMATTED = set(string.ascii_lowercase)
PUNCTUATION = set(" !\"&'(),-.:;?[]_`")
ALLOWED = UNFORMATTED | set(string.ascii_uppercase) | PUNCTUATION
EMPTY = '<NONE>'
CAPITALIZE = '<CAP>'
def generate_sequences(text: str, k: int):
"""
Yields tuples of subsequence of k characters, next character
(if within a special set)
"""
for i in range(len(text) - k):
seq = text[i:i + k]
next_char = text[i + k]
punct_char = (next_char if next_char in PUNCTUATION else
CAPITALIZE if next_char.isupper() else EMPTY)
yield seq, punct_char
gutenberg = nltk.corpus.gutenberg
gutenberg.ensure_loaded()
sample_file = gutenberg.fileids()[0]
sample = ' '.join(gutenberg.raw(sample_file).split())
with open('seq.txt', 'w') as file:
file.writelines(f"{seq}|{punct}\n" for seq, punct in generate_sequences(sample, k=10))
df = pd.read_csv('seq.txt', sep='|', quoting=csv.QUOTE_NONE,
names=['sequence', 'next_char'], skipinitialspace=False,
dtype=str, na_filter=False)
seq_length = len(df.at[0, 'sequence'])
lengths = df['sequence'].apply(len)
assert (lengths == seq_length).all()
```
#### Problem description
`AssertionError` is raised because there is one element in the `sequence` column that isn't of length 10, even though the text file was manually crafted to contain sequences of exactly 10 characters, followed by the separator `|`, followed by another value.
Upon inspection:
```python
>>> df.assign(length=lengths)[lengths != seq_length]
sequence next_char length
763047 it could <NONE> 9
```
but
```python
>>> with open('seq.txt') as file:
... lines = file.readlines()
...
>>> lines[763047]
' it could |<NONE>\n'
>>> len(lines[763047].split('|')[0])
10
```
This is unexpected behaviour because `skipinitialspace=False`, `quoting=csv.QUOTE_NONE`, `dtype=str` and `na_filter=False` were all passed to `pd.read_csv`, meaning that the values should be interpreted as raw as they come (i.e. including any leading space).
Moreover, this behaviour is inconsistent since there are plenty other examples in `seq.txt` of values with leading spaces that *do* get parsed correctly.
What's even weirder (and probably near to the crux of the problem) is that setting `EMPTY` to `'<EMPTY>'` or something else instead of `'<NONE>'` in the script above makes the problem disappear. Furthermore, any value of `EMPTY` with *exactly* four characters enclosed in angle brackets starting with `NA` produces the error. That is, `'<NAAA>'`, `'<NAZZ>'`, *do* produce the error, but `'<NA>'`, `'<NAAA'`, `'<NAA>'` do *not*.
Probably this has to do with NA parsing? Though I thought passing `na_filter=False` should have fixed that.
#### Expected Output
All elements of `df['sequence']` are strings of the same length (10 in this case), so no `AssertionError`.
#### Output of ``pd.show_versions()``
For installed environment:
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
For test on `master`:
<details>
```text
INSTALLED VERSIONS
------------------
commit : 998a0deea39f11fa06071af77cc1afba65900330
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3
Cython : 0.29.17
pytest : 5.4.2
hypothesis : 5.11.0
sphinx : 3.0.3
blosc : 1.9.1
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.1
```
</details>
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| 5
| 2020-05-09T00:42:04Z
| 2020-05-26T09:31:52Z
| 2020-05-09T12:43:08Z
|
CONTRIBUTOR
| null |
Botocore release today changed some Errors
https://pypi.org/project/botocore/#history
```
______________________ TestS3.test_write_s3_parquet_fails ______________________
[gw1] linux -- Python 3.6.10 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.io.parser.test_network.TestS3 object at 0x7f5e201c9080>
tips_df = total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 ....75 Male No Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2
[244 rows x 7 columns]
@td.skip_if_no("pyarrow")
def test_write_s3_parquet_fails(self, tips_df):
# GH 27679
with pytest.raises(
FileNotFoundError, match="The specified bucket does not exist"
):
> tips_df.to_parquet("s3://an_s3_bucket_data_doesnt_exit/not_real.parquet")
```
PR to follow shortly.
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CI: Fix botocore Error
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| 4
| 2020-05-09T03:06:07Z
| 2020-05-26T09:31:59Z
| 2020-05-09T12:43:08Z
|
CONTRIBUTOR
| null |
- [x] closes #34086
AWS Service Exceptions are not statically defined in boto3. Easiest to just catch a ClientError here.
Ref Docs:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/error-handling.html#parsing-error-responses-and-catching-exceptions-from-aws-services
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ENH: make Tick comparisons match Timedelta behavior
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QST: Distinguishing row and column slices in DataFrame subclass
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| 2020-12-07T08:15:27Z
| 2020-09-01T22:43:44Z
|
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/questions/61246252/distinguish-column-and-row-slice-in-pandas-subclass).
---
Hi pandas devs,
What is the best way to distinguish whether a row or column was sliced in a DataFrame subclass? I am designing a subclass where row slices should return my custom Series/DF subclass, but columns are just vanilla pd.Series. In addition, I want non-slice calls to `_constructor` (e.g., `apply`) to simply return vanilla DataFrames. Is there a straightforward way to do this?
Thanks for a great package!
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| 1
| 2020-05-09T10:53:05Z
| 2020-05-09T19:50:51Z
| 2020-05-09T19:50:47Z
|
CONTRIBUTOR
| null |
We want to add regression test for #27220 and make sure that this kind of issues will not be present in the future.
Thanks to @yanglinlee [code](https://github.com/pandas-dev/pandas/pull/27667) I created test that works with current `master` branch.
- [x] closes #27220
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NDE1NTYwODM0
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DOC: use new numpy doc url
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MEMBER
| null |
- [ ] xref #33953
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NDE1NTYxNDg5
| 34,092
|
CLN, TYP: Factor out part of info
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| 2
| 2020-05-09T11:30:37Z
| 2020-05-17T09:17:58Z
| 2020-05-11T21:44:28Z
|
MEMBER
| null |
Precursor to #31796, which still has a rather large diff. This should help make it easier to read.
On master, `cols` holds the DataFrame's columns. If we were to use this method on a Series, then it would hold an Index containing the Series' name (`Index([data.name])`). Is there a good name for a variable that could store either? At the moment I've called it `ids`
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| 615,212,159
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MDU6SXNzdWU2MTUyMTIxNTk=
| 34,093
|
DISC: Deprecate use_inf_as_na
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| 2020-05-09T16:28:58Z
| 2020-09-26T15:36:45Z
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|
MEMBER
| null |
I think a case could be made for deprecating pd.options.mode.use_inf_as_na. Few thoughts:
* Doesn't seem necessary or particularly useful: it's easy to get the same behavior by checking for NA _or_ infinity.
* Source of bugs: e.g., https://github.com/pandas-dev/pandas/issues/33655, https://github.com/pandas-dev/pandas/issues/33594. If an array has a cached mask attribute there's no guarantee that it's correct:
```python
import numpy as np
import pandas as pd
arr = pd.Categorical([1.0, 2.0, np.inf])
pd.options.mode.use_inf_as_na = True
arr.isna().any() # False
```
* General maintenance cost: several null-checking functions either have to be duplicated or equipped with boolean arguments to switch their behavior (e.g., _isna and _isna_ndarraylike from /core/dtypes/missing.py, or isnaobj and isnaobj_old from /_libs/missing.pyx). These could all be cleaned up if NA had _one_ meaning.
Thoughts?
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CLN: Change old string formatting syntax into f-strings
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| 2020-05-09T19:20:42Z
|
CONTRIBUTOR
| null |
- [x] xref #29547
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU2MTUyNTM5NzA=
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ENH: enable concat for nullable Int with other dtypes
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| 5
| 2020-05-09T20:27:49Z
| 2020-07-08T16:15:56Z
| 2020-07-08T16:15:56Z
|
MEMBER
| null |
Following-up on https://github.com/pandas-dev/pandas/pull/33607 which added the `ExtensionDtype._get_common_dtype` method to the EA interface to determine concat/append behaviour, we can now use this to refine the concat behaviour for nullable integer and boolean dtypes.
See comment here: https://github.com/pandas-dev/pandas/pull/33607#discussion_r410127707
For now, the PR only enabled concat of all IntegerDtype objects (any other combination will result in object dtype). But, we can expand this to the combination of IntegerDtype with numpy int dtype, Int with boolean, Int with float, etc
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MDExOlB1bGxSZXF1ZXN0NDE1NjM5MTE2
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TST: Mark groupby.nunique test as slow
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| 3
| 2020-05-09T22:29:37Z
| 2020-05-10T15:05:30Z
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|
MEMBER
| null |
This test takes about 40 seconds to run for me while shrinking the size of the data brings the runtime down to 2 seconds, presumably without changing coverage (do we even need to parametrize over n and m here?).
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MDU6SXNzdWU2MTUyOTQ1OTA=
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BUG: Incorrect levels of MultiIndex when creating a new DataFrame by dropping a row
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| 5
| 2020-05-10T01:32:20Z
| 2020-06-14T22:41:59Z
| 2020-06-14T22:41:59Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
<img width="1143" alt="image" src="https://user-images.githubusercontent.com/33289453/81488715-0c2db980-929f-11ea-9960-61d24d1ab44f.png">
```python
#%%
import pandas as pd
import numpy as np
#%%
df = pd.DataFrame(np.arange(16).reshape(4,4), index=pd.MultiIndex.from_arrays([list("AABB"), list("XYXY")]), columns=list("abcd"))
df
#%%
df.index, df.index.levels
#%%
df_new = df.drop(index="A")
df_new
#%%
df_new.index, df_new.index.levels
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
The levels of MultiIndex of DataFrame never change when creating a new DataFrame by dropping a row. When creating a new DataFrame, the levels should match the new MultiIndex.
#### Expected Output
df_new.index.levels
should be
FrozenList([['B'], ['X', 'Y']]))
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : None
</details>
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BUG:pd.value_counts() When the values are equal, the order of equal values is invalid
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[] | null | 4
| 2020-05-10T03:41:53Z
| 2020-05-11T02:59:44Z
| 2020-05-11T02:59:44Z
|
NONE
| null |
```python
import pandas as pd
df = pd.DataFrame([('a', 1), ('b', 1), ('c', 3), ('a', 1), ('b', 1)])
print(pd.value_counts(df[0], sort=True))
# you can see two situations
# b 2
# a 2
# c 1
# Name: 0, dtype: int64
# a 2
# b 2
# c 1
# Name: 0, dtype: int64
```
#### Problem description
When the values are equal, the order of equal values is invalid, Sorting is only for values, no index is processed
#### Expected Output
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : zh_CN.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
None
</details>
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MDExOlB1bGxSZXF1ZXN0NDE1NzQ1OTUx
| 34,099
|
REF: implement general case DateOffset methods on BaseOffset
|
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| 1
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| 2020-05-12T16:12:23Z
| 2020-05-12T15:29:28Z
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MDExOlB1bGxSZXF1ZXN0NDE1NzQ4MDA2
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|
DOC: timezone warning for dates beyond TODAY
|
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| 6
| 2020-05-10T16:47:28Z
| 2020-08-21T17:16:40Z
| 2020-08-21T17:16:30Z
|
CONTRIBUTOR
| null |
introducing a suggestion discussed in PR #33863 :
Added a warning in the user guide that timezone conversion on future dates is inherently unreliable.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2MTU0NDM2OTI=
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|
BUG: Inconsistency in week anchoring?
|
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[] | null | 0
| 2020-05-10T18:01:42Z
| 2020-06-05T20:34:58Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
ts = pd.Timestamp('2020-05-13 15:30:00')
freqstr = 'W-MON'
period_start1 = pd.tseries.frequencies.to_offset(freqstr).rollback(ts.normalize())
freqstr = 'W-SUN'
period_start2 = pd.Period(ts, freq=freqstr).start_time
period_start1 == period_start2
```
#### Problem description
Result is `True` where it should be `False`
I would like to raise what seem to me 2 "troubles".
#####Trouble 1
I don't understand why it is not possible to use the same `freqstr` between `to_offset` and `Period` to get the same result (which would be according my understanding `W-SUN` here)
#####Trouble 2
To get `period_start1` anchored to beginning of the week, which starts at midnight, I have to `normalize()` it, either the `Timestamp` or the `Timestamp` provided by the `rollback`.
On the other hand `rollback` documentation states: 'Rolled timestamp if not on offset'
For me the offset is at period beginning, i.e. midnight on Sunday.
#### Expected Output
I would expect the following code to give equivalent result (no `normalize` and same `freqstr`).
```python
ts = pd.Timestamp('2020-05-13 15:30:00')
freqstr = 'W-SUN'
period_start1 = pd.tseries.frequencies.to_offset(freqstr).rollback()
freqstr = 'W-SUN'
period_start2 = pd.Period(ts, freq=freqstr).start_time
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-51-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.0.3
numpy : 1.16.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDU6SXNzdWU2MTU0NDM3MDA=
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CLN: use staticmethods where applicable.
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| 2021-03-21T15:43:54Z
| 2021-03-21T15:43:54Z
|
CONTRIBUTOR
| null |
# Issue
In my arbitrary sample of three `.py` files in the code base, Two files had multiple class methods in which the `cls` attribute was never used. It's entirely incomprehensible to me why these are specifically decorated as class methods since they don't access the class. It would improve code readability if they were static methods. Technically, most of them could also be module-level functions but refactoring that appears to be way more effort.
An example is `pandas/plotting/matplotlib/core.py`.
# Proposed fix
- To prevent anyone from making this mistake again, we should make sure our Linter rejects methods with unused arguments such as [PyLint does](http://pylint-messages.wikidot.com/messages:w0613).
- Write a script that converts all methods (including class methods) with an unused first argument (typically `self` and `cls`) to static methods. Run that script across the Pandas codebase.
- Fix any resulting test failures or other issues. Though very likely there will be none because a static method is virtually indistinguishable from a class method or instance method except if you check the type directly.
- Make the pull request.
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REF: put PeriodArray-specific method in PeriodArray
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API/BUG: pd.NA == pd.NaT returns False
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MEMBER
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@jorisvandenbossche I expected this to return pd.NA. Is this intentional?
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REF: make nat_strings cimport-able
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MEMBER
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Shouldn't make a real impact, mostly makes the imports easier to read
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REF: make ccalendar objects cimportable
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MEMBER
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xref #34105
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CLN: avoid ndim checks in _libs
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CLN: make sure we do no-copy ravel()
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ENH: to_dict(orient="index", into=T) returns T[K, dict], expecting T[K, T]
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| 2020-05-10T21:10:15Z
| 2021-06-10T12:51:18Z
| 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 of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Live at https://repl.it/@TomGoBravo/RoughUncommonDatabase
```python
import pandas as pd
import numpy as np
from collections import OrderedDict
df = pd.DataFrame(
np.random.randint(10, size=(3, 2)),
columns=list('AB'),
)
print(df.to_dict(orient='index', into=OrderedDict))
# Today this outputs something like:
# OrderedDict([(0, {'A': 7, 'B': 9}),
# (1, {'A': 8, 'B': 9}),
# (2, {'A': 9, 'B': 0})])
# but I'm expecting the values to be OrderedDict too instead
# of {}
```
#### Problem description
I expected the `into` parameter to be used for all dicts returned by to_dict but the implementation for orient="index" has `dict` hard-coded. A work around is fairly simple but thought I'd report the issue.
#### Expected Output
I'm expecting the values of the indexed mapping to also be of type `into`, not `dict`. The fix is replacing `dict` at https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L1503 with `into_c`. I tried changing this line of code and re-ran the `test_fast.sh`. It didn't seem to break any tests, I get `78277 passed, 8446 skipped, 1000 xfailed, 31 warnings in 967.05s (0:16:07)` vs `78277 passed, 8446 skipped, 1000 xfailed, 31 warnings in 1546.42s (0:25:46)` running master with no change.
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MDExOlB1bGxSZXF1ZXN0NDE1NzgzNjI1
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|
CLN: Don't use astype_nansafe after to_numpy
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| 2020-05-10T21:27:06Z
| 2020-06-01T13:22:42Z
| 2020-06-01T01:48:28Z
|
MEMBER
| null |
I'm not 100% sure about the change in the tests, but it seems to align with numpy:
```python
[ins] In [1]: np.array([1, 2]).astype(str)
Out[1]: array(['1', '2'], dtype='<U21')
[ins] In [2]: pd.array([1, 2]).astype(str)
Out[2]: array(['1', '2'], dtype='<U21')
```
```python
[ins] In [1]: np.array([True, False]).astype(str)
Out[1]: array(['True', 'False'], dtype='<U5')
[ins] In [2]: pd.array([True, False]).astype(str)
Out[2]: array(['True', 'False'], dtype='<U5')
```
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PERF: use is_foo_dtype fastpaths
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|
BUG: unstack() sorts automatically, default is preferably unsorted?
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[] | null | 2
| 2020-05-11T00:07:32Z
| 2020-07-19T18:27:16Z
| 2020-05-11T12:59:41Z
|
NONE
| null |
The unstack() function is automatically sorting my labels in my boxplot-code. Isn't it nice to have a sort = False option (probably the default?)
My labels are perfectly ordered, it's just after unstacking they get messed-up.
my post on stack-overflow: https://stackoverflow.com/questions/61720090/issues-with-boxplot-figure-x-label-are-alphabetically-and-extra-nan-label-is-cr
another example: https://stackoverflow.com/questions/32163063/pandas-plotting-incorrectly-sorts-the-binned-values-on-the-graph
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REGR: exceptions not caught in _call_map_locations
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| 2020-05-11T10:38:47Z
| 2020-05-15T09:33:18Z
| 2020-05-15T09:33:13Z
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MEMBER
| null |
- [ ] xref #32409
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
# NOTE: issue 'fixed' on master. This PR against 1.0.x branch
cc @jbrockmendel Could there still be issue on master with the exceptions not being caught?
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MacPython linux build failing
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[] | null | 21
| 2020-05-11T14:14:48Z
| 2020-05-29T19:05:40Z
| 2020-05-29T19:05:40Z
|
CONTRIBUTOR
| null |
There are build failures with the manylinux1 32-bit docker image.
```
gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -DNPY_NO_DEPRECATED_API=0 -Ipandas/_libs/tslibs -I./pandas/_libs/tslibs -I/opt/python/cp38-cp38/lib/python3.8/site-packages/numpy/core/include -I/opt/python/cp38-cp38/include/python3.8 -c pandas/_libs/tslibs/period.c -o build/temp.linux-x86_64-3.8/pandas/_libs/tslibs/period.o
pandas/_libs/tslibs/period.c: In function ‘__pyx_f_6pandas_5_libs_6tslibs_6period_pqyear’:
pandas/_libs/tslibs/period.c:12464:3: warning: ‘__pyx_v_year’ may be used uninitialized in this function [-Wmaybe-uninitialized]
return __pyx_r;
^
pandas/_libs/tslibs/period.c: In function ‘__pyx_f_6pandas_5_libs_6tslibs_6period_pquarter’:
pandas/_libs/tslibs/period.c:12512:3: warning: ‘__pyx_v_quarter’ may be used uninitialized in this function [-Wmaybe-uninitialized]
return __pyx_r;
building 'pandas._libs.hashtable' extension
gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wl,-strip-all -I/usr/local/include -fPIC -DNPY_NO_DEPRECATED_API=0 -Ipandas/_libs/src/klib -I/opt/python/cp38-cp38/lib/python3.8/site-packages/numpy/core/include -I/opt/python/cp38-cp38/include/python3.8 -c pandas/_libs/hashtable.c -o build/temp.linux-i686-3.8/pandas/_libs/hashtable.o -Werror
pandas/_libs/hashtable.c: In function ‘__pyx_f_6pandas_5_libs_9hashtable_value_count_float64’:
pandas/_libs/hashtable.c:27378:41: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
pandas/_libs/hashtable.c:29050:41: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_9; __pyx_t_10+=1) {
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_8duplicated_uint64’:
pandas/_libs/hashtable.c:29800:38: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
__pyx_t_10 = ((__pyx_v_k != __pyx_v_table->n_buckets) != 0);
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_10ismember_uint64’:
pandas/_libs/hashtable.c:30271:177: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
*__Pyx_BufPtrStrided1d(__pyx_t_5numpy_uint8_t *, __pyx_pybuffernd_result.rcbuffer->pybuffer.buf, __pyx_t_17, __pyx_pybuffernd_result.diminfo[0].strides) = (__pyx_v_k != __pyx_v_table->n_buckets);
^
pandas/_libs/hashtable.c: In function ‘__pyx_f_6pandas_5_libs_9hashtable_value_count_object’:
pandas/_libs/hashtable.c:30731:33: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) {
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_14duplicated_object’:
pandas/_libs/hashtable.c:31389:31: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
__pyx_t_9 = ((__pyx_v_k != __pyx_v_table->n_buckets) != 0);
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_16ismember_object’:
pandas/_libs/hashtable.c:31825:171: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
*__Pyx_BufPtrStrided1d(__pyx_t_5numpy_uint8_t *, __pyx_pybuffernd_result.rcbuffer->pybuffer.buf, __pyx_t_16, __pyx_pybuffernd_result.diminfo[0].strides) = (__pyx_v_k != __pyx_v_table->n_buckets);
^
pandas/_libs/hashtable.c: In function ‘__pyx_f_6pandas_5_libs_9hashtable_value_count_int64’:
pandas/_libs/hashtable.c:32258:39: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) {
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_20duplicated_int64’:
pandas/_libs/hashtable.c:33007:38: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
__pyx_t_10 = ((__pyx_v_k != __pyx_v_table->n_buckets) != 0);
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_22ismember_int64’:
pandas/_libs/hashtable.c:33478:177: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
*__Pyx_BufPtrStrided1d(__pyx_t_5numpy_uint8_t *, __pyx_pybuffernd_result.rcbuffer->pybuffer.buf, __pyx_t_17, __pyx_pybuffernd_result.diminfo[0].strides) = (__pyx_v_k != __pyx_v_table->n_buckets);
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_24mode_float64’:
pandas/_libs/hashtable.c:33800:41: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) {
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_26mode_int64’:
pandas/_libs/hashtable.c:34225:41: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) {
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_28mode_uint64’:
pandas/_libs/hashtable.c:34650:41: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) {
^
pandas/_libs/hashtable.c: In function ‘__pyx_pf_6pandas_5_libs_9hashtable_30mode_object’:
pandas/_libs/hashtable.c:35077:35: error: comparison between signed and unsigned integer expressions [-Werror=sign-compare]
for (__pyx_t_13 = 0; __pyx_t_13 < __pyx_t_12; __pyx_t_13+=1) {
^
cc1: all warnings being treated as errors
```
To reproduce
```Dockerfile
FROM quay.io/pypa/manylinux1_i686:latest
RUN git clone --depth=1 https://github.com/pandas-dev/pandas \
&& cd pandas \
&& /opt/python/cp38-cp38/bin/python -m pip install numpy cython python-dateutil pytz
WORKDIR pandas
RUN /opt/python/cp38-cp38/bin/python setup.py build_ext -i
```
I've uploaded the generated hashtable.c file [here][hashtable].
[hashtable]: https://gist.github.com/1f60b2059a103e6d36c02a0d07200e59
---
#34409 fixed the following issues.
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=35250&view=logs&j=e9afaa34-1a0f-534e-78fc-fae528ccd915&t=4685e914-c192-594f-c25f-e6cef98b7ba4
```
gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wl,-strip-all -I/usr/local/include -fPIC -DNPY_NO_DEPRECATED_API=0 -Ipandas/_libs/tslibs -I./pandas/_libs/tslibs -I/opt/python/cp38-cp38/lib/python3.8/site-packages/numpy/core/include -I/opt/python/cp38-cp38/include/python3.8 -c pandas/_libs/tslibs/period.c -o build/temp.linux-x86_64-3.8/pandas/_libs/tslibs/period.o -Werror
pandas/_libs/tslibs/period.c: In function ‘__pyx_f_6pandas_5_libs_6tslibs_6period_pqyear’:
pandas/_libs/tslibs/period.c:12479:3: error: ‘__pyx_v_year’ may be used uninitialized in this function [-Werror=maybe-uninitialized]
return __pyx_r;
^
pandas/_libs/tslibs/period.c: In function ‘__pyx_f_6pandas_5_libs_6tslibs_6period_pquarter’:
pandas/_libs/tslibs/period.c:12527:3: error: ‘__pyx_v_quarter’ may be used uninitialized in this function [-Werror=maybe-uninitialized]
return __pyx_r;
^
cc1: all warnings being treated as errors
warning: pandas/_libs/groupby.pyx:1101:26: Unreachable code
```
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=34768&view=results from May 3rd may be the first one with this warning. https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=34727&view=results from May 2nd built successfully.
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Backport PR #34048 on branch 1.0.x (Bug in DataFrame.replace casts columns to ``object`` dtype if items in ``to_replace`` not in values)
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| 0
| 2020-05-11T14:29:38Z
| 2020-05-14T12:26:47Z
| 2020-05-14T12:26:42Z
|
MEMBER
| null |
xref #34048, #32988
fixes regression in 1.0.0
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| 615,959,462
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MDExOlB1bGxSZXF1ZXN0NDE2MTY2MDE5
| 34,116
|
MAINT: Clarify supported Stata dta versions
|
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| 5
| 2020-05-11T15:07:53Z
| 2020-07-28T14:41:35Z
| 2020-05-12T14:54:10Z
|
CONTRIBUTOR
| null |
Test against old Stata versions and remove text indicating support
for versions which do not work reliably
closes #26667
- [x] closes #26667
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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