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MDU6SXNzdWU2MTU5NjQ3ODA=
| 34,117
|
BUG: my_DataFrame.groupby().min()/-max() gives Assertion error message
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| 2020-05-11T15:14:57Z
| 2020-08-21T17:05:37Z
| 2020-05-11T17:20:24Z
|
NONE
| null |
With pandas 1.0.0. (and newer) min/max methods for groups to not work as before:
```python
my_DataFrame.groupby().min()
my_DataFrame.groupby().max()
```
are not working anymore - it will give you an Assertion error message with no special location in the code.
You currently new to use aggregation to get this information:
```python
my_DataFrame.groupby().agg('min')
my_DataFrame.groupby().agg('max')
```
I could verify that
```python
my_DataFrame.groupby().mean()
```
still works in newest pandas, but somehow -min()/-max() no more.
Would it be possible to implement these methods back again to pandas?
I can confirm that my_DataFrame.groupby().max()/-min() worked properly in pandas 0.3.0.
Kind regards,
DK
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PERF: use fast-paths for dtype checks
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TST/REF: Deprivate check_pairwise_moment and remove ConsistencyBase
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MEMBER
| null |
the `check_pairwise_moment` function will be the last function moving out of big `ConsistencyBase` class, and after this, `ConsistencyBase` class only has the `_create_data` function which creates some mock data, so we could entirely remove `ConsistencyBase` and use `Base` instead.
cc @jreback
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BUG: read_csv returns inconsistent or misleading values with dtype=float
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| 2020-05-11T17:47:47Z
| 2021-08-10T22:00:23Z
| null |
NONE
| null |
The issue occurs for `read_csv()` when reading a mixture of empty or NaN and potential Boolean values when choosing `float` as dtype. NaN is either read as 1.0, or in the case of an empirical csv-file even inconsistently either as 1.0 or NaN. I could not replicate the inconsistency with synthetic-data, therefore the csv-file in question is provided here: https://github.com/c06n/Pandas_readcsv_issue/blob/master/empirical_data.csv
Neither issue occurs if `engine='python'`, therefore I believe it is a bug.
#### example with synthetic data:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'a': np.r_[[np.nan]*2000,
np.repeat('True', 100),
[np.nan]*2000,
np.repeat('True', 100),
[np.nan]*2000]})
df.to_csv('synthetic_data.csv', sep=';', decimal=',', index=False)
df = pd.read_csv('synthetic_data.csv',
na_values='',
sep=';',
decimal=',',
dtype={'a': 'float'})
df.a
```
#### example with empirical data:
Get the data from https://github.com/c06n/Pandas_readcsv_issue/blob/master/empirical_data.csv and then use the code below. The interesting columns are the right-most ones, e.g. the column `approach`.
```
df = pd.read_csv('empirical_data.csv',
na_values='',
sep=';',
decimal=',',
dtype={'approach': 'float'})
df.approach
```
#### Problem description
In the synthetic data, all values are converted to 1.0 (both NaN-values and the `True`-string object). In the empirical data, the first NaN-values are converted to 1.0, but at the end there are values that remain NaN.
* The problematic nature of the inconsistent treatment of NaN in case of the empirical data file should be self explanatory.
* Returning 1.0 for NaN-values seems inappropriate as well. Because the Python-engine returns NaN, this behavior does not appear to be intended.
Personally, I find the behavior in either case hugely problematic, because converting NaN to 1.0 is very non-intuitive in either case. Throwing an error, if possible, would be my preference.
#### Expected Output
Empty cells and values that are explicitely defined as NaN (e.g. "nan") should remain NaN.
#### Output of ``pd.show_versions()``
<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 142 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 : 0.29.17
pytest : None
hypothesis : 5.11.0
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
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 : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDE2MjUzNjk4
| 34,121
|
Fix typo in core/accessor.py
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CONTRIBUTOR
| null |
This is just a typo fix.
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API: remove dropped multilevel index from levels (GH: #34097)
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| 2020-05-11T18:36:34Z
| 2020-06-14T22:41:21Z
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|
MEMBER
| null |
- [x] closes #34097
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: Index with tuples comparison not working anymore
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| 6
| 2020-05-11T19:21:02Z
| 2020-05-17T21:16:51Z
| 2020-05-17T21:16:51Z
|
MEMBER
| null |
It seems trace back to #34081
with Index we could do something like:
```python
>>> pd.Index(['a', 'b', 'c']) == 'c'
array([False, False, True])
```
And it's also possible to do the same for Index with tuples:
```python
>>> pd.Index([('a', 'b'), ('b', 'c'), ('c', 'a')]) == ('c', 'a')
array([False, False, True])
```
But seems not working this way anymore after #34081 , is this length check here correct (so it is designed this way on purpose)? or we should consider it a bug?
cc @jbrockmendel
currently master:
```python
>>> pd.Index([('a', 'b'), ('b', 'c'), ('c', 'a')]) == ('c', 'a')
ValueError: ('Lengths must match', (3,), (2,), <class 'tuple'>)
```
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MDExOlB1bGxSZXF1ZXN0NDE2MzMwODc0
| 34,124
|
Fix #23055 Change pd.to_datetime and pd.to_timedelta to always convert null-like argument into pd.NaT
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| 2020-05-11T20:31:48Z
| 2020-07-10T19:43:17Z
| 2020-07-10T19:43:17Z
|
CONTRIBUTOR
| null |
I've tried to address the bug in `to_datetime` and `to_timedelta` functions. Please let me know if there're some other places worth to be changed.
- [x] closes #23055
- [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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MDU6SXNzdWU2MTYxOTcwNjQ=
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BUG: isin does some inconsistent type casting?
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| 2
| 2020-05-11T21:21:57Z
| 2021-01-09T18:53:52Z
| 2021-01-09T18:53:46Z
|
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. (exists on the version I checked out 2 weeks ago)
I observed the following behavior:
When I define my `DataFrame` with a numeric column like
```
import pandas as pd
df = pd.DataFrame({"a": [1, 2]})
```
and filter with `isin()` for `"1"` like
```
df["a"].isin(["1"])
```
I get
```
0 True
1 False
Name: a, dtype: bool
```
which results probably from implicit type casting?
But when my column has datatype object like
```
df = pd.DataFrame({"a": [1, "2"]})
```
and filter again with `isin()`
```
df["a"].isin(["1"])
```
I get the following:
```
0 False
1 False
Name: a, dtype: bool
```
That was the output I expected.
This seems a bit inconsistent. Is this the expected behavior? If not, I would look into this, if I have a bit of time in the coming weeks. This may be loosely related to #19356 .
#### Output of ``pd.show_versions()``
<details>
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-99-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
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 : 4.8.2
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 : 5.4.1
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 : 1.2.8
numba : 0.49.0
</details>
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DOC: Add clarifications and link to tutorials in the getting started page
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| 1
| 2020-05-11T21:53:56Z
| 2020-05-12T13:25:39Z
| 2020-05-12T13:25:36Z
|
MEMBER
| null |
Some people are having problems following our getting started instructions. The notes added here should help.
Also, adding missing link to the tutorials.
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| 1
| 2020-05-12T10:04:26Z
| 2020-10-20T19:52:21Z
| 2020-06-04T11:38:49Z
|
CONTRIBUTOR
| null |
- [X] closes #31544
- [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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| 34,129
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BUG: melt MultiIndex columns using index columns as identifier variables
|
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| 2020-05-12T11:40:27Z
| 2020-05-27T12:31:24Z
| 2020-05-26T23:17:17Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
[['p', 'q', 'r'],
['s', 't', 'u'],
['v', 'w', 'x'],
],
index=pd.MultiIndex.from_arrays(
[list('123'), list('456')],
names=['ind1', 'ind2']
),
columns=pd.MultiIndex.from_arrays(
[list('ABC'), list('DEF')])
)
# melt using level1 (or above in other cases) fails
df_l1 = df.reset_index(col_level=1)
print("\nL1 index insert:\n", df_l1)
# NOTE: THIS FAILS!
df_l1 = pd.melt(df_l1, col_level=1,
id_vars=['ind1'], value_vars=['D','E'])
print("\nL1 melt:\n", df_l1)
```
#### Problem description
Suppose that we have multi-index columns and we would like to `melt`, using the index as the `id_vars`:
In this example, if we `reset_index(col_level=1)` and then `melt()` will fail as shown below:
```python
L1 index insert:
A B C
ind1 ind2 D E F
0 1 4 p q r
1 2 5 s t u
2 3 6 v w x
FAILS: KeyError: 'ind1'
```
<details>
```python-traceback
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/Repos/spec17/venv/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2896 try:
-> 2897 return self._engine.get_loc(key)
2898 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'ind1'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
in
17 print("\nL1 index insert:\n", df_l1)
18 # NOTE: THIS FAILS!
---> 19 df_l1 = pd.melt(df_l1, col_level=1,
20 id_vars=['ind1'], value_vars=['D','E'])
21 print("\nL1 melt:\n", df_l1)
~/Repos/spec17/venv/lib/python3.8/site-packages/pandas/core/reshape/melt.py in melt(frame, id_vars, value_vars, var_name, value_name, col_level)
102 mdata = {}
103 for col in id_vars:
--> 104 id_data = frame.pop(col)
105 if is_extension_type(id_data):
106 id_data = concat([id_data] * K, ignore_index=True)
~/Repos/spec17/venv/lib/python3.8/site-packages/pandas/core/generic.py in pop(self, item)
860 3 monkey NaN
861 """
--> 862 result = self[item]
863 del self[item]
864 try:
~/Repos/spec17/venv/lib/python3.8/site-packages/pandas/core/frame.py in __getitem__(self, key)
2993 if self.columns.nlevels > 1:
2994 return self._getitem_multilevel(key)
-> 2995 indexer = self.columns.get_loc(key)
2996 if is_integer(indexer):
2997 indexer = [indexer]
~/Repos/spec17/venv/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2897 return self._engine.get_loc(key)
2898 except KeyError:
-> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key))
2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2901 if indexer.ndim > 1 or indexer.size > 1:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'ind1'
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.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 : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.14.0
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
</details>
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CI: test_object_factorize_dropna failing in MacPython/pandas-wheels
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| 2
| 2020-05-12T11:41:55Z
| 2020-05-12T14:52:21Z
| 2020-05-12T14:52:21Z
|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=35317&view=logs&j=79f3a53a-4a6d-509c-98b5-ff6e9111f67c&t=25a63239-9c30-5cf0-cb4c-3d01da7b47c5
```python
def test_object_factorize_dropna(
self, data, dropna, expected_codes, expected_uniques
):
codes, uniques = algos.factorize(data, dropna=dropna)
tm.assert_numpy_array_equal(uniques, expected_uniques)
> tm.assert_numpy_array_equal(codes, expected_codes)
E AssertionError: numpy array are different
E
E Attribute "dtype" are different
E [left]: int32
E [right]: int64
```
I'm not sure what the correct behavior is here, but the implementation is returning int32 while we're asserting int64. The test should probably check for `np.dtype("intp")`.
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CI/TST: slow tests
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| null |
CONTRIBUTOR
| null |
xref #30641
from the new arm64 job, but indicative of general slowness: https://travis-ci.org/github/pandas-dev/pandas/jobs/685955999
- let's make these much faster
- we can still mark some as `slow` if needed
- let's change the https://github.com/pandas-dev/pandas/blob/master/ci/run_tests.sh#L23, `durations` to 20 or 30
This issue can be closed by multiple PRs
```
========================== slowest 10 test durations ===========================
88.88s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[tzlocal()-30S-Y]
65.15s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[tzlocal()-30S-10M]
22.33s call pandas/tests/io/parser/test_multi_thread.py::test_multi_thread_path_multipart_read_csv[python]
21.54s call pandas/tests/io/parser/test_multi_thread.py::test_multi_thread_path_multipart_read_csv[c_high]
21.03s call pandas/tests/io/parser/test_multi_thread.py::test_multi_thread_path_multipart_read_csv[c_low]
19.46s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['dateutil/US/Pacific'-30S-Y]
15.75s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['dateutil/Asia/Singapore'-30S-10M]
15.71s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['dateutil/Asia/Singapore'-30S-Y]
15.61s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['Asia/Tokyo'-30S-Y]
15.59s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['US/Eastern'-30S-Y]
14.27s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['Asia/Tokyo'-30S-10M]
13.18s call pandas/tests/groupby/transform/test_transform.py::test_cython_transform_frame[shift-args3-<lambda>]
13.11s call pandas/tests/test_sorting.py::TestSorting::test_int64_overflow_moar
12.98s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['US/Eastern'-30S-10M]
12.92s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['dateutil/US/Pacific'-30S-10M]
12.78s call pandas/tests/io/sas/test_sas7bdat.py::TestSAS7BDAT::test_iterator_loop
12.76s call pandas/tests/io/sas/test_xport.py::TestXport::test1_basic
12.18s call pandas/tests/groupby/transform/test_transform.py::test_cython_transform_frame[shift-args2-<lambda>]
11.97s call pandas/tests/indexing/multiindex/test_chaining_and_caching.py::test_indexer_caching
11.95s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[pytz.FixedOffset(300)-30S-Y]
11.28s call pandas/tests/io/parser/test_multi_thread.py::test_multi_thread_string_io_read_csv[python]
11.19s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[datetime.timezone(datetime.timedelta(0, 3600))-30S-Y]
10.60s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[tzutc()-30S-Y]
10.45s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[<UTC>-30S-Y]
10.35s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[datetime.timezone.utc-30S-Y]
10.19s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[datetime.timezone(datetime.timedelta(-1, 82800), 'foo')-30S-Y]
10.12s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[tzutc()-30S-10M]
10.05s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit['UTC'-30S-Y]
9.99s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[pytz.FixedOffset(-300)-30S-Y]
9.58s call pandas/tests/resample/test_datetime_index.py::test_nearest_upsample_with_limit[<UTC>-30S-10M]```
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MDExOlB1bGxSZXF1ZXN0NDE2Njg3ODEz
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TST: DataFrame.interpolate axis name for axis argument
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| 1
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| 2020-12-12T11:37:22Z
| 2020-05-12T13:30:48Z
|
MEMBER
| null |
- [ ] closes #25190
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #31566
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BUG: Join of DataFrames with MultiIndex does not keep order of index
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| 2021-08-07T06:02:15Z
| 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 numpy as np
import pandas as pd
arrays = [np.array(['top', 'top', 'top', 'top', 'bottom', 'bottom', 'bottom', 'bottom']),
np.array(['bar', 'bar', 'baz', 'baz', 'bar', 'bar', 'baz', 'baz']),
np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
left = pd.DataFrame(np.random.randn(8, 4), index=arrays)
left.index.set_names(['pos', 'vars', 'numbers'], inplace=True)
right = pd.DataFrame({'vars': ['bar', 'bar', 'baz', 'baz'], 'numbers': ['one', 'two', 'one', 'two'], 'value': [1, 2, 3, 4]})
right = right.set_index(['vars', 'numbers'])
joined = left.join(right)
print(left.index.names)
print(joined.index.names)
```
output
```
['pos', 'vars', 'numbers']
['vars', 'numbers', 'pos']
```
#### Problem description
When two multindexed DataFrames are joined on a subset of the left index the resulting index becomes reordered.
#### Expected Output
I expect the order of the left dataframe to be kept after the left join.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-51-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 : 1.16.2
pytz : 2019.2
dateutil : 2.7.3
pip : 18.1
setuptools : 41.1.0
Cython : None
pytest : 3.7.4
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.7.4
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDE2NzA5MTc5
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TST:Make test faster
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CONTRIBUTOR
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- [x] Part of #34131
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CI: Fix expected for 32-bit platform
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| 2
| 2020-05-12T13:51:32Z
| 2020-05-12T15:27:04Z
| 2020-05-12T14:52:21Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/34130
|
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MDU6SXNzdWU2MTY2ODgzMjU=
| 34,136
|
Performance regression in replace.ReplaceList.time_replace_list_one_match
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| 0
| 2020-05-12T14:09:49Z
| 2020-06-14T14:34:35Z
| 2020-06-14T14:34:35Z
|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#replace.ReplaceList.time_replace_list_one_match?p-inplace=False&commits=6388370eb313244ba0e4cd8215dcdbfdabb7004b-88d5f1264bf58b518b7cce39230d018db59b3c72 points to https://github.com/pandas-dev/pandas/compare/6388370eb313244ba0e4cd8215dcdbfdabb7004b...88d5f1264bf58b518b7cce39230d018db59b3c72.
https://github.com/pandas-dev/pandas/pull/34048 mentions DataFrame.replace. cc @simonjayhawkins.
https://pandas.pydata.org/speed/pandas/index.html#replace.FillNa.time_replace?p-inplace=True&commits=6388370eb313244ba0e4cd8215dcdbfdabb7004b-88d5f1264bf58b518b7cce39230d018db59b3c72 is probably the same cause.
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| 616,777,930
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MDExOlB1bGxSZXF1ZXN0NDE2ODIzNTky
| 34,137
|
Revert: REF: do length-checks in boilerplate decorator
|
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| 10
| 2020-05-12T16:05:52Z
| 2020-05-19T08:08:40Z
| 2020-05-17T21:16:52Z
|
MEMBER
| null |
closes #34123
As discussed in #34123
let's revert this PR34081 first since it has potentially big impact on some cases in ops.
cc @jbrockmendel @jorisvandenbossche
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|
BUG: to_numeric doesn't work properly when errors='ignore'
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| 2020-05-12T19:47:41Z
| 2020-05-12T19:47:41Z
|
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
s = pd.Series(['a', '1.0'])
out = pd.to_numeric(s, errors='ignore')
print(list(out))
```
output:
```python
['a', '1.0']
```
#### Problem description
The output should cast '1.0' to a float and it doesn't.
This only happends when there is an error to "ignore", so a series containing
```python
['1.0', '1.0']
```
Will cast correctly to:
```python
[1.0, 1.0]
```
#### Expected Output
```python
['a', 1.0]
```
#### 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 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.3.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDE2ODU0NTg5
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REF: move mixins, repr_attrs to liboffsets
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MDExOlB1bGxSZXF1ZXN0NDE2ODg0NjA0
| 34,140
|
CLN: Splits Moments and MomentsConsistency tests into separate files
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| 2020-05-12T18:05:06Z
| 2020-05-13T20:43:49Z
| 2020-05-13T20:43:45Z
|
MEMBER
| null |
Due to previous cleaning as well as the large size of those files, we could now split the `Moments` and `MomentsConsistency` test classes into two different files for `rolling` and `ewm`. And since current `test_moments_expanding` is actually MomentsConsistencyExpanding class, so rename it to `test_moments_consistency_expanding` to keep consistency.
And I think this will be the second last PR to close the original test clean issue #30486 . The next PR will be fixturizing the `_create_data` so that we could remove the Base entirely (which currently only creates those data). But since this `base` is also used in other several files like `test_api.py`, `test_ewm.py` etc in window tests, I am afraid the next PR will be quite big.
Please let me your thoughts on the plan (or if you think the change is quite big then no need to proceed since the original issue is to only clean the `moments`)?
cc @jreback
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|
Incorrect multi-index specific level sorting after an align
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| 2020-05-12T18:19:39Z
| 2021-03-26T05:55:33Z
| 2021-03-26T05:55:33Z
|
NONE
| null |
Hello,
Level sorting does not seem to work on the output of an align between two DataFrames when
- you align on multi-index
- you align on left dataframe
- left dataframe columns are lexsorted
- right dataframe contains an extra-level compares to the left one
- you're trying to sort according to that extra-level
Here is a sample code:
```python
import pandas as pd
import numpy as np
col1 = pd.DataFrame(
[['a', 'g'],
['c', 'a'],
['b', 'w'],
], columns=['name', 'name2'])
col2 = pd.DataFrame(
[
['a', 'g', 1],
['c', 'a', 5],
['b', 'w', 100],
['d', 'h', 3]
], columns=['name', 'name2', 'id'])
T = 100
data1 = np.random.randn(col1.shape[0], T)
data2 = np.random.randn(col2.shape[0], T)
col1 = pd.MultiIndex.from_frame(col1)
col2 = pd.MultiIndex.from_frame(col2)
df1 = pd.DataFrame(data1.T, columns=col1)
df2 = pd.DataFrame(data2.T, columns=col2)
df1_sorted = df1.sort_index(1)
# sorting works
df_a = df1.align(df2, 'left')[0].sort_index(1, 'id')
print(df_a.head())
# sorting does not work
df_b = df1_sorted.align(df2, 'left')[0].sort_index(1, 'id')
print(df_b.head())
```
Thanks for your help
**Output of ```pd.show_versions()```**
<details>
```
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_GB.UTF-8
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 20.1
setuptools : 45.2.0
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.2
pyxlsb : None
s3fs : 0.4.2
scipy : 1.3.3
sqlalchemy : 1.3.10
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
```
</details>
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MDU6SXNzdWU2MTY5NDcyODA=
| 34,142
|
May 2020 Dev Meeting
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| 2020-05-12T20:43:56Z
| 2020-05-15T16:35:17Z
| 2020-05-15T16:35:17Z
|
CONTRIBUTOR
| null |
The monthly dev meeting is tomorrow, Wednesday 13th, at 18:00 UTC. Our calendar is at https://pandas.pydata.org/docs/development/meeting.html#calendar to check your local time.
Video Call: https://zoom.us/j/942410248
Minutes: https://docs.google.com/document/u/1/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-licoBmBU/edit?ouid=102771015311436394588&usp=docs_home&ths=true
Please add items you'd like to see discussed to the agenda.
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CLN: avoid hasattr check in Timestamp.__richcmp__
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| 2020-05-12T21:54:50Z
| 2020-05-13T15:40:13Z
| 2020-05-13T15:22:02Z
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MEMBER
| null |
The only behavior change should be comparison against zero-dim ndarray, which now lo longer un-packs so ends up returning an np.bool_ object instead of a bool object.
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REF: Implement NullFrequencyError in pd.errors
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REF: separate to_time, avoid runtime imports
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REF: move generate_range from offsets to the one place it is used
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INT: take DateOffset out of the inheritance tree for BaseOffset subclasses
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MEMBER
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Use a `__instancecheck__` override to keep isinstance checks working.
This will allow us to start moving other BaseOffset subclasses up into liboffsets without having to get DateOffset (which will be one of the trickiest) first.
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REF: move more of Tick into liboffsets._Tick
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MDU6SXNzdWU2MTcxNjAwNTI=
| 34,149
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CI: Moving ARM64 build from allowed failures
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| 2020-05-13T05:44:58Z
| 2020-09-17T23:12:18Z
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|
CONTRIBUTOR
| null |
@jreback ,
As suggested in #30641 , adding an issue on moving ARM64 build from allow_failures tag in Travis-CI.
|
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CI: Linting errors from flake >= 3.8.1
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| 8
| 2020-05-13T06:43:50Z
| 2020-10-21T15:54:26Z
| 2020-10-10T22:29:29Z
|
CONTRIBUTOR
| null |
The new version of flake bumps the version of pycodestyle, which results in several linting errors on CI. These include:
- (many instances) E741:ambiguous variable name 'l'
- (many instances) F541:f-string is missing placeholders
- (one or few instances) E721:do not compare types, use 'isinstance()'
Here the output:
```
2020-05-13T05:37:20.8820311Z ##[error]./pandas/_testing.py:630:22:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8824860Z ##[error]./pandas/_testing.py:1006:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8825837Z ##[error]./pandas/core/indexing.py:1569:42:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8826437Z ##[error]./pandas/core/series.py:2588:17:F541:f-string is missing placeholders
2020-05-13T05:37:20.8826992Z ##[error]./pandas/core/common.py:40:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8827546Z ##[error]./pandas/core/common.py:280:20:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8828505Z ##[error]./pandas/core/common.py:288:24:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8829122Z ##[error]./pandas/core/frame.py:685:37:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8830045Z ##[error]./pandas/core/indexes/multi.py:992:49:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8830652Z ##[error]./pandas/core/indexes/multi.py:1143:15:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8831243Z ##[error]./pandas/core/indexes/multi.py:1146:29:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8831817Z ##[error]./pandas/core/reshape/reshape.py:143:37:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8832381Z ##[error]./pandas/core/dtypes/concat.py:27:21:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8832963Z ##[error]./pandas/core/internals/concat.py:446:28:E721:do not compare types, use 'isinstance()'
2020-05-13T05:37:20.8833301Z ##[error]./pandas/core/arrays/interval.py:1054:5:E301:expected 1 blank line, found 0
2020-05-13T05:37:20.8833868Z ##[error]./pandas/util/_doctools.py:37:52:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8834428Z ##[error]./pandas/util/_doctools.py:38:48:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8834986Z ##[error]./pandas/util/_doctools.py:40:49:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8835554Z ##[error]./pandas/util/_doctools.py:41:49:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8836113Z ##[error]./pandas/util/_doctools.py:61:35:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8837945Z ##[error]./pandas/util/_doctools.py:76:55:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8838579Z ##[error]./pandas/util/_doctools.py:77:55:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8839206Z ##[error]./pandas/util/_doctools.py:91:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8839782Z ##[error]./pandas/plotting/_matplotlib/tools.py:374:9:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8840359Z ##[error]./pandas/plotting/_matplotlib/boxplot.py:147:29:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8840933Z ##[error]./pandas/plotting/_matplotlib/boxplot.py:148:43:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8842322Z ##[error]./pandas/plotting/_matplotlib/core.py:1508:56:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8843347Z ##[error]./pandas/io/pytables.py:4587:59:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8844029Z ##[error]./pandas/io/parsers.py:2971:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8845476Z ##[error]./pandas/io/parsers.py:3000:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8846243Z ##[error]./pandas/io/parsers.py:3020:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8847558Z ##[error]./pandas/io/formats/format.py:979:57:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8848615Z ##[error]./pandas/tests/groupby/test_categorical.py:505:13:F541:f-string is missing placeholders
2020-05-13T05:37:20.8849732Z ##[error]./pandas/tests/groupby/transform/test_numba.py:20:46:F541:f-string is missing placeholders
2020-05-13T05:37:20.8850374Z ##[error]./pandas/tests/groupby/transform/test_numba.py:23:46:F541:f-string is missing placeholders
2020-05-13T05:37:20.8851580Z ##[error]./pandas/tests/groupby/aggregate/test_numba.py:21:46:F541:f-string is missing placeholders
2020-05-13T05:37:20.8852714Z ##[error]./pandas/tests/groupby/aggregate/test_numba.py:24:46:F541:f-string is missing placeholders
2020-05-13T05:37:20.8853337Z ##[error]./pandas/tests/indexes/interval/test_interval.py:56:43:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8853951Z ##[error]./pandas/tests/indexes/interval/test_interval.py:77:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8854548Z ##[error]./pandas/tests/indexes/interval/test_constructors.py:333:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8855131Z ##[error]./pandas/tests/indexes/multi/test_indexing.py:797:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8855712Z ##[error]./pandas/tests/plotting/test_datetimelike.py:760:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8856293Z ##[error]./pandas/tests/plotting/test_datetimelike.py:787:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8856877Z ##[error]./pandas/tests/plotting/test_datetimelike.py:800:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8857459Z ##[error]./pandas/tests/plotting/test_datetimelike.py:866:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8858037Z ##[error]./pandas/tests/plotting/test_datetimelike.py:884:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8858611Z ##[error]./pandas/tests/plotting/test_datetimelike.py:997:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8859187Z ##[error]./pandas/tests/plotting/test_datetimelike.py:1006:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8859765Z ##[error]./pandas/tests/plotting/test_datetimelike.py:1131:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8860363Z ##[error]./pandas/tests/plotting/test_datetimelike.py:1135:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8860937Z ##[error]./pandas/tests/plotting/test_datetimelike.py:1236:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8861515Z ##[error]./pandas/tests/frame/methods/test_to_dict.py:121:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8862087Z ##[error]./pandas/tests/reshape/test_pivot.py:2069:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8862677Z ##[error]./pandas/tests/reshape/merge/test_merge.py:2183:38:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8863272Z ##[error]./pandas/tests/reshape/merge/test_merge.py:2183:69:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8863847Z ##[error]./pandas/tests/io/test_stata.py:1864:35:F541:f-string is missing placeholders
2020-05-13T05:37:20.8864409Z ##[error]./pandas/tests/io/test_sql.py:2402:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8864972Z ##[error]./pandas/tests/io/test_sql.py:2683:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8865545Z ##[error]./pandas/tests/io/formats/test_format.py:1801:31:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8866121Z ##[error]./pandas/tests/extension/test_interval.py:30:32:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8866713Z ##[error]./pandas/tests/indexing/test_floats.py:287:50:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8867287Z ##[error]./pandas/tests/indexing/test_floats.py:346:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8867867Z ##[error]./pandas/tests/indexing/test_floats.py:360:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8868581Z ##[error]./pandas/tests/indexing/test_floats.py:383:17:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8869229Z ##[error]./pandas/tests/indexing/test_floats.py:407:48:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8869822Z ##[error]./pandas/tests/indexing/test_floats.py:439:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8870388Z ##[error]./pandas/tests/indexing/test_floats.py:455:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8870956Z ##[error]./pandas/tests/indexing/test_floats.py:470:13:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8871524Z ##[error]./pandas/tests/indexing/test_floats.py:492:66:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8872091Z ##[error]./pandas/tests/indexing/test_floats.py:519:32:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8872862Z ##[error]./pandas/tests/indexing/test_loc.py:802:22:E741:ambiguous variable name 'l'
2020-05-13T05:37:20.8873460Z ##[error]./asv_bench/benchmarks/reshape.py:106:36:E741:ambiguous variable name 'l'
```
There is an additional issue with flake8-rst, e.g. here:
- https://gitlab.com/pycqa/flake8/-/issues/641
- https://github.com/kataev/flake8-rst/issues/22
Happy to have a go, but would like suggestions about how to proceed? Suggestion:
Staged approach:
- [x] Pin flake < 3.8.0 to deal with flake8-rst issue
- [x] Ignore errors to get build green
- [x] Address F541
- [x] Address E721
- [ ] Address E741 (or not?)
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CI: Pin flake8 < 3.8.0
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CONTRIBUTOR
| null |
xref #34150
This handles multiple issues:
- flake8 >= 3.8.1 introduces a new version of pycodestyle which introduces
extra checking
- flake8-rst is broken with the newest version of flake8
- See kataev/flake8-rst#22
A follow-up PR handling the first of these is forth-coming
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CONTRIBUTOR
| null |
xref #34150
This partially addresses the above issue by:
- Ignoring E741
- Fixing other linting errors
The issue with flake8-rst is handled temporarily in #34151, and the build here will fail the flake8-rst part until that is merged in.
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ENH: Allow for `loc` to specify numeric and/or name slicing
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| 2020-08-21T17:05:38Z
| 2020-05-13T11:19:33Z
|
NONE
| null |
#### Is your feature request related to a problem?
Yes. The problem is that many use cases require specifying a numeric row range (say, the 10th - 20th rows) and a name-based column range (columns "timestamp" and "value").
As of now, the most consistent way to solve it is:
```python
df.loc[df.index[start_val:(end_val + 1)],cols_of_interest]
```
This is not ideal because
1. It is long and ugly to need to use `df.index` at all.
1. Notice the `(end_val + 1)`: While most of Python uses exclusive ranges (eg, `range(1,3)` does not include `3`), `pandas`' `loc` is inclusive. So, developers get used to `df.loc[1:3,'a']` giving all three rows.
#### Describe the solution you'd like
[this should provide a description of the feature request, e.g. "`DataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature]
`DataFrame.loc` should have a new argument `num_idxs` which is a boolean list of 2 values, specifying whether the row or column indices should be numeric or names. Eg, `[True, False]` means that the rows should be treated as numeric and the columns as names.
This argument should have a default value of `None`, since default lists are dangerous in Python, but that `None` should resolve to `[True, False]`, since this is the most common situation that most of us want (I think).
The behavior of numeric should be exactly identical to the current implementation of `iloc`. The behavior of names should be identical to `loc`, currently.
#### API breaking implications
[this should provide a description of how this feature will affect the API]
The only API breaking implication I am aware of is that using the default I suggested (rows being treated as numeric) will create "off-by-one" issues, since `iloc` is exclusive and `loc` is inclusive.
This could potentially be handled by first making the default resolve to `[False, False]` upon initial introduction, but it should eventually be deprecated to `[True, False]` as I say above, since this is the most common case.
#### Describe alternatives you've considered
[this should provide a description of any alternative solutions or features you've considered]
As mentioned above, I sprinkle my code with the ugly:
```python
df.loc[df.index[start_val:(end_val + 1)],cols_of_interest]
```
The reason this is necessary is because (a) row *names* rarely mean anything to me in my work, and (b) I try to make code as generalizable as possible.
#### Additional context
I feel the description above covers it, but I am happy to add some examples if there is confusion.
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DOC: add black badge
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| 1
| 2020-05-13T11:03:30Z
| 2020-05-18T06:46:53Z
| 2020-05-17T22:26:26Z
|
MEMBER
| null |
IMO pandas benefits greatly from `black`, so I thought I'd add their badge to the readme
- [x] passes `black pandas`
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MDU6SXNzdWU2MTczODU5NzE=
| 34,155
|
BUG: str.split fails on a multiple character match
|
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closed
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[] | null | 1
| 2020-05-13T11:55:33Z
| 2020-08-21T17:05:38Z
| 2020-05-13T12:31:01Z
|
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({
'fails': ['let me||test', 'the||splitting']
})
test_df['fails'].str.split('||')
```
#### Problem description
I expect pandas to retain the same behavior as `str.split` and return the split column.
However, it doesn't handle multiple characters, and throws this instead (I didn't include the whole traceback to keep the issue clean).
```python
ValueError: split() requires a non-empty pattern match.
```
#### Expected Output
```python
0 [let me, test]
1 [the, splitting]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9f69ccadffbc67de0c946e4fd9f22d41d30392f3
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
Version : Darwin Kernel Version 19.4.0: Wed Mar 4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1563.g9f69ccadf
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 18.1
setuptools : 40.6.2
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
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2MTczODk3NDk=
| 34,156
|
BUG: Pandas import error Python 2.7.6 isinstance() arg 2 must be a class, type, or tuple of classes and types
|
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[] | null | 6
| 2020-05-13T12:02:05Z
| 2020-06-15T16:06:45Z
| 2020-06-15T16:06:44Z
|
NONE
| null |
Hi,
I am obliged to use Python 2.7.6 and pandas below 0.24.2.
The only thing I am trying to do as for now is 'import pandas'.
And I keep getting the error below. I have checked online and tslib was fixed in new versions but I have to stay on 2.7.6.
I have already tried pip uninstall and pip install again.
Any possible fix you might know of ?
Thanking you very much for your answer,
Regards and take care
ERROR LOG:
---------------------------------------------------------------------------------------------------------------
468","import pandas |
468"," File "C | \Python27\lib\site-packages\pandas\__init__.py", line 42, in <module>
|
468","from pandas.core.api import * |
|
468"," File "C | \Python27\lib\site-packages\pandas\core\api.py", line 10, in <module>
|
468","from pandas.core.groupby import Grouper |
|
468"," File "C | \Python27\lib\site-packages\pandas\core\groupby.py", line 47, in <module>
|
468","from pandas.core.index import (Index, MultiIndex, |
|
468"," File "C | \Python27\lib\site-packages\pandas\core\index.py", line 2, in <module>
|
468","from pandas.core.indexes.api import * |
|
468"," File "C | \Python27\lib\site-packages\pandas\core\indexes\api.py", line 9, in <module>
|
468","from pandas.core.indexes.interval import IntervalIndex # noqa |
|
468"," File "C | \Python27\lib\site-packages\pandas\core\indexes\interval.py", line 30, in <module>
|
468","from pandas.core.indexes.datetimes import date_range |
|
468"," File "C | \Python27\lib\site-packages\pandas\core\indexes\datetimes.py", line 39, in <module>
|
468","from pandas.tseries.frequencies import ( |
|
468"," File "C | \Python27\lib\site-packages\pandas\tseries\frequencies.py", line 18, in <module>
|
468","from pandas.tseries.offsets import DateOffset |
|
468"," File "C | \Python27\lib\site-packages\pandas\tseries\offsets.py", line 2692, in <module>
|
484","class Tick(SingleConstructorOffset) |
|
484"," File "C | \Python27\lib\site-packages\pandas\tseries\offsets.py", line 2693, in Tick
|
484","_inc = Timedelta(microseconds=1000) |
|
484"," File "pandas\_libs\tslib.pyx", line 2585, in pandas._libs.tslib.Timedelta.__new__ |
|
484","TypeError"
484","isinstance() arg 2 must be a class, type, or tuple of classes and types
|
484","Python interpretor failed
|
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|
QST: Select some data based on a condition
|
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| 2020-08-21T17:05:38Z
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|
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).
---
#### Question about pandas
```python
def condition(index):
# Complex operations here, involving several external arrays queries
# And multiple for loops
return [ True / False ] # Returns a boolean
```
Hi!
I have a condition (like above) and a DataFrame.
How may I filter my DataFrame to only keep the rows for which the index returns True when passed to the above `condition()`?
I cannot find a way, using pandas, to pass a function to `.where()` or to `.filter()`.
Please do not hesitate if you have questions.
Thanks in advance!
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REGR: use dtype.fill_value in ExtensionBlock.fill_value where available
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| 2020-05-13T15:50:08Z
| 2020-06-04T12:02:27Z
| 2020-06-01T01:46:31Z
|
CONTRIBUTOR
| null |
- [X] closes #27781, closes #29563
- [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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ENH: allow storing ExtensionArrays in the Index
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| 2020-05-13T16:12:01Z
| 2021-11-16T12:38:35Z
| 2021-10-31T00:31:52Z
|
MEMBER
| null |
I started experimenting with allowing EAs to be used in the Index (so we can put nullable int/bool, string dtype etc in the Index).
My approach is to use the base `Index` class for this (and not a custom subclass):
- We use a similar approach for Series, which can also hold either ndarray or EA as underlying values
- I don't think we want to start adding subclasses for all new EAs (like Int64Index for the new Int64Dtype is not quite possible ..)
- This also works for external EAs
For now, it just integrates it, so an EA can be put in an index and tested operations work. This PR does *not* (yet) handle ensuring that operations relying on the index' Engine are efficient (currently it simply uses `np.asarray(EA)` as the engine values, which eg will be object dtype for Int64Dtype)
cc @jbrockmendel @TomAugspurger @jreback
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MDExOlB1bGxSZXF1ZXN0NDE3NTI2Nzg1
| 34,160
|
ENH: enable mul, div on Index by dispatching to Series
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| 5
| 2020-05-13T18:08:12Z
| 2020-08-06T15:52:14Z
| 2020-08-06T15:49:07Z
|
MEMBER
| null |
One of the last remaining inconsistencies between Index and Series arithmetic is that division and multiplication is entirely disabled for object-dtype Index, while for Series we operate pointwise. This PR makes Index behave the same way, and simplifies the code by using existing Series-op-wrapping code to do so.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I'm pretty sure there is at least one issue for this, will track it down.
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BUG: Setting values in a copy of a DataFrame, using iloc, changes the original DataFrame too
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| 2
| 2020-05-13T18:24:42Z
| 2020-08-21T17:05:39Z
| 2020-05-13T22:09:08Z
|
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
import numpy as np
import pandas as pd
# 1. Create original DataFrame
a = np.array([[1, 2, 3], [4, 5, 6]])
a = pd.DataFrame(a)
# 2. Create copy DataFrame
b = a
print("\n\n Before \n a \n", a, "\n b \n", b)
# 3. Set 2nd column of copy
b.iloc[:,2] = 0
# 4. Second column of original has also been set
print("\n After \n a\n", a, "\n b \n", b)
# 5. Now try all this with a second copy
a = np.array([[1, 2, 3], [4, 5, 6]])
a = pd.DataFrame(a)
b = a
c = b
print("\n\n Before \n a \n", a, "\n b \n", b, "\n c \n", c)
c.iloc[:,2] = 0
print("\n\n After \n a \n", a, "\n b \n", b, "\n c \n", c)
```
#### Problem description
If I a create a df as a copy of an existing df (i.e. using b = a), then when I change values in the copy using iloc (i.e. b.iloc[:,2] = 0 ), then the corresponding values in the original df also get set (i.e. both a and b change).
The output of the above program which I got is:
Before
a
0 1 2
0 1 2 3
1 4 5 6
b
0 1 2
0 1 2 3
1 4 5 6
After
a
0 1 2
0 1 2 0
1 4 5 0
b
0 1 2
0 1 2 0
1 4 5 0
Before
a
0 1 2
0 1 2 3
1 4 5 6
b
0 1 2
0 1 2 3
1 4 5 6
c
0 1 2
0 1 2 3
1 4 5 6
After
a
0 1 2
0 1 2 0
1 4 5 0
b
0 1 2
0 1 2 0
1 4 5 0
c
0 1 2
0 1 2 0
1 4 5 0
#### Expected Output
Only the df which is changed explicitly in the code should change. The original should remain unchanged. In the first example above, the output should be:
Before
a
0 1 2
0 1 2 3
1 4 5 6
b
0 1 2
0 1 2 3
1 4 5 6
After
a
0 1 2
0 1 2 3
1 4 5 6
b
0 1 2
0 1 2 0
1 4 5 0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-96-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
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 : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
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 : 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 : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.0
</details>
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BUG: Performance regression in categorical indexer
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| 3
| 2020-05-13T19:01:35Z
| 2020-05-19T21:40:44Z
| 2020-05-19T21:40:36Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
Apologies if it has been, there are a few issues that may be related, in particular
https://github.com/pandas-dev/pandas/issues/33924
https://github.com/pandas-dev/pandas/issues/28122
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
print(pd.__version__)
df = pd.DataFrame(np.random.rand(100, 5))
df['event'] = [*range(len(df)//2)]*2
b1 = pd.cut(df.loc[:,0], bins=[0, .2,.4,.6,.8, 1])
b2 = pd.cut(df.loc[:,1], bins=[0, .2,.4,.6,.8, 1])
b3 = pd.cut(df.loc[:,2], bins=[0, .2,.4,.6,.8, 1])
for _ in range(50):
df.groupby([b1, b2, b3,'event'])[4].sum()
```
#### Problem description
The above example runs some 20 times slower on pandas version 1.0 as compared to 0.24. For 0.24 timing the script reports
```
$ time python xx.py (/tmp/pandas)
0.24.2
0.92user 0.03system 0:00.95elapsed 99%CPU (0avgtext+0avgdata 70544maxresident)k
0inputs+0outputs (0major+10302minor)pagefaults 0swaps
```
and most of the loop runtime is comparable to the import time. However for pandas 1.0 I get
```
$ time python xx.py (/tmp/pandas)
1.0.3
13.18user 0.03system 0:13.22elapsed 99%CPU (0avgtext+0avgdata 74384maxresident)k
0inputs+0outputs (0major+13029minor)pagefaults 0swaps
```
I ran py-spy both cases. This is what I get for 0.24:
and this is for 1.0
Comparing the two, there seems to be some additional reindexing happening, which is responsible for most of the runtime.
#### Problem description
The code snipped above works around 20 times slower in pandas 1.03 compared to pandas 0.24.
#### Output of ``pd.show_versions()``
<details>
<summary>Slow 1.0 version</summary>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.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 : 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 : 20.0.2
setuptools : 46.2.0.post20200511
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 : 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
</details>
<details>
<summary>Fast 0.24 version</summary>
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.2.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: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 20.0.2
setuptools: 46.2.0.post20200511
Cython: None
numpy: 1.18.1
scipy: None
pyarrow: None
xarray: None
IPython: 7.13.0
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2020.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details
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MDExOlB1bGxSZXF1ZXN0NDE3NjEyNTMz
| 34,163
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CLN: remove methods of ExtensionIndex that duplicate base Index
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| 2
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| 2020-05-15T07:58:24Z
| 2020-05-15T07:58:11Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/34159
cc @jbrockmendel
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MDU6SXNzdWU2MTc3NzYxMTY=
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|
BUG: grouping with categorical interval columns
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| 2020-05-13T21:47:26Z
| 2021-08-07T06:03:41Z
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NONE
| null |
Versions:
pandas 1.0.3
numpy 1.18.1
There is a bug in the 1.XXX pandas release that does not allow you to group by a categorical interval index column together with another column.
```
import numpy as np
import pandas as pd
pd.set_option("use_inf_as_na",True)
t = pd.DataFrame({"x":np.random.randn(100), 'w':np.random.choice(list("ABC"), 100)})
qq = pd.qcut(t['x'], q=np.linspace(0,1,5))
```
This works and gives the expected result:
`
t.groupby([qq])['x'].agg('mean')
`
`x
(-10.001, -1.0] -1.431893
(-1.0, 0.0] -0.423564
(0.0, 1.0] 0.461174
(1.0, 10.0] 1.662297
Name: x, dtype: float64
`
This raises a TypeError:
`
t.groupby([qq,'w'])['x'].agg('mean')
`
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-6d7782f17653> in <module>
----> 1 t.groupby([qq,'w'])['x'].agg('mean')
~/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, *args, **kwargs)
245
246 if isinstance(func, str):
--> 247 return getattr(self, func)(*args, **kwargs)
248
249 elif isinstance(func, abc.Iterable):
~/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in mean(self, *args, **kwargs)
1223 nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
1224 return self._cython_agg_general(
-> 1225 "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
1226 )
1227
~/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
907 raise DataError("No numeric types to aggregate")
908
--> 909 return self._wrap_aggregated_output(output)
910
911 def _python_agg_general(self, func, *args, **kwargs):
~/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _wrap_aggregated_output(self, output)
384 output=output, index=self.grouper.result_index
385 )
--> 386 return self._reindex_output(result)._convert(datetime=True)
387
388 def _wrap_transformed_output(
~/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _reindex_output(self, output, fill_value)
2481 levels_list = [ping.group_index for ping in groupings]
2482 index, _ = MultiIndex.from_product(
-> 2483 levels_list, names=self.grouper.names
2484 ).sortlevel()
2485
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py in from_product(cls, iterables, sortorder, names)
551
552 codes = cartesian_product(codes)
--> 553 return MultiIndex(levels, codes, sortorder=sortorder, names=names)
554
555 @classmethod
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py in __new__(cls, levels, codes, sortorder, names, dtype, copy, name, verify_integrity, _set_identity)
278
279 if verify_integrity:
--> 280 new_codes = result._verify_integrity()
281 result._codes = new_codes
282
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _verify_integrity(self, codes, levels)
366
367 codes = [
--> 368 self._validate_codes(level, code) for level, code in zip(levels, codes)
369 ]
370 new_codes = FrozenList(codes)
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py in <listcomp>(.0)
366
367 codes = [
--> 368 self._validate_codes(level, code) for level, code in zip(levels, codes)
369 ]
370 new_codes = FrozenList(codes)
~/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _validate_codes(self, level, code)
302 to a level with missing values (NaN, NaT, None).
303 """
--> 304 null_mask = isna(level)
305 if np.any(null_mask):
306 code = np.where(null_mask[code], -1, code)
~/miniconda3/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
~/miniconda3/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _isna_old(obj)
181 return False
182 elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass, ABCExtensionArray)):
--> 183 return _isna_ndarraylike_old(obj)
184 elif isinstance(obj, ABCGeneric):
185 return obj._constructor(obj._data.isna(func=_isna_old))
~/miniconda3/lib/python3.7/site-packages/pandas/core/dtypes/missing.py in _isna_ndarraylike_old(obj)
281 else:
282 result = np.empty(shape, dtype=bool)
--> 283 vec = libmissing.isnaobj_old(values.ravel())
284 result[:] = vec.reshape(shape)
285
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got Categorical)
```
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MDU6SXNzdWU2MTc3ODQ1MjE=
| 34,165
|
BUG: Inconsistent behaviour for assert_index_equal on empty Indices
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[] | null | 1
| 2020-05-13T22:04:31Z
| 2020-08-21T17:05:39Z
| 2020-05-13T22:13:40Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
pandas.testing.assert_index_equal(
pandas.RangeIndex(0, 0),
pandas.RangeIndex(5, 5)
)
pandas.testing.assert_index_equal(
pandas.RangeIndex(0, 0),
pandas.Index([])
)
pandas.testing.assert_index_equal(
pandas.RangeIndex(0, 1),
pandas.Index([0])
)
```
#### Problem description
The first and third assertions pass, while the second fails. This seems like inconsistent behaviour, particularly with respect to the 2nd and 3rd cases.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.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 : 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 : 20.0.2
setuptools : 46.2.0.post20200511
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
</details>
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MDU6SXNzdWU2MTc3OTY1Mjg=
| 34,166
|
ENH: Add high-performance DecimalArray type?
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[] | null | 10
| 2020-05-13T22:32:27Z
| 2021-08-07T06:04:10Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
Doing operations with decimal types is currently very slow, since pandas stores them as python Decimal objects. I think it would be nice to have a high-performance, vectorized DecimalArray ExtensionArray type. Would you be open to a PR adding a type like this, or is this something you think belongs outside the pandas library?
#### Describe the solution you'd like
Create an ExtensionArray datatype that internally represents numbers similar to in pyarrow (as ints, with precision and scale). (Require that all values have the same precision and scale.) Implement basic arithmetic operations (+, -, *, /, etc.).
#### API breaking implications
By adding a `__from_arrow__` and `__to_arrow__` field to DecimalArray, this would cause decimal data from pyarrow to be converted to DecimalArray rather than object array by default, which would be a breaking change.
#### Describe alternatives you've considered
The status quo (loading as object arrays) works, but is slow.
#### Additional context
This would also be really helpful for loading parquet files that decimal types via pyarrow more quickly, since the step to instantiate python Decimal objects for each value can be very slow for large DataFrames.
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MDExOlB1bGxSZXF1ZXN0NDE3NjYzODMx
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REF: make BaseOffset a cdef class
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| 8
| 2020-05-13T23:24:00Z
| 2020-05-18T18:04:15Z
| 2020-05-18T18:00:35Z
|
MEMBER
| null |
@jreback i could use help here with one last stubborn pickle/pytables test `test_read_py2_hdf_file_in_py3`
Within this legacy file we have the substring (sub-bytes?)
`blob = b"ccopy_reg\n_reconstructor\np1\n(cpandas.tseries.offsets\nBusinessDay\np2\nc__builtin__\nobject\np3\nNtRp4\n(dp5\nS'normalize'\np6\nI00\nsS'n'\nI1\nsS'kwds'\np7\n(dp8\nsS'offset'\np9\ncdatetime\ntimedelta\np10\n(I0\nI0\nI0\ntRp11\nsb."`
AFAICT it is doing `pickle.loads(blob)` and getting:
```
>>> pickle.loads(blob)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/Cellar/python/3.7.7/Frameworks/Python.framework/Versions/3.7/lib/python3.7/copyreg.py", line 43, in _reconstructor
obj = object.__new__(cls)
TypeError: object.__new__(BusinessDay) is not safe, use BusinessDay.__new__()
```
Then it is falling back and for reasons not clear to me is calling `to_offset(blob)` which raises `ValueError` which is what pytest shows us.
IIUC there is a way to register functions for unpickling, but _none_ of the functions in pd.compat.pickle_compat are getting called in this process, so I'm at a loss for how to do this. Any ideas?
|
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MDExOlB1bGxSZXF1ZXN0NDE3NjgyOTgy
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DOC: Fix comment
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| 1
| 2020-05-14T00:30:16Z
| 2020-05-17T22:24:23Z
| 2020-05-17T22:24:19Z
|
CONTRIBUTOR
| null |
The code clears the O_NONBLOCK flag (that is, makes IO blocking), so make the comment match.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
TST: Added test for json containing the key "last_status_change_at"
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|
NONE
| null |
- [x] closes #32409
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: pandas.concat() got an unexpected keyword argument 'join_axes'
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| 2020-08-21T17:05:39Z
| 2020-05-14T01:25:27Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] 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
```
#### Problem description
pandas.concat() got an unexpected keyword argument 'join_axes'
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
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 : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
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 : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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DEPR: DateOffset.__call__
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- [ ] 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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MDU6SXNzdWU2MTc5ODU5Njk=
| 34,172
|
BUG: GroupBy sum on an Int64 column with min_count=1 is wrong when all values are null
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| 3
| 2020-05-14T07:17:30Z
| 2020-05-15T00:50:53Z
| 2020-05-14T16:34:52Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
import pandas as pd
df = pd.DataFrame(
[
{"key": 1, "value": None},
],
dtype="Int64",
)
>>> df["value"].sum(min_count=1)
<NA>
>>> df.groupby("key")["value"].sum(min_count=1)
key
1 -9223372036854775808
Name: value, dtype: Int64
```
#### Problem description
When summing with min_count=1 (so that groups with all nulls sum to null), DataFrame.sum exhibits expected behaviour (ie, all nulls sum to null), but GroupBy.sum returns the minimum Int64 -9223372036854775808 rather than `<NA>`.
#### Expected Output
```python
>>> df.groupby("key")["value"].sum(min_count=1)
key
1 <NA>
Name: value, dtype: Int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
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 : 7.14.0
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
</details>
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Backport PR #33645, #33632 and #34087 on branch 1.0.x
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| 2
| 2020-05-14T10:10:42Z
| 2020-05-14T11:42:02Z
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|
MEMBER
| null |
xref #33645, #33632 and #34087
fix test_s3_roundtrip failure on branch 1.0.x https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=35271&view=logs&j=98c84d08-34d8-513e-80be-2c581992dd5a&t=f75024c1-aff0-57fb-4f0e-35923b654e09
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BUG: pd.concat inconsistent behaviour with empty and tz-aware Series
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| 2
| 2020-05-14T14:39:02Z
| 2021-08-07T06:04:38Z
| null |
NONE
| null |
```python
df = pd.concat([
pd.Series(dtype=float),
pd.Series(index=[pd.Timestamp('2020-01-01T00:00:00+00:00')], data=0, dtype=float)],
axis=1)
>>> df
0 1
2020-01-01 00:00:00+00:00 NaN 0.0
```
```python
df = pd.concat([
pd.Series(index=[pd.Timestamp('2020-01-01T00:00:00')], data=0, dtype=float)],
pd.Series(dtype=float),
axis=1)
>>> df
0 1
2020-01-01 0.0 NaN
```
```python
df = pd.concat([
pd.Series(index=[pd.Timestamp('2020-01-01T00:00:00+00:00')], data=0, dtype=float),
pd.Series(dtype=float)],
axis=1)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\reshape\concat.py", line 281, in concat
sort=sort,
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\reshape\concat.py", line 452, in __init__
self.new_axes = self._get_new_axes()
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\reshape\concat.py", line 517, in _get_new_axes
for i in range(ndim)
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\reshape\concat.py", line 517, in <listcomp>
for i in range(ndim)
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\reshape\concat.py", line 523, in _get_comb_axis
self.objs, axis=data_axis, intersect=self.intersect, sort=self.sort
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\indexes\api.py", line 90, in get_objs_combined_axis
return _get_combined_index(obs_idxes, intersect=intersect, sort=sort)
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\indexes\api.py", line 138, in _get_combined_index
index = union_indexes(indexes, sort=sort)
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\indexes\api.py", line 202, in union_indexes
return result.union_many(indexes[1:])
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\indexes\datetimes.py", line 363, in union_many
this, other = this._maybe_utc_convert(other)
File "C:\Users\user\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\indexes\datetimelike.py", line 832, in _maybe_utc_convert
raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
TypeError: Cannot join tz-naive with tz-aware DatetimeIndex
```
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.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 : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.45.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDE4MDQ0MDI5
| 34,175
|
Update test_period_range.py
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| 2
| 2020-05-14T14:43:32Z
| 2020-05-15T12:58:21Z
| 2020-05-15T12:58:18Z
|
CONTRIBUTOR
| null |
Attempting to implement test for resolved issue #21793
- [x] closes #21793
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CLN: update cimports
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MEMBER
| null |
some cosmetic, some ordering conventions, some using cimportable classes instead of py-classes
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BUG: Subclassed DataFrame doesn't persist _metadata properties across binary operations
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| 7
| 2020-05-14T18:10:35Z
| 2020-12-07T08:10:53Z
| 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
When subclassing a DataFrame, fields added to the `_metadata` property are only persisted across some operations (such as slicing) and not others (such as any arithmetic operation).
I would expect any properties defined on the subclass to persist whenever the result of an operation is an instance of the subclass.
The following is the example taken from the "Extending Pandas" docs: https://pandas.pydata.org/pandas-docs/stable/development/extending.html
```python
import pandas as pd
class SubclassedDataFrame2(pd.DataFrame):
# temporary properties
_internal_names = pd.DataFrame._internal_names + ['internal_cache']
_internal_names_set = set(_internal_names)
# normal properties
_metadata = ['added_property']
@property
def _constructor(self):
return SubclassedDataFrame2
df = SubclassedDataFrame2({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
df.internal_cache = "cached"
df.added_property = "property"
```
With the above setup, here's how to reproduce the problem:
```python
>>> df.added_property
'property'
>>> df[["A", "B"]].added_property # this works as expected
'property'
>>> (df * 2).added_property # I would expect this to work
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/clausmith/Developer/pandas-bug/pandas/pandas/core/generic.py", line 5220, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'SubclassedDataFrame2' object has no attribute 'added_property'
```
#### Problem description
The current behavior means that you can almost never rely on custom properties to persist on a subclassed DataFrame. This substantially reduces the utility of these custom properties.
#### Expected Output
I would expect the `added_property` property in the example above to persist after performing the arithmetic operation on the DataFrame. _Especially_ because the result of `(df * 2)` is still an instance of `SubclassedDataFrame2`.
#### Output of ``pd.show_versions()``
<details>
```
commit : 507cb1548d36bbf48c3084a78d59af2fed78a9d1
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Feb 10 21:08:45 PST 2020; root:xnu-4903.278.28~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1576.g507cb1548
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
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MDExOlB1bGxSZXF1ZXN0NDE4MTc3Njc4
| 34,178
|
PERF: avoid creating numpy array in groupby.first|last
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| 1
| 2020-05-14T18:36:35Z
| 2020-05-24T16:56:36Z
| 2020-05-15T12:54:41Z
|
CONTRIBUTOR
| null |
A unneeded numpy array is created for each group when calling ``groupby.first`` and ``groupby.last`` on ExtensionArrays. This avoids that.
```python
>>> cat = pd.Categorical(["a"] * 1_000_000 + ["b"] * 1_000_000)
>>> ser = pd.Series(cat)
>>> %timeit ser.groupby(cat).first()
210 ms ± 3.03 ms per loop # master
78.4 ms ± 766 µs per loop # this PR
```
The same speedup is archieved for ``groupby.last``. The above is 3x faster than in master because there are two groups == we save creating two arrays. If there were more groups/larger arrays, we'd get even more improvements.
Also adds some type hints to help understand what parameters these funtions accept.
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|
CLN/TST: Remove Base Class and all subclasses and fixturize data
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| 2
| 2020-05-14T19:00:01Z
| 2020-05-19T08:11:25Z
| 2020-05-15T12:53:25Z
|
MEMBER
| null |
- [x] closes #30486
This PR will be able to finally close 30486 in my opinion (even a little beyond the scope ^^), and after this, all classes adhered to Base in `tests/window` folder is removed.
Although the PR is huge, it actually is quite simple, basically it does:
1. Turn needed data from `_create_data()` in `Base` class as fixtures
2. Remove Base class
3. Change the shared methods which need to inherit data from Base class to use arguments, and in the real tests, assign corresponding fixtures to those arguments accordingly.
4. Remove all sub-classes which inherit from Base class, and now they all turn to independent test functions.
cc @jreback
And there is no test missing or added, so number of tests remain unchanged for all:
This is the result after i did a quick run for all changed tests to check the number of tests:
```
test_api: 15
test_ewm: 6
test_expanding: 21
test_rolling: 150
test_window: 23
test_moments_ewm: 22
test_moments_rolling: 253
test_moments_consitency_ewm: 4310
test_moments_consistenty_expanding: 1340
test_moments_consistency_rolling: 10881
```
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|
BUG: Index formed with list of tuples return wrong output in eq ops
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| 1
| 2020-05-14T20:17:30Z
| 2020-10-18T15:01:55Z
| 2020-10-18T15:01:55Z
|
MEMBER
| null |
xref #34137
Currently
```python
>>> pd.Index([('a', 'b'), ('b', 'c'), ('c', 'a')]) == ('c', 'a')
>>> array([False, False, False])
```
The issue is such case is treated as a MultiIndex other than plain Index.
Expected:
```python
>>> pd.Index([('a', 'b'), ('b', 'c'), ('c', 'a')]) == ('c', 'a')
>>> array([False, False, True])
```
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REF: inherit BaseOffset in BusinessMixin
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| 0
| 2020-05-14T20:37:09Z
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|
MEMBER
| null |
Cython's cdef classes dont play nicely with multiple inheritance, so we're going to have to turn our mixins into a chain of base classes. This does that for BusinessMixin and BusinessHourMixin, de-duplicating some `__init__` methods along the way.
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TST: add explicit csr explicit zero tests for GH28992
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| 2020-05-14T20:44:35Z
| 2020-07-24T12:43:37Z
| 2020-07-24T12:43:37Z
|
NONE
| null |
simple test case to check that explicitly setting a value to zero in a matrix with csr representation does not cause other values to change, specifically to be set to zero erroneously
- [ ] closes #xxxx
- [*] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG/ENH: Fix apply to only call `func` once on the first column/row
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CONTRIBUTOR
| null |
closes #33879
closes #30815
closes #31620
- [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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REF: inherit BaseOffset in WeekOfMonthMixin
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MEMBER
| null |
Analogous to #34181
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|
BUG: Inconsistent behaviour when averaging Decimals, floats and ints
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| 2020-05-15T03:27:26Z
| 2020-05-19T22:09:37Z
| 2020-05-19T22:09:36Z
|
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.
---
```python
from decimal import Decimal
df = pd.DataFrame({'col_1': [Decimal(1.5), Decimal(4.0)], 'col_2': [5.0, 10.0]})
df.mean(axis=1) # returns 5, 10 -- ignoring the decimals types in the averaging
df2 = pd.DataFrame({'col_1': [Decimal(1.5), Decimal(4.0)], 'col_2': [5, 10]})
df2.mean(axis=1) # returns 3.25, 7 -- includes the decimals types in the averaging
```
#### Problem description
There is inconsistent behaviour on how Decimal is being averaging depending if it is averaged to an int vs a float. Is it expected that the two dataframes above return different results?
#### Expected Output
I would expect in both cases to see `3.25` and `7` as the mean of the rows.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
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 : 5.4.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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BUG: Accessing year 2263 datetime in a dataframe using iloc raises OutOfBoundsDatetime
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[] | null | 5
| 2020-05-15T12:55:19Z
| 2020-05-26T13:44:35Z
| 2020-05-26T13:44:35Z
|
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 datetime
import pandas as pd
df = pd.DataFrame({'x': [datetime.datetime(2_263, 1, 1)]})
print(df)
print('loc:', repr(df.loc[0, 'x']))
print('iloc:', end=' ')
try:
print(repr(df.iloc[0, 0]))
except Exception as exc:
print(repr(exc))
print('iloc after extracting column:', repr(df['x'].iloc[0]))
```
#### Problem description
Output:
```
x
0 2263-01-01 00:00:00
loc: datetime.datetime(2263, 1, 1, 0, 0)
iloc: OutOfBoundsDatetime('Out of bounds nanosecond timestamp: 2263-01-01 00:00:00')
iloc after extracting column: datetime.datetime(2263, 1, 1, 0, 0)
```
When a timestamp that fits into a `datetime.datetime` but does not fit into a `pd.Timestamp` is accessed through `df.iloc[0, 0]`, it results in OutOfBoundsDatetime being raised. In contrast, accesses through `df.loc[0, 'x']` or `df['x'].iloc[0]` successfully return the datetime.
It'd be more predictable and consistent to have the same behavior for all three access methods. As it is, unless you've tried all these access methods with suitable datetimes, you'll never guess which of them will work and which won't.
#### Expected Output
```
x
0 2263-01-01 00:00:00
loc: datetime.datetime(2263, 1, 1, 0, 0)
iloc: datetime.datetime(2263, 1, 1, 0, 0)
iloc after extracting column: datetime.datetime(2263, 1, 1, 0, 0)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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CLN: deduplicate __setitem__ and _reduce on masked arrays
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| 2020-05-21T14:02:29Z
| 2020-05-20T20:54:37Z
|
MEMBER
| null |
For `__setitem__`, the only difference was that they both call to their own "coercing" function, so I made this available as a helper method, so it the rest of the implementation of `__setitem__` can be shared.
For `_reduce`, everything was the same, BooleanArray just has an additional check for `any` and `all`.
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BUG: pandas.read_excel creates a DataFrame with incorrect multi-level columns
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| 3
| 2020-05-15T14:12:20Z
| 2021-08-07T06:06:48Z
| 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.
There is a file a.xlsx with only index and multi-level columns:
<img width="547" alt="image" src="https://user-images.githubusercontent.com/33289453/82064630-9e025000-96ff-11ea-8f59-278622c2202a.png">
```pd.read_excel('a.xlsx', index_col=0, header=[0, 1])``` creates a DataFrame with incorrect multi-level columns:
<img width="502" alt="image" src="https://user-images.githubusercontent.com/33289453/82058218-06006880-96f7-11ea-8098-879220a4e88c.png">
However, if the file b.xlsx with the same structure (shown below) is not empty, the result appears to be correct:
<img width="568" alt="C559F1A1-36E2-4B1E-B6EF-8AD1EDAE7420" src="https://user-images.githubusercontent.com/33289453/82063866-9c845800-96fe-11ea-85c4-7848d83a0ab1.png">
<img width="598" alt="image" src="https://user-images.githubusercontent.com/33289453/82063912-aa39dd80-96fe-11ea-844f-2b7ff2156b19.png">
#### Problem description
The parameter ```header=[0, 1]``` indicates that the first two rows of the excel file should be used as multi-level columns, but the result got first three rows as the multi-level columns.
#### Expected Output
```pd.read_excel('a.xlsx', index_col=0, header=[0, 1])``` should get the DataFrame:
<img width="293" alt="image" src="https://user-images.githubusercontent.com/33289453/82059223-4e6c5600-96f8-11ea-8363-c829d43324db.png">
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.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 : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.2.0.post20200511
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 : 3.1.3
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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MDU6SXNzdWU2MTkwMDIyMjU=
| 34,189
|
BUG: dt.round affects data other than target series
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| 4
| 2020-05-15T14:20:56Z
| 2020-05-15T14:30:48Z
| 2020-05-15T14:28:19Z
|
NONE
| null |
Hi friends,
From the below experiment you can see dt.round unexpectedly affects non target data. Is this expected behavior?
```python
from datetime import datetime
import pandas as pd
def test_rounding_affects_other_frames():
dt = datetime.today()
s = pd.Series([dt], dtype='datetime64[ns]', name='datetime')
original = pd.DataFrame(s)
copied = original[:]
original_ms = original['datetime'][0].microsecond
assert original is not copied
# Round the copied dataframe
copied['datetime'] = copied['datetime'].dt.round('ms')
new_ms = copied['datetime'][0]
assert original_ms != new_ms
# The original dataframe has unexpectedly been rounded!
original_ms_2 = original['datetime'][0].microsecond
assert original_ms_2 != original_ms
```
#### Problem description
You can see the original data frame also had its microseconds rounded when we rounded the copied frame.
#### Expected Output
I would not expect the copied frame to have its data changed.
|
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Backport PR #33983 on branch 1.0.x (BUG: Use args and kwargs in Rolling.apply)
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MEMBER
| null |
xref #33983, #33433
fixes regression in 1.0.0
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DEPR: deprecate Index.__getitem__ with float key
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| 4
| 2020-05-15T15:38:03Z
| 2020-05-23T13:17:29Z
| 2020-05-23T13:17:29Z
|
MEMBER
| null |
I would propose to deprecate interpreting float numbers as integers in Index `__getitem__`.
Numpy does (no longer) allow this:
```
In [1]: a = np.array([1, 2, 3])
In [3]: a[0.0]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-3-2ec1d2c6d8d4> in <module>
----> 1 a[0.0]
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
With a Series with `iloc`, we also don't allow this:
```
In [7]: pd.Series(a).iloc[0.0]
...
TypeError: Cannot index by location index with a non-integer key
```
But for Index we do check if the float is integer-like and in that case convert to integer:
```
In [5]: pd.Index(a)[0.0]
Out[5]: 1
```
However, this is not for all Index subclasses the case, eg:
```
In [9]: pd.DatetimeIndex(a)[0.0]
...
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
So I think we can simply deprecate this to align Index with numpy/Series/some Index subclasses.
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PERF: Remove unnecessary copies in sorting functions (#33917)
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| 10
| 2020-05-15T15:43:10Z
| 2020-06-03T09:04:54Z
| 2020-06-03T09:04:43Z
|
CONTRIBUTOR
| null |
- [x] closes #33917
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR fixes pereformance regression after commit `dec736f3f` by removing unnecessary copies if `key=None` in sorting functions.
```
setup = """
import pandas as pd
import numpy as np
N = 10 ** 5
idx = pd.date_range(start="1/1/2000", periods=N, freq="s")
s = pd.Series(np.random.randn(N), index=idx)
"""
import timeit
timeit.timeit("s.sort_index()",setup=setup, number=100000)
# master
# 41.245621485984884
# now
# 14.826273362035863
```
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DEPR: deprecate Index.__getitem__ with float key
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| 3
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| 2020-05-23T13:17:33Z
| 2020-05-23T13:17:29Z
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MEMBER
| null |
See #34191 for motivation
Closes #34191
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MDExOlB1bGxSZXF1ZXN0NDE4NzM4NTUw
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DOC: Add note about columns parameter not relevant for Series in rename_axis()
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| 2020-05-19T15:15:05Z
| 2020-05-19T14:57:52Z
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CONTRIBUTOR
| null |
- [x] closes #33564
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: tslibs typing, avoid private funcs
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MDExOlB1bGxSZXF1ZXN0NDE4NzcwNTk1
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REF: remove redundant get_freq function
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| 2020-05-15T19:38:12Z
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MEMBER
| null |
We have multiple functions named get_freq/get_freq_group, trying to de-duplicate these
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ENH: implement efficient sorting methods for SparseArray (argsort/argmin/argmax)
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/27801#discussion_r426017444
`SparseArray` currently does not implement `_values_for_argsort` or `argsort/`argmin`/`argmax` itself, which means it is using the base implementation, which converts the sparse array to a dense numpy array. Room for improvements thus.
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CLN: let Index use general concat machinery - remove Index._concat_same_dtype
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| 2
| 2020-05-15T20:20:25Z
| 2020-05-18T06:42:04Z
| 2020-05-17T22:13:09Z
|
MEMBER
| null |
Following up on the recent changes in the `concat_compat` machinery (to use the `_get_common_dtype` protocol for EAs), we can also use this in the Index class.
Doing this also makes the `_concat_same_dtype` method unnecessary (as this is handled in `concat_compat` to use `EA._concat_same_type` when appropriate)
cc @jbrockmendel
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MDExOlB1bGxSZXF1ZXN0NDE4NzkyODg1
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|
PERF: Fixes performance regression in DataFrame[bool_indexer] (#33924)
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| 10
| 2020-05-15T20:25:45Z
| 2020-06-01T01:51:04Z
| 2020-06-01T01:42:31Z
|
CONTRIBUTOR
| null |
- [x] closes #33924
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Insted of calling `construction.array` in `check_array_indexer`, creates array with `dtype=bool` before calling `check_array_indexer`.
Fixes performance regression after commit b9bcdc307.
```
setup = """
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(100000, 5))
bool_indexer = [True] * 50000 + [False] * 50000
"""
import timeit
timeit.timeit("df[bool_indexer]",setup=setup, number=1000)
# master
# 27.29123323399108
# now
# 8.814320757053792
```
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MDExOlB1bGxSZXF1ZXN0NDE4ODIyNzQ0
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CLN/TYP: Groupby agg methods
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| 1
| 2020-05-15T21:20:41Z
| 2020-08-08T08:30:55Z
| 2020-05-17T22:10:31Z
|
CONTRIBUTOR
| null |
Same as #34178. I had some trouble rebasing that PR, so just made a new PR instead.
This takes care of the comment in #34178 about staticmethod.
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ENH: Implement __iter__ for Rolling and Expanding
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| 6
| 2020-05-15T21:33:29Z
| 2020-06-09T18:17:49Z
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|
MEMBER
| null |
- [x] closes #11704
- [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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MDExOlB1bGxSZXF1ZXN0NDE4ODYyNzU2
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REF: de-duplicate methods calling get_dst_info
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| 2020-05-15T22:30:59Z
| 2021-11-20T23:21:42Z
| 2020-05-16T15:53:02Z
|
MEMBER
| null |
We have a 8ish functions that all do something like:
```
def do_thing_vectorized(values, tz):
if is_utc(tz):
for i in range(n):
local_val = values[n]
do_thing(local_val)
elif is_tzlocal(tz);
for i in range(n):
local_val = get_local_val_for_tzlocal(values[n])
do_thing(local_val)
elif is_fixed_offset(tz):
for i in range(n):
local_val = get_local_val_for_fixed_offset(values[n])
do_thing(local_val)
[...]
```
with each of these functions using a different "do_thing". If this were happening in python-space, we would use a generator and it would look like:
```
def do_thing_vectorized(values, tz):
for local_val in hypothetical_generator(tz):
do_thing(local_val)
```
But we can't do that in cython-space. Until now, we've just made do with having this repeated code. This PR de-duplicates this code by implementing a Localizer class, so do_thing_vectorized becomes:
```
def do_thing_vectorized(values, tz):
localizer = get_localizer(tz)
for i in range(N):
local_val = localizer.localize(value)
do_thing(local_val)
```
I still need to run asvs, but based on ipython results, i am not seeing any performance degradation so far.
cc @jorisvandenbossche pretty good exposition huh?
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MDExOlB1bGxSZXF1ZXN0NDE4ODg3NjQz
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REF: move get_freq_group to libfreqs, de-duplicate with Resolution.get_freq_group
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| 2
| 2020-05-16T00:32:09Z
| 2020-05-18T14:10:38Z
| 2020-05-18T12:56:31Z
|
MEMBER
| null |
Having both libresolution.get_freq_group and libresolution.Resolution.get_freq_group is confusing, this adds 1 line to the module-level function to make it handle the method, removes the method, and moves the function to libfrequencies, which is a better fit.
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BUG: stacked histogram on datetimes
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| 2020-05-16T01:25:47Z
| 2021-08-07T06:07:28Z
| null |
NONE
| null |
Hello, I want to create a staked histogram, and I hoped to do it with pandas `hist` method. The data I want to plot is in one column of my DataFrame, while one other column is the factor by which I want to color the bars of the histogram. To obtain the actual dataset on which to use the `hist` method, I tried two of the methods listed here: https://stackoverflow.com/questions/41622054/stacked-histogram-of-grouped-values-in-pandas, whith a consistent result like this:
v= np.array([['2019-10-15T07:33:50.000000000', 'NaT'],
['2019-10-18T09:19:58.000000000', 'NaT'],
[ 'NaT', '2020-04-26T16:14:38.000000000'],
[ 'NaT', '2020-04-27T04:48:30.000000000']],
dtype= 'datetime64[ns]')
df= pd.DataFrame(v)
But when I call the histogram on that dataframe:
df.plot(kind= 'hist', stacked=True)
it gives me the error `ValueError: view limit minimum -0.05000000000000001 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units`.
If, on the other hand, I use method `hist` directly:
df.hist(stacked= True)
it gives me the error `ValueError: hist method requires numerical columns, nothing to plot`, even if one column alone can be plotted safely.
`NaT` values shouldn't be the problem here, and I assume that both the codes I posted should work, because they produce regular stacked histograms when the columns are numerical instead of datetime.
<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 78 Stepping 3, 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 : 0.29.17
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 3.0.3
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
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 : 3.0.3
pandas_gbq : None
pyarrow : None
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 : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.43.1
</details>
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Option to return all idxmax rows in case of ties
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| 9
| 2020-05-16T06:04:31Z
| 2021-09-11T11:46:36Z
| null |
NONE
| null |
What do you think of adding an option to « idmax » in order to return not just the first idmax but all of them in case of ties.
I think this would be really useful, especially when used with groupby. For exemple when you want the most recent yearly published data for an entity - say a company.
We could simply add a Boolean to the function like returnTies that would be false by default.
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MDU6SXNzdWU2MTk0Mjk5MzQ=
| 34,206
|
BUG: fillna(inplace=True) does not work when a subset of rows is selected via .loc
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| 11
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| 2020-05-18T11:21:46Z
| 2020-05-16T11:59:20Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'x': [float('nan')] * 3})
df.loc[1:, 'x'].fillna('bla', inplace=True)
print(df)
```
```
x
0 NaN
1 NaN
2 NaN
```
#### Problem description
`fillna()` does not work with a subset of rows, while direct assignment works:
```python
df = pd.DataFrame({'x': [float('nan')] * 3})
df.loc[1:, 'x'] = 'bla'
print(df)
```
```
x
0 NaN
1 bla
2 bla
```
`fillna()` also works when all rows are selected:
```python
df = pd.DataFrame({'x': [float('nan')] * 3})
df.loc[:, 'x'].fillna('bla', inplace=True)
print(df)
```
```
x
0 bla
1 bla
2 bla
```
This is very confusing.
#### Expected Output
```
x
0 NaN
1 bla
2 bla
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
```
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-45-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 : 1.14.1
pytz : 2019.1
dateutil : 2.2
pip : 20.0.2
setuptools : 42.0.1
Cython : None
pytest : 5.1.3
hypothesis : None
sphinx : 2.4.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 2.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.1.3
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : 0.14.0
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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TYP: add return types to read_sql|read_sql_query|read_sql_table
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| 2
| 2020-05-16T12:12:04Z
| 2020-05-21T09:58:06Z
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|
CONTRIBUTOR
| null |
Adds return types to read_sql|read_sql_query|read_sql_table.
I'd like to take on the other pd.read_* functions in follow-ups.
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MDExOlB1bGxSZXF1ZXN0NDE4OTY5NDM5
| 34,208
|
Restrict Pandas merge suffixes param type to list/tuple to avoid interchange in right and left suffix order
|
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| 10
| 2020-05-16T13:38:03Z
| 2020-06-13T09:33:31Z
| 2020-06-08T01:36:52Z
|
CONTRIBUTOR
| null |
- [x] closes #33740
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDE4OTc2MjU4
| 34,209
|
Eliminated the skipna / dropna inconsistency in the docs.
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| 2020-05-16T14:34:48Z
| 2020-05-27T04:42:27Z
| 2020-05-27T04:42:27Z
|
CONTRIBUTOR
| null |
As @bf mentioned in the comments under the issue #34063 ,
There are errors in the docs for Dataframe.mode, https://pandas.pydata.org/pandasdocs/dev/reference/api/pandas.DataFrame.mode.html (See source : https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L8722-L8807) , where the dropna parameter (which occurs more number of times than skipna) has been named skipna (first occurrence on line 8370).
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| null |
- [ ] closes #32651
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: Executing SQL containing non-escaped percent sign fails without parameters
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| 2021-03-22T19:33:12Z
| 2021-03-22T19:33:11Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.read_sql("SELECT 100 AS '100%'", con="mysql://root@localhost:3306")
```
raises the follow exception:
```
ProgrammingError: (MySQLdb._exceptions.ProgrammingError) not enough arguments for format string
```
#### Problem description
Many DB-APIs ([MySQL](https://github.com/PyMySQL/mysqlclient-python/blob/master/MySQLdb/cursors.py#L194), [PyHive](https://github.com/dropbox/PyHive/blob/master/pyhive/presto.py#L256), etc.) when executing a statement explicitly check whether the optional parameters are/are not explicitly `None` (as opposed to falsy) prior to substitution. The SQL statements in Pandas are executed via the SQLAlchemy connection (per [here](https://github.com/pandas-dev/pandas/blob/master/pandas/io/sql.py#L1080)) resulting in `parameters` of type `NoneType` being translated into an empty dictionary which can be problematic for the checks.
Many of these DB-APIs use the `%` operator for applying the parameters which is problematic if no parameters are specified and the SQL statement contains the `%` character (common in LIKE operator). One could modify their SQL by escaping the `%` percent character however the preferred fix is merely to leverage SQLAlchemy's [execution_options](https://docs.sqlalchemy.org/en/13/core/connections.html#sqlalchemy.engine.Connection.execution_options) setting `no_parameters=True` per,
> no_parameters – When True, if the final parameter list or dictionary is totally empty, will invoke the statement on the cursor as cursor.execute(statement), not passing the parameter collection at all. Some DBAPIs such as psycopg2 and mysql-python consider percent signs as significant only when parameters are present; this option allows code to generate SQL containing percent signs (and possibly other characters) that is neutral regarding whether it’s executed by the DBAPI or piped into a script that’s later invoked by command line tools.
which ensures that falsy parameters will not pass the parameters to the DB-API resulting in parameters being `None` and thus no parameter formatting will be attempted.
#### Expected Output
```python
100%
0 100
```
#### 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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- [x] closes https://github.com/pandas-dev/pandas/issues/34211
- [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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| 2020-05-16T18:01:26Z
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MEMBER
| null |
Collecting TODOs for once we bump to cython3:
- [ ] use conditional nogil e.g. [here](https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/algos.pyx#L901)
- [ ] change labels.shape[0] -> len(labels) e.g. groupbyL965
- [ ] update `searchsorted` usages to use fixed [cython](https://github.com/cython/cython/pull/3606) version
- [ ] Move Timestamp methods to _Timestamp, assuming https://github.com/cython/cython/pull/3605 is merged
- [ ] in `_libs.internals` use `from cpython.pyport cimport PY_SSIZE_T_MAX` instead of `cdef extern ...`
- [x] in `_libs.util` use `from numpy cimport PyArray_CLEARFLAGS, NPY_ARRAY_C_CONTIGUOUS, NPY_ARRAY_F_CONTIGUOUS` instead of `cdef extern ...`
- [ ] in `_libs.parsers` use `from cpython.unicode cimport PyUnicode_FromString` instead of `cdef extern ...`
- [ ] in `tslibs.parsing` replace `PyObject_Str` with `str` (xref https://github.com/cython/cython/pull/3478)
@WillAyd am i missing anything (const object[:]?)? If so, go ahead and edit the OP.
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| 2020-05-16T18:29:31Z
| 2020-05-17T21:35:13Z
| 2020-05-17T21:27:56Z
|
MEMBER
| null |
Per discussions about removing libreduction code, this is part of an effort to make the non-libreduction path more performant.
Performance comparisons are done by disabling fast_apply entirely and taking the two most-affected asvs:
```
import numpy as np
from pandas import DataFrame
N = 10 ** 4
labels = np.random.randint(0, 2000, size=N)
labels2 = np.random.randint(0, 3, size=N)
df = DataFrame(
{
"key": labels,
"key2": labels2,
"value1": np.random.randn(N),
"value2": ["foo", "bar", "baz", "qux"] * (N // 4),
}
)
%prun -s cumtime df.groupby(["key", "key2"]).apply(lambda x: 1)
PR -> 0.263 s
No optimization -> 0.308 s
master -> .039 s
%prun -s cumtime df.groupby("key").apply(lambda x: 1)
PR -> 0.083 s
No optimization -> 0.127 s
master -> .012 s
```
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MDExOlB1bGxSZXF1ZXN0NDE5MDA3OTEx
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|
CLN: remove no-longer-used reverse_ops
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| 619,553,778
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MDExOlB1bGxSZXF1ZXN0NDE5MDIxNjMz
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CLN: Timestamp unused attrs, annotations
|
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