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https://api.github.com/repos/pandas-dev/pandas/issues/32116 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32116/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32116/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32116/events | https://github.com/pandas-dev/pandas/pull/32116 | 567,771,837 | MDExOlB1bGxSZXF1ZXN0Mzc3MzM0OTc3 | 32,116 | REF: collect arithmetic benchmarks | {
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} | 3 | 2020-02-19T18:53:09Z | 2020-02-22T16:08:29Z | 2020-02-22T16:06:41Z | MEMBER | null | ATM these are scattered, making it tough to a) run just arithmetic-relevant benchmarks and b) determine what we have good benchmark coverage for.
This collects the scattered benchmarks in one place, cleaning up and fleshing these out is for a separate pass.
I also intend to do something analogous for indexing benchmarks. | {
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} | 4 | 2020-02-19T20:34:26Z | 2020-02-21T14:48:51Z | 2020-02-21T14:48:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Thanks for an amazing package and maintaining it
```python
# Your code here
s = pd.Series(["h", "i", 1])
s.convert_dtypes()
```
#### Problem description
Example raises an error, when it shouldn't. I think the case is missing from the tests
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
series with object dtype ?
#### Output of ``pd.show_versions()``
[paste the output of ``pd.show_versions()`` here below this line]
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
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 : 45.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
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 : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
<details>
</details>
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"de... | closed | false | null | [] | null | 6 | 2020-02-19T20:37:12Z | 2021-01-04T06:20:41Z | 2020-02-21T02:15:45Z | MEMBER | null | Scattered throughout the tests we have other helpers like assert_block_equal that probably belong in `tm`. Before trying to collect all of these, it makes sense to split this already-cumbersome module. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32119/events | https://github.com/pandas-dev/pandas/issues/32119 | 567,861,278 | MDU6SXNzdWU1Njc4NjEyNzg= | 32,119 | Using python comparison operators when SparseArrays of uneven length raises uninformative error | {
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"description": "Incorrect or improved er... | open | false | null | [] | null | 1 | 2020-02-19T21:33:48Z | 2021-07-29T04:05:23Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
I first discovered this issue attempting a comparison of the following form
**Case 1**
```python
>>> import pandas as pd
>>> import numpy as np
>>> a = pd.Series(pd.SparseArray(np.arange(10)))
>>> b = pd.Series(np.arange(11))
>>> (a == 5) & (b == 5)
```
which raises the following uninformative `AttirbuteError`
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-78-bb3d3268cfcc> in <module>
----> 1 (a == 5) & (b == 5)
/usr/lib/python3.8/site-packages/pandas/core/ops/__init__.py in wrapper(self, other)
1319 # integer dtypes. Otherwise these are boolean ops
1320 filler = fill_int if is_self_int_dtype and is_other_int_dtype else fill_bool
-> 1321 res_values = na_op(self.values, ovalues)
1322 unfilled = self._constructor(res_values, index=self.index, name=res_name)
1323 filled = filler(unfilled)
/usr/lib/python3.8/site-packages/pandas/core/ops/__init__.py in na_op(x, y)
1252 def na_op(x, y):
1253 try:
-> 1254 result = op(x, y)
1255 except TypeError:
1256 assert not isinstance(y, (list, ABCSeries, ABCIndexClass))
/usr/lib/python3.8/site-packages/pandas/core/arrays/sparse.py in cmp_method(self, other)
1821
1822 if isinstance(other, SparseArray):
-> 1823 return _sparse_array_op(self, other, op, op_name)
1824 else:
1825 with np.errstate(all="ignore"):
/usr/lib/python3.8/site-packages/pandas/core/arrays/sparse.py in _sparse_array_op(left, right, op, name)
493 right_sp_values = right.sp_values
494
--> 495 sparse_op = getattr(splib, opname)
496
497 with np.errstate(all="ignore"):
AttributeError: module 'pandas._libs.sparse' has no attribute 'sparse_and_object'
```
Note, if `a` and `b` are of the same length, this code runs fine:
**Case 2**
```python
a = pd.Series(pd.SparseArray(np.arange(10)))
b = pd.Series(np.arange(10))
(a == 5) & (b == 5)
```
returns
```
0 False
1 False
2 False
3 False
4 False
5 True
6 False
7 False
8 False
9 False
dtype: Sparse[bool, False]
```
and if they are both non-sparse, the code evaluates fine.
**Case 3**
```python
a = pd.Series(np.arange(10))
b = pd.Series(np.arange(11))
(a == 5) & (b == 5)
```
```
0 False
1 False
2 False
3 False
4 False
5 True
6 False
7 False
8 False
9 False
10 False
dtype: bool
```
#### Expected Output
I expect one of two behaviors:
1. An error message that states the two arrays must be of even length in **Case 1**
2. The code in **Case 1** to return the same output as in **Case 3**
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.7-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 42.0.2
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: 0.8.1
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.0.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 9 | 2020-02-19T21:34:30Z | 2020-02-26T01:37:31Z | 2020-02-26T01:37:31Z | MEMBER | null | ```
> assert re.search(r"commit\s*:\s[0-9a-f]{40}\n", result)
E AssertionError: assert None
E + where None = <function search at 0x102703560>('commit\\s*:\\s[0-9a-f]{40}\\n', '\nINSTALLED VERSIONS\n------------------\ncommit : None\npython : 3.7.6.final.0\npython-bits ... : 0.14.1\nxlrd : 1.2.0\nxlwt : 1.3.0\nxlsxwriter : 1.2.1\nnumba : 0.47.0\n')
E + where <function search at 0x102703560> = re.search
``` | {
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} | 1 | 2020-02-19T21:35:24Z | 2020-02-21T18:40:03Z | 2020-02-21T18:38:13Z | MEMBER | null | - [x] closes #31760
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Could use confirmation from OP that this does in fact close #31760. | {
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} | 1 | 2020-02-19T22:08:21Z | 2020-02-20T01:06:05Z | 2020-02-20T01:06:00Z | CONTRIBUTOR | null |
- [ ] xref #29547
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2020-02-20T00:25:36Z | 2020-02-23T14:59:00Z | 2020-02-23T14:59:00Z | NONE | null | #### Code Sample
```python
pd.DataFrame({'x': [1, 1, 2, 2], 'y': [1, 2, 3, pd.NA]}).groupby('x').first()
# *** TypeError: boolean value of NA is ambiguous
pd.DataFrame({'x': [1, 1, 2, 2], 'y': [1, 2, 3, np.nan]}).groupby('x').first()
# y
# x
# 1 1.0
# 2 3.0
pd.DataFrame({'x': [1, 1, 2, 2], 'y': [1, 2, 3, pd.NA]}).astype('Int64').groupby('x').first()
# y
# x
# 1 1
# 2 3
```
#### Problem description
Applying the `GroupBy.first` aggregation to a `object` dtype column that contains a `pd.NA` causes the method to fail with an exception: `TypeError: boolean value of NA is ambiguous`. Method works fine when using `np.nan` and also works as expected when the column is first converted to an `Int64` dtype column.
#### Expected Output
```
y
x
1 1
2 3
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_PT.UTF-8
pandas : 1.0.1
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 42.0.1
Cython : None
pytest : 5.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.1
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 : 5.2.3
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.10
tables : 3.6.1
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.46.0
```
</details>
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} | 4 | 2020-02-20T01:37:52Z | 2020-02-23T15:07:44Z | 2020-02-23T14:59:00Z | MEMBER | null | - [x] closes #32123
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This comparison `val == val` happens in a lot of these groupby operations but only seems to raise here in the presence of `NA`. Are we just always / mostly converting to numpy beforehand in the other cases? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32125 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32125/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32125/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32125/events | https://github.com/pandas-dev/pandas/issues/32125 | 568,134,474 | MDU6SXNzdWU1NjgxMzQ0NzQ= | 32,125 | Missing labels when applying style gives a KeyError | {
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"color": "006b... | closed | false | null | [] | null | 9 | 2020-02-20T08:33:47Z | 2021-02-10T20:30:51Z | 2021-02-10T20:30:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({"a": [1, 2, 3], "b":[4, 5, 6]})
df.style.bar(["a", "b", "c"])
```
#### Problem description
This issue occurs when passing a list of columns to `bar()` where one (or more) of the columns is not present in the dataframe. It will give a `KeyError`:
```
KeyError: 'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'
```
#### Expected Output
This error does not describe the problem. I would expect one of two possible outcomes:
1) An error message, which informs not to pass column names to `bar()` that are not present.
2) Fixing `_apply()` in `io/formats/style.py` to deal with missing labels.
I would prefer option 2, but I can image that woudl not be the preferred behavior of pandas :-).
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-17134-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.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 : 45.2.0.post20200209
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
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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https://api.github.com/repos/pandas-dev/pandas/issues/32126 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32126/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32126/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32126/events | https://github.com/pandas-dev/pandas/pull/32126 | 568,316,427 | MDExOlB1bGxSZXF1ZXN0Mzc3NzY4NTcy | 32,126 | BUG: Fix for convert_dtypes with mix of int and string | {
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} | 1 | 2020-02-20T13:52:45Z | 2020-02-21T14:49:05Z | 2020-02-21T14:48:51Z | CONTRIBUTOR | null | - [x] closes #32117
- [x] tests added / passed
- added new cases for `test_convert_dtypes`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- placed in v1.0.2 whatsnew
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https://api.github.com/repos/pandas-dev/pandas/issues/32127 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32127/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32127/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32127/events | https://github.com/pandas-dev/pandas/issues/32127 | 568,369,075 | MDU6SXNzdWU1NjgzNjkwNzU= | 32,127 | qcut not respecting precision values | {
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"description": "cut, qcut",
"id": 16... | open | false | null | [] | null | 2 | 2020-02-20T15:10:56Z | 2020-04-14T23:51:47Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
np.random.seed(42)
df = pd.DataFrame({"a": np.random.rand(20)})
for prec in range(1,10):
print(f"Prec: {prec}, first interval: {pd.qcut(df['a'], q=10, precision=prec).cat.categories[0]}")
# Prec: 1, first interval: (0.011000000000000001, 0.15]
# Prec: 2, first interval: (0.011000000000000001, 0.15]
# Prec: 3, first interval: (0.0196, 0.146]
# Prec: 4, first interval: (0.02048, 0.1462]
# Prec: 5, first interval: (0.020574000000000002, 0.1462]
# Prec: 6, first interval: (0.020583499999999998, 0.146203]
# Prec: 7, first interval: (0.02058439, 0.1462034]
# Prec: 8, first interval: (0.020584483999999997, 0.14620343]
# Prec: 9, first interval: (0.0205844933, 0.14620343]
```
#### Problem description
Example above shows inconsistent behaviour of `qcut` function when changing the `precision` argument. I can potentially understand why the first 2 values returned what they did (maybe it is not possible to create enough intervals with low precision, but then error/warning should be raised, shouldn't it?), but output of precisions 5, 6 and 8 is rather confusing and causes issues in e.g. printing or plotting using labels.
This is not happening for all intervals as far as I can see, just the first one, see precisions 6 and 7 output below:
```python
Prec: 6, first interval: IntervalIndex([(0.020583499999999998, 0.146203], (0.146203, 0.176664], (0.176664, 0.203659], (0.203659, 0.299037], (0.299037, 0.403243], (0.403243, 0.554317], (0.554317, 0.633202], (0.633202, 0.752084], (0.752084, 0.87463], (0.87463, 0.96991]],
closed='right',
dtype='interval[float64]')
Prec: 7, first interval: IntervalIndex([(0.02058439, 0.1462034], (0.1462034, 0.1766637], (0.1766637, 0.2036587], (0.2036587, 0.299037], (0.299037, 0.4032426], (0.4032426, 0.5543173], (0.5543173, 0.6332023], (0.6332023, 0.7520837], (0.7520837, 0.87463], (0.87463, 0.9699099]],
closed='right',
dtype='interval[float64]')
```
I have checked issues for "qcut precision", but none of the existing issues seems to cover this.
#### Expected Output
```python
# Prec: 1, first interval: (0.01, 0.2]
# Prec: 2, first interval: (0.011, 0.15]
# Prec: 3, first interval: (0.0196, 0.146]
# Prec: 4, first interval: (0.02048, 0.1462]
# Prec: 5, first interval: (0.020574, 0.1462]
# Prec: 6, first interval: (0.0205835, 0.146203]
# Prec: 7, first interval: (0.02058439, 0.1462034]
# Prec: 8, first interval: (0.020584484, 0.14620343]
# Prec: 9, first interval: (0.0205844933, 0.14620343]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.165-102.185.amzn1.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.12.0
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 : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.7
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/32128 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32128/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32128/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32128/events | https://github.com/pandas-dev/pandas/pull/32128 | 568,377,295 | MDExOlB1bGxSZXF1ZXN0Mzc3ODE4MzMz | 32,128 | CLN: auto-inherit specified Index method docstrings | {
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] | closed | false | null | [] | null | 3 | 2020-02-20T15:23:09Z | 2021-03-07T16:55:45Z | 2020-03-13T22:37:15Z | MEMBER | null | When working on indexing methods, I find myself grepping for the names, and having the extra Appender lines is a small-but-repeated nuisance.
In py36+ we can use `__init_subclass_` to automatically inherit docstrings without needing to repeat these Appenders.
This PR only does this for a few methods as a proof of concept, but we could expand this list quite a bit.
Based on a handful of measurements, no obvious impact on import time.
cc @WillAyd you were -1 on the proof of concept when it was on PandasObject and automatic for all methods; are you more amenable to this more finely-tuned approach? | {
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"de... | closed | false | null | [] | null | 2 | 2020-02-20T16:01:38Z | 2020-04-03T04:34:16Z | 2020-04-03T04:34:16Z | NONE | null | Hi, Would it be possible to implement pivot with a list of index ?
This will throw an error :
```python
df.pivot(index=['time', 'category'], columns='bar', values='baz')
```
But this does : (Found on Stack Overflow)
```python
def multiindex_pivot(df, index=None, columns=None, values=None):
if index is None:
names = list(df.index.names)
df = df.reset_index()
else:
names = index
list_index = df[names].values
tuples_index = [tuple(i) for i in list_index] # hashable
df = df.assign(tuples_index=tuples_index)
df = df.pivot(index="tuples_index", columns=columns, values=values)
tuples_index = df.index # reduced
index = pd.MultiIndex.from_tuples(tuples_index, names=names)
df.index = index
return df
df.pipe(multiindex_pivot, index=['time', 'category'], columns='bar', values='baz')
```
I just think it would be nice to have something directly available :)
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} | 1 | 2020-02-20T16:32:29Z | 2020-02-22T16:40:45Z | 2020-02-22T16:09:15Z | MEMBER | null | ```
In [2]: idx = pd.Index(range(1000)).insert(-1, 1)
In [3]: %timeit idx[:5]
2.84 µs ± 97.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
24.5 µs ± 292 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
In [4]: idx2 = idx.astype('uint64')
In [5]: %timeit idx2[5:]
2.99 µs ± 331 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
25.8 µs ± 957 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
```
Motivated by profiling in #32086 and seeing that _shallow_copy_with_infer was taking over a third of the runtime in the relevant benchmark. | {
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} | 1 | 2020-02-20T17:12:27Z | 2020-02-22T16:39:02Z | 2020-02-22T16:10:24Z | MEMBER | null | Make methods into classmethods where feasible, annotate in a couple of places.
Removes nonsense test (constructs an invalid NDFrame)
Preparatory for making a potential fastpath _from_mgr constructor.
`attrs` is never passed. Is there a plan to do so @TomAugspurger ? | {
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Description
What steps will reproduce the problem?
import pandas
Traceback (most recent call last):
File "", line 1, in
import pandas
File "C:\Users\tjash\Anaconda3\lib\site-packages\pandas_init_.py", line 54, in
from pandas.core.api import (
File "C:\Users\tjash\Anaconda3\lib\site-packages\pandas\core\api.py", line 29, in
from pandas.core.groupby import Grouper, NamedAgg
File "C:\Users\tjash\Anaconda3\lib\site-packages\pandas\core\groupby_init_.py", line 1, in
from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy
File "C:\Users\tjash\Anaconda3\lib\site-packages\pandas\core\groupby\generic.py", line 63, in
from pandas.core.groupby.groupby import (
File "C:\Users\tjash\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 61, in
from pandas.core.groupby import base, ops
File "C:\Users\tjash\Anaconda3\lib\site-packages\pandas\core\groupby\ops.py", line 16, in
import pandas._libs.reduction as libreduction
File "pandas_libs\reduction.pyx", line 30, in init pandas._libs.reduction
AttributeError: type object 'pandas._libs.reduction.Reducer' has no attribute 'reduce_cython'
Versions
Spyder version: 4.0.1
Python version: 3.7.3
Qt version: 5.9.6
PyQt5 version: 5.9.2
Operating System: Windows 10
Dependencies
atomicwrites >=1.2.0 : 1.3.0 (OK)
chardet >=2.0.0 : 3.0.4 (OK)
cloudpickle >=0.5.0 : 1.3.0 (OK)
diff_match_patch >=20181111 : 20181111 (OK)
intervaltree : None (OK)
IPython >=4.0 : 7.12.0 (OK)
jedi =0.14.1 : 0.14.1 (OK)
nbconvert >=4.0 : 5.6.1 (OK)
numpydoc >=0.6.0 : 0.9.2 (OK)
pexpect >=4.4.0 : 4.8.0 (OK)
pickleshare >=0.4 : 0.7.5 (OK)
psutil >=0.3 : 5.6.7 (OK)
pygments >=2.0 : 2.5.2 (OK)
pylint >=0.25 : 2.4.4 (OK)
pyls >=0.31.2;<0.32.0 : 0.31.7 (OK)
zmq >=17 : 18.1.1 (OK)
qdarkstyle >=2.7 : 2.8 (OK)
qtawesome >=0.5.7 : 0.6.1 (OK)
qtconsole >=4.6.0 : 4.6.0 (OK)
qtpy >=1.5.0 : 1.9.0 (OK)
rtree >=0.8.3 : 0.9.3 (OK)
sphinx >=0.6.6 : 2.4.0 (OK)
spyder_kernels >=1.8.1;<2.0.0: 1.8.1 (OK)
watchdog : None (OK)
cython >=0.21 : 0.29.15 (OK)
matplotlib >=2.0.0 : 3.1.3 (OK)
numpy >=1.7 : 1.18.1 (OK)
pandas >=0.13.1 : 1.0.1 (OK)
scipy >=0.17.0 : 1.4.1 (OK)
sympy >=0.7.3 : 1.5.1 (OK) | {
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} | 4 | 2020-02-20T17:39:09Z | 2020-02-22T16:33:51Z | 2020-02-22T16:33:47Z | CONTRIBUTOR | null | - [x] xref #32039
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry | {
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"... | open | false | null | [] | null | 1 | 2020-02-20T17:52:10Z | 2020-04-28T06:06:33Z | null | NONE | null | #### Code Sample
```
import pandas as pd
import numpy as np
from io import StringIO
TESTDATA1 = StringIO("""col1;col2
1;123
2;456
3;1582218195625938945
""")
TESTDATA2 = StringIO("""col1;col2
1;123
2;
3;1582218195625938945
""")
D1 = pd.read_csv(TESTDATA1, sep=";", dtype={'col2':'Int64'})
D2 = pd.read_csv(TESTDATA2, sep=";", dtype={'col2':'Int64'})
D1['col2']
D2['col2']
D1.loc[2,'col2'] == D2.loc[2,'col2']
```
Yields :
```
>>> D1['col2']
0 123
1 456
2 1582218195625938945
Name: col2, dtype: Int64
>>> D2['col2']
0 123
1 <NA>
2 1582218195625938944
Name: col2, dtype: Int64
>>> D1.loc[2,'col2'] == D2.loc[2,'col2']
False
```
#### Problem description
When pd.read_csv reads a column with Nullable Int64 data, and the column contains missing values (N/A), the precision on other values in that column is not maintained.
#### Expected Output
```
>>> D1['col2']
0 123
1 456
2 1582218195625938945
Name: col2, dtype: Int64
>>> D2['col2']
0 123
1 <NA>
2 1582218195625938945
Name: col2, dtype: Int64
>>> D1.loc[2,'col2'] == D2.loc[2,'col2']
True
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-3-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
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 : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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https://api.github.com/repos/pandas-dev/pandas/issues/32135 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32135/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32135/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32135/events | https://github.com/pandas-dev/pandas/issues/32135 | 568,511,110 | MDU6SXNzdWU1Njg1MTExMTA= | 32,135 | Indexing Code Roundup | {
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"de... | open | false | null | [] | null | 0 | 2020-02-20T19:13:31Z | 2021-07-29T04:25:47Z | null | MEMBER | null | xref #9595 for an overview the the indexing API, this is focused on the state of the code.
Needs Eyeballs
---------------
- Tests are scattered:
- tests/indexing/
- tests/(frame|series)/indexing/
- tests/indexes/*/indexing
- Moreover, many of the indexing tests are leftover from ix, and it isn't clear whether they are testing anything meaningful anymore. Organizing these tests and getting a handle on exactly what we are testing is going to be a marathon (cc @MomIsBestFriend)
- Many of our tests use the `indices` fixture or `tm.makeFooIndex`, which leaves many cases un-covered, off the top of my head:
- above/below _libs.index._SIZE_CUTOFF
- monotonic increasing/decreasing/non-monotonic
- has NAs or not
- near implementation bounds
- name attributes
- readonly or not
- view or not
- Ditto for indexing Benchmarks
- GH Issue Tracker
- The "Indexing" label is used pretty loosely. A thorough pass through the tracker to get a handle on what issues are about `__setitem__`, `__getitem__`, `loc`, `iloc`, `at`, and `iat` would be worthwhile.
_libs
----
- In `_libs.index` we define `_bin_search` as an alternative to `ndarray.searchsorted`. AFAICT this is more performant than ndarray.searchsorted for object dtype, but not for other dtypes. If this is correct, then we should override the non-object IndexEngine subclasses to use searchsorted (we do this for `DatetimeEngine`)
- The `IndexEngine` methods `get_indexer`, `get_pad_indexer`, `get_backfill_indexer` don't need to be in cython, can go back on the Index classes.
- Having them in cython makes it harder to tell that the `PeriodEngine` versions are never called
- We could avoid some casting if we had HashTable classes for itemsizes smaller than 64 bits
- I expect this would also let us avoid some casting in core.algorithms.
Potential Optimizations
-----------------------
Some of these increase code complexity, so it isn't obvious whether they are worthwhile:
- Separate Loc/iLoc/At/iAt classes for 1D vs 2D could allow some optimizations
- iloc input validations could be removed, letting numpy's exception messages surface instead
- cached check (or just separate subclass?) for if a object-dtype Index contains any tuples. If we can rule out tuples, a bunch of `loc` and `__getitem__` code can be simplified.
Potential Refactors
-------------------
- DatetimeIndex, TimedeltaIndex, and PeriodIndex `get_loc` could be refactored to move most of the action into `_maybe_cast_indexer`. I _think_ that the resulting `_maybe_cast_indexer` methods could then be shared with other DTA/TDA/PA methods, in particular the casting done in comparison methods. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32136 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32136/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32136/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32136/events | https://github.com/pandas-dev/pandas/issues/32136 | 568,551,451 | MDU6SXNzdWU1Njg1NTE0NTE= | 32,136 | groupby pd.Grouper with freq gives inconsistent groupings | {
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... | closed | false | null | [] | null | 4 | 2020-02-20T20:29:57Z | 2020-10-02T15:07:46Z | 2020-10-02T15:07:46Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
dates = pd.date_range(start='2020-02-01 09:30:00.01', periods=5, freq='14ms')
dates2 = pd.date_range(start='2020-02-01 11:00:00.01', periods=5, freq='8ms')
df = pd.DataFrame({'A': [1] * 10 + [2] * 10,
'B': np.concatenate((dates, dates2, dates, dates2)),
'C': np.arange(20) + 1})
df["B"] = pd.to_datetime(df["B"])
df['num_group'] = df.groupby(["A", pd.Grouper(key="B", freq="60ms")]).ngroup()
df
```
For the desired group of len=5, where A==1 and B about 9:30:
num_group==0 has length 4, num_group==1 has length 1
```python
df['num_group'] = df.groupby(["A", pd.Grouper(key="B", freq="61ms")]).ngroup()
```
num_group==0 has length 1, num_group==1 has length 4
```python
df['num_group'] = df.groupby(["A", pd.Grouper(key="B", freq="70ms")]).ngroup()
```
num_group==0 has length 3, num_group==1 has length 2
```python
df['num_group'] = df.groupby(["A", pd.Grouper(key="B", freq="72ms")]).ngroup()
```
num_group==0 has length 5 (expected output, this is all A==1 and B around 9:30)
```python
df['num_group'] = df.groupby(["A", pd.Grouper(key="B", freq="73ms")]).ngroup()
```
same split of the group as freq='60ms'
For freq greater than "100ms" (have not rigorously tested), appears to be the same (expected) as the 72ms freq case.
#### Problem description
Performing a groupby on a pd.Grouper with varying freqs returns differing ngroups() for some reason. As I change freq from 60 to 73ms, it splits what should be one group into subgroups of varying sizes.
Grouping on the pd.Grouper and then calling ngroup would be an nice way to perform secondary groupby-aggregations using the num_group as a key instead of a time window. If we have a sparse timeseries, then resample or rolling would 1) create additional unwanted rows or 2) condense the "B" time series column such that there isn't a unique key to merge back to the original dataframe.
Since the first five rows are from 09:30:00.010 to 09:30:00.066, I would not expect to have to use a freq >= 100ms to group these 56ms rows together, and the freqs cause unexpected groupings without explanation.
#### Expected Output
Expected output for all of the above should return num_group a series of [0]*5 + [1]*5 + [2]*5 + [3]*5, instead of varying divisions with max(num_group) == e.g. 7
### Show Versions
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.1
numpy : 1.15.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200209
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 2.2.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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} | 1 | 2020-02-20T21:47:02Z | 2020-02-21T18:34:34Z | 2020-02-21T18:34:11Z | MEMBER | null | AFAICT this gets rid of the last two places where we set `tm.N` externally. With those gone, this privatizes these variables in the hopes of not falling back into that pattern. | {
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} | 4 | 2020-02-20T22:19:56Z | 2020-02-26T01:41:32Z | 2020-02-26T01:37:31Z | MEMBER | null | - [x] closes #32120
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2020-02-21T02:13:55Z | 2020-02-21T18:35:15Z | 2020-02-21T18:35:15Z | NONE | null | ```python
findtime = pd.Timestamp('2019-12-12 10:19:25', tz='US/Eastern')
start = pd.Timestamp('2019-12-12 0:0:0', tz='US/Eastern')
end = pd.Timestamp('2019-12-13 0:0:0', tz='US/Eastern')
testindex = pd.date_range(start, end, freq='5s')
testindex.get_loc(findtime, method='nearest')
```
#### Problem description
With pandas 1.0.1, python 3.8.1
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Program Files\Python38\lib\site-packages\pandas\core\indexes\datetimes.py", line 699, in get_loc
return Index.get_loc(self, key, method, tolerance)
File "C:\Program Files\Python38\lib\site-packages\pandas\core\indexes\base.py", line 2649, in get_loc
indexer = self.get_indexer([key], method=method, tolerance=tolerance)
File "C:\Program Files\Python38\lib\site-packages\pandas\core\indexes\base.py", line 2740, in get_indexer
indexer = self._get_nearest_indexer(target, limit, tolerance)
File "C:\Program Files\Python38\lib\site-packages\pandas\core\indexes\base.py", line 2821, in _get_nearest_indexer
left_distances = abs(self.values[left_indexer] - target)
numpy.core._exceptions.UFuncTypeError: ufunc 'subtract' cannot use operands with types dtype('<M8[ns]') and dtype('O')
```
With 0.25.3, this returns index location 7433 as expected.
I've tested this on Windows 10 1909, and Ubuntu WSL, and get the same error on both.
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : AMD64 Family 23 Model 8 Stepping 2, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Canada.1252
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
``` | {
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} | 1 | 2020-02-21T02:42:26Z | 2020-02-22T16:34:04Z | 2020-02-22T16:12:43Z | MEMBER | null | The dtype keyword is akward, so this removes the one usage.
In turn this will allow us to simplify `_simple_new` and tighten up what it expects. Saved for separate PR. | {
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} | 6 | 2020-02-21T07:10:00Z | 2020-02-24T16:32:50Z | 2020-02-24T16:32:47Z | CONTRIBUTOR | null | - [x] closes #27553
- [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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} | 1 | 2020-02-21T08:16:09Z | 2020-02-26T05:50:43Z | 2020-02-26T05:50:36Z | CONTRIBUTOR | null | to understand tfrecords format requires quite much time and finally its details are not so needed in daily work with data. [pandas-tfrecords](https://pypi.org/project/pandas-tfrecords/) hides low-level details and provides easy human-usable api to convert pandas to tfrecords and restore back. | {
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} | 2 | 2020-02-21T09:14:50Z | 2020-03-18T11:02:50Z | 2020-03-16T16:55:37Z | MEMBER | null | https://travis-ci.org/pandas-dev/pandas/jobs/653274362?utm_medium=notification&utm_source=github_status
cc @jorisvandenbossche
```
=================================== FAILURES ===================================
________________________________ test_geopandas ________________________________
[gw0] linux -- Python 3.6.10 /home/travis/miniconda3/envs/pandas-dev/bin/python
@pytest.mark.filterwarnings("ignore:can't resolve:ImportWarning")
def test_geopandas():
> geopandas = import_module("geopandas") # noqa
pandas/tests/test_downstream.py:112:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/test_downstream.py:20: in import_module
return importlib.import_module(name)
../../../miniconda3/envs/pandas-dev/lib/python3.6/importlib/__init__.py:126: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:994: in _gcd_import
???
<frozen importlib._bootstrap>:971: in _find_and_load
???
<frozen importlib._bootstrap>:955: in _find_and_load_unlocked
???
<frozen importlib._bootstrap>:665: in _load_unlocked
???
<frozen importlib._bootstrap_external>:678: in exec_module
???
<frozen importlib._bootstrap>:219: in _call_with_frames_removed
???
../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/geopandas/__init__.py:5: in <module>
from geopandas.io.file import read_file # noqa
../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/geopandas/io/file.py:6: in <module>
import fiona
../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/fiona/__init__.py:83: in <module>
from fiona.collection import BytesCollection, Collection
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
import logging
import os
import warnings
from fiona import compat, vfs
> from fiona.ogrext import Iterator, ItemsIterator, KeysIterator
E ImportError: libkea.so.1.4.7: cannot open shared object file: No such file or directory
../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/fiona/collection.py:9: ImportError
``` | {
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```python
>>> pd.test()
running: pytest --skip-slow --skip-network --skip-db /usr/local/lib/python3.6/dist-packages/pandas
ImportError while loading conftest '/usr/local/lib/python3.6/dist-packages/pandas/conftest.py'.
/usr/local/lib/python3.6/dist-packages/pandas/conftest.py:25: in <module>
timeout=hypothesis.unlimited,
E AttributeError: module 'hypothesis' has no attribute 'unlimited'
```
#### Problem description
Running ``pd.test()`` results in the above error
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
Python 3.6.9 (default, Nov 7 2019, 10:44:02)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.9.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-76-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: 5.3.5
pip: 20.0.2
setuptools: 41.2.0
Cython: None
numpy: 1.17.2
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2018.3
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.3.5
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
>>>
</details>
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} | 6 | 2020-02-21T10:06:03Z | 2020-03-09T15:42:17Z | 2020-03-09T15:42:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
# a series with booleans and nan
pd.Series([False,True,True,pd.NA]).value_counts(dropna=False)
True 2
False 1
True 1
dtype: int64
# check the actual index of the value_counts result
pd.Series([False,True,True,pd.NA]).value_counts(dropna=False).index
Index([True, False, nan], dtype='object')
# a similar Serie, with ints instead of nans, seems to work ok
pd.Series([0,1,1,pd.NA]).value_counts(dropna=False)
1.0 2
0.0 1
NaN 1
dtype: int64
```
#### Problem description
As shown in the example, the __repr__ of the value_counts result is apparently wrong when booleans are in the serie. It should report nan as a possible value, instead it maps it to True.
Note that this is limited to series with boolean. A similar example with ints works ok.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-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 : 45.2.0
Cython : None
pytest : 5.3.5
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 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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"description": "to_sql, read_sql, rea... | open | false | null | [] | null | 1 | 2020-02-21T10:35:32Z | 2020-06-25T13:25:09Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
import sqlalchemy as sa
from sqlalchemy import create_engine
engine = create_engine('sqlite:///:memory:')
data = pd.DataFrame({'id':[26,42,63],
'Date':['2012-10-18','2012-10-19', '2012-10-20'],
'Col_1':['X','Y','Z'],
'Col_2':[25.7,-12.4,5.73],
'Col_3':[True,False,True]})
data.to_sql('data', engine, if_exists='replace', index=False)
sa.MetaData().clear()
metadata = sa.MetaData(bind=engine)
data_table = sa.Table('data', metadata,
sa.Column('id', sa.Integer),
sa.Column('Date', sa.DateTime),
sa.Column('Col_1', sa.String),
sa.Column('Col_2', sa.Float),
sa.Column('Col_3', sa.Boolean)
)
pd.read_sql(sa.select([data_table]).where(data_table.c.Col_3 == True),engine)
#### Problem description
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py in <listcomp>(.0)
482 else:
483 # last ditch effort
--> 484 data = [tuple(x) for x in data]
485 return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
486
ValueError: Couldn't parse datetime string: '2012-10-18'
The original version "where(data_table.c.Col_3 is True)" do not work , so i change to "where(data_table.c.Col_3 == True)" .
When I change "sa.Column('Date', sa.DateTime)" to "sa.Column('Date', sa.Date)" , it work well.
I want to know what happen ? And Why ?
#### Expected Output
id | Date | Col_1 | Col_2 | Col_3
-- | -- | -- | -- | --
26 | 2012-10-18 | X | 25.70 | True
63 | 2012-10-20 | Z | 5.73 | True
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
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 : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
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} | 0 | 2020-02-21T14:52:34Z | 2020-02-21T16:07:24Z | 2020-02-21T16:07:24Z | NONE | null | Backport PR #32055: REGR: read_pickle fallback to encoding=latin_1 upon a UnicodeDecodeError | {
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} | 2 | 2020-02-21T14:56:29Z | 2020-02-21T19:40:51Z | 2020-02-21T18:36:10Z | MEMBER | null | - [ ] closes #31964
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/32156 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32156/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32156/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32156/events | https://github.com/pandas-dev/pandas/issues/32156 | 569,036,933 | MDU6SXNzdWU1NjkwMzY5MzM= | 32,156 | False positive for "nested renamer is not supported" error | {
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... | open | false | null | [] | null | 15 | 2020-02-21T15:53:54Z | 2021-07-29T04:28:01Z | null | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'A': [1, 1, 1, 2, 2], 'B': range(5), 'C': range(5)})
df.groupby('A').agg({'B': 'sum', 'G': 'min'}) # aggregate by a non existing column
```
produces
```
<ipython-input-5-f5ac34bf856f> in <module>
----> 1 df.groupby('A').agg({'B': 'sum', 'G': 'min'})
~/src/dgr00/.venv3/lib/python3.6/site-packages/pandas/core/groupby/generic.py in aggregate(self, func, *args, **kwargs)
938 func = _maybe_mangle_lambdas(func)
939
--> 940 result, how = self._aggregate(func, *args, **kwargs)
941 if how is None:
942 return result
~/src/dgr00/.venv3/lib/python3.6/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
364 obj.columns.intersection(keys)
365 ) != len(keys):
--> 366 raise SpecificationError("nested renamer is not supported")
367
368 from pandas.core.reshape.concat import concat
SpecificationError: nested renamer is not supported
```
#### Problem description
While groupby.agg() with a dictionary when renaming was deprecated in 1.0 (
https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.20.0.html#deprecate-groupby-agg-with-a-dictionary-when-renaming) the corresponding error message can also be obtained when aggregating by an non existing column which can lead to confusion.
#### Expected Output
Error saying that the column `G` does not exist.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
python : 3.6.4.final.0
OS : Linux
machine : x86_64
pandas : 1.0.1
</details>
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} | 2 | 2020-02-21T16:15:57Z | 2020-02-26T12:36:36Z | 2020-02-26T12:36:36Z | MEMBER | null | ```
dti = pd.date_range('2016-01-01', periods=10)
left, right = dti[:5], dti[5:]
joined = left.join(right, how="outer")
```
In master `joined.freq` comes back as None, while in 0.25.3 it was "D". There is some code in DatetimeIndex._wrap_joined_index that suggests to me this was intentional.
Can someone confirm this suspicion cc @jorisvandenbossche ? If so, I've got a branch that restores the old behavior, just need to add tests. | {
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} | 2 | 2020-02-21T16:52:19Z | 2020-02-23T19:29:02Z | 2020-02-23T15:16:22Z | CONTRIBUTOR | null | - [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/32159 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32159/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32159/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32159/events | https://github.com/pandas-dev/pandas/pull/32159 | 569,118,254 | MDExOlB1bGxSZXF1ZXN0Mzc4NDE3MzI4 | 32,159 | Backport PR #32121 on branch 1.0.x (REG: dont call func on empty input) | {
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} | 0 | 2020-02-21T18:38:44Z | 2020-02-21T19:25:50Z | 2020-02-21T19:25:50Z | NONE | null | Backport PR #32121: REG: dont call func on empty input | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32160 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32160/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32160/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32160/events | https://github.com/pandas-dev/pandas/pull/32160 | 569,145,230 | MDExOlB1bGxSZXF1ZXN0Mzc4NDM5OTg4 | 32,160 | Backport PR #32152 on branch 1.0.x (TST: add test for get_loc on tz-aware DatetimeIndex) | {
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} | 1 | 2020-02-21T19:37:32Z | 2020-02-29T13:34:06Z | 2020-02-21T21:12:58Z | MEMBER | null | xref #32152
@jbrockmendel with tests moving around, is this OK for the backport. i.e. in the same place as on master. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32161 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32161/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32161/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32161/events | https://github.com/pandas-dev/pandas/pull/32161 | 569,148,797 | MDExOlB1bGxSZXF1ZXN0Mzc4NDQyOTA1 | 32,161 | Backport PR #32155 on branch 1.0.x (TST: add test for DataFrame.reindex on nearest tz-aware DatetimeIndex) | {
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} | 0 | 2020-02-21T19:45:00Z | 2020-02-29T13:37:02Z | 2020-02-21T21:13:22Z | MEMBER | null | xref #32155
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https://api.github.com/repos/pandas-dev/pandas/issues/32162 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32162/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32162/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32162/events | https://github.com/pandas-dev/pandas/issues/32162 | 569,171,830 | MDU6SXNzdWU1NjkxNzE4MzA= | 32,162 | Multiple aggregation on Groupby with DatetimeIndex and Categorical column or chained Groupby and Resample with multiple aggregation functions | {
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... | open | false | null | [] | null | 7 | 2020-02-21T20:35:46Z | 2021-07-29T04:29:07Z | null | NONE | null | Given a categorical column and a `datetime` index, one can groupby and aggregate on either column, but one cannot groupby and aggregate on both.
Setup
-----
```python
import pandas as pd
joint = pd.DataFrame({"a": [0, 1, 2, 3, 0, 1, 2],
"cat": pd.Categorical(["a", "b", "c", "c", "b", "a", "a"])},
index=pd.date_range("2020-01-01", freq="7h", periods=7)
)
```
Case (A): `Groupby` and aggregate on `categorical` works
----------
```python
joint.groupby("cat").mean()
a
cat
a 1.0
b 0.5
c 2.5
```
Case (B): `Groupby` and aggregate on `DatetimeIndex` works
-----
```python
joint.groupby(pd.Grouper(freq="20h")).mean()
a
2020-01-01 00:00:00 1.000000
2020-01-01 20:00:00 1.333333
2020-01-02 16:00:00 2.000000
```
## Problem description
`Groupby` and aggregate on both fails
-------
```python
joint.groupby(["cat", pd.Grouper(freq="20h")]).mean()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-81-f936a2ab3c64> in <module>
----> 1 joint.groupby(["cat", pd.Grouper(freq="20h")]).mean()
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in mean(self, *args, **kwargs)
1230 nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
1231 return self._cython_agg_general(
-> 1232 "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
1233 )
1234
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
1004 how, alt=alt, numeric_only=numeric_only, min_count=min_count
1005 )
-> 1006 return self._wrap_agged_blocks(agg_blocks, items=agg_items)
1007
1008 def _cython_agg_blocks(
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _wrap_agged_blocks(self, blocks, items)
1753 result = result.T
1754
-> 1755 return self._reindex_output(result)._convert(datetime=True)
1756
1757 def _iterate_column_groupbys(self):
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _reindex_output(self, output, fill_value)
2488 levels_list = [ping.group_index for ping in groupings]
2489 index, _ = MultiIndex.from_product(
-> 2490 levels_list, names=self.grouper.names
2491 ).sortlevel()
2492
~/.conda/envs/test_env/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
~/.conda/envs/test_env/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
~/.conda/envs/test_env/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)
~/.conda/envs/test_env/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)
~/.conda/envs/test_env/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)
~/.conda/envs/test_env/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
~/.conda/envs/test_env/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))
~/.conda/envs/test_env/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)
```
Chaining groupby and resample works for a single aggregation functions
-----
```python
joint.groupby("cat").resample("20h").mean()
a
cat
a 2020-01-01 00:00:00 0
2020-01-01 20:00:00 1
2020-01-02 16:00:00 2
b 2020-01-01 00:00:00 1
2020-01-01 20:00:00 0
c 2020-01-01 00:00:00 2
2020-01-01 20:00:00 3
```
But fails with a `RecursionError` for multiple functions using `.agg`
-----
```python
joint.groupby("cat").resample("20h").agg(["mean", "std"])
---------------------------------------------------------------------------
RecursionError Traceback (most recent call last)
<ipython-input-123-82216bdd4daa> in <module>
----> 1 joint.groupby("cat").resample("20h").agg(["mean", "std"])
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/resample.py in aggregate(self, func, *args, **kwargs)
279
280 self._set_binner()
--> 281 result, how = self._aggregate(func, *args, **kwargs)
282 if result is None:
283 how = func
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
484 elif is_list_like(arg):
485 # we require a list, but not an 'str'
--> 486 return self._aggregate_multiple_funcs(arg, _axis=_axis), None
487 else:
488 result = None
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _axis)
530 colg = self._gotitem(col, ndim=1, subset=obj.iloc[:, index])
531 try:
--> 532 new_res = colg.aggregate(arg)
533 except (TypeError, DataError):
534 pass
... last 3 frames repeated, from the frame below ...
~/.conda/envs/test_env/lib/python3.7/site-packages/pandas/core/resample.py in aggregate(self, func, *args, **kwargs)
279
280 self._set_binner()
--> 281 result, how = self._aggregate(func, *args, **kwargs)
282 if result is None:
283 how = func
RecursionError: maximum recursion depth exceeded while calling a Python object
```
even though the output should still be
```python
a
cat
a 2020-01-01 00:00:00 0
2020-01-01 20:00:00 1
2020-01-02 16:00:00 2
b 2020-01-01 00:00:00 1
2020-01-01 20:00:00 0
c 2020-01-01 00:00:00 2
2020-01-01 20:00:00 3
```
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.3.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 8 | 2020-02-21T20:39:06Z | 2020-03-19T21:31:19Z | 2020-03-19T21:29:19Z | MEMBER | null | Using Clang and latest numpy locally looks like we can use `-Werror`. WIP as I think there may still be things with gcc and older numpy versions to sort out
Note that there is also a warning emitted by cython for unreachable code in groupby, but -Werror wouldn't affect the "cythonization" step.
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} | 9 | 2020-02-21T21:21:34Z | 2020-02-25T01:52:38Z | 2020-02-25T01:52:33Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 1 | 2020-02-21T23:50:14Z | 2020-02-22T09:57:57Z | 2020-02-22T09:57:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
print( pd.__version__ ) # 1.0.1
df = pd.DataFrame( [[1, 0], [2, np.nan], [3, np.nan]], columns=['a', 'b'] )
print( df )
# a b
# 0 1 0.0
# 1 2 NaN
# 2 3 NaN
unused = df.groupby('a').agg({'b': 'nunique'}) # this shouldn't change anything in df, right?
print( df ) # prints out
# a b
# 0 1 0.000000e+00
# 1 2 -9.223372e+18
# 2 3 -9.223372e+18
# what happened to my nans?
```
#### Problem description
For some reason the agg, groupby operation is changing the orginal nan values in the b column
to a special float value.
In the previous version of pandas I was using, this didn't happen.
### Expected Output
The expected output of the print should the same as the one of the first print as groupby + agg should never change the input df (right?)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-70-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : sv_SE.UTF-8
LOCALE : sv_SE.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
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 : 2.11.1
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 : 1.0.6
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 9 | 2020-02-22T00:09:16Z | 2020-02-27T15:34:46Z | 2020-02-26T12:36:36Z | MEMBER | null | - [x] closes #32157
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
also just a nice cleanup, hoping to get this into ExtensionIndex before long | {
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} | 4 | 2020-02-22T00:21:01Z | 2020-02-23T17:12:53Z | 2020-02-23T15:50:38Z | MEMBER | null | - [ ] closes #xxxx
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In master we get a `CategoricalIndex` back.
Fixing this turns out to be pre-requisite to fixing a bug in `CategoricalDtype._from_values_or_dtype`, which in turn is needed to simplify the CategoricalIndex constructors (xref #32141) | {
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xref #32167. Together with that, this will allow us to simplify the CategoricalIndex constructor. | {
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} | 1 | 2020-02-22T02:54:41Z | 2020-02-22T15:42:16Z | 2020-02-22T15:40:55Z | MEMBER | null | we get a little bit of parametrization out of the deal, not much | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2020-02-22T10:36:01Z | 2020-04-06T21:15:19Z | 2020-04-06T21:15:19Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df=DataFrame([1,2,3,4],[4,5,6,7], columns= [list('abcd'), list('cdef')])
```
#### Problem description
when creating DataFrame by list of list with multiIndex, raise ValueError. I'm new to pandas, I don't know why, but it seems like a bug...
`d=DataFrame([[1,2,3,4],[4,5,6,7]], columns= [list('abcd'), list('cdef')])`
```
Traceback (most recent call `last):
File "<ipython-input-70-b1022cf50f33>", line 1, in <module>
d=DataFrame([[1,2,3,4],[4,5,6,7]], columns= [list('abcd'), list('cdef')])
File "D:\Users\tangliu\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py", line 450, in __init__
arrays, columns = to_arrays(data, columns, dtype=dtype)
File "D:\Users\tangliu\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\internals\construction.py", line 464, in to_arrays
return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
File "D:\Users\tangliu\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\internals\construction.py", line 504, in _list_to_arrays
raise ValueError(e) from e
ValueError: 2 columns passed, passed data had 4 columns`
It's ok using numpy ndarray or something else
```
```
d=DataFrame([[1,2,3,4],[4,5,6,7]])
d
Out[72]:
0 1 2 3
0 1 2 3 4
1 4 5 6 7
d.columns=[list('abcd'),list('cdef')]
d
Out[74]:
a b c d
c d e f
0 1 2 3 4
1 4 5 6 7
```
it's ok when using numpy's ndarray
```
d=DataFrame(np.random.randn(6,4), columns=[list('abcd'), list('cdef')])
d
Out[81]:
a b c d
c d e f
0 -0.066216 -1.264157 -1.199212 -0.184394
1 0.413666 -0.310750 1.138304 -0.271516
2 0.263854 0.827858 0.652344 -0.024369
3 0.041060 -2.235779 0.707691 0.319255
4 -0.118407 -1.278394 0.398972 0.597036
5 0.048947 0.697497 2.168712 0.650022
``` | {
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} | 0 | 2020-02-22T10:46:45Z | 2021-09-23T16:14:14Z | null | NONE | null | Hi there,
it seems that something changed in python 3.8 that makes `pandas.Timestamp.utctimetuple()` raise a `TypeError` instead of producing a `time.struct_time`.
#### Code Sample
```python
In [1]: date = pd.to_datetime("2000-01-01", utc=True)
In [2]: date.utctimetuple()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-17-e863c650e721> in <module>
----> 1 date.utctimetuple()
pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()
TypeError: an integer is required
```
#### Expected Output
With earlier Python versions the output is:
```python
In [3]: date.utctimetuple()
Out[3]: time.struct_time(tm_year=2000, tm_mon=1, tm_mday=1, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=5, tm_yday=1, tm_isdst=0)
```
An intermediate workaround can be to convert to `datetime.datetime` (python 3.8):
```python
In [4]: date.to_pydatetime().utctimetuple()
Out[4]: time.struct_time(tm_year=2000, tm_mon=1, tm_mday=1, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=5, tm_yday=1, tm_isdst=0)
```
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1066-oem
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C
LOCALE : en_GB.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : 5.3.5
hypothesis : 5.5.1
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.15.0
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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just reopening #31973 for now - this doesn't special-case NaT. Will think if there's a better way to do it, feel free to close / take over if it's wrong | {
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} | 3 | 2020-02-22T17:54:49Z | 2020-03-03T01:22:10Z | 2020-03-03T01:22:02Z | CONTRIBUTOR | null | - [x] xref #28792
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2020-02-22T17:54:57Z | 2020-02-28T14:40:59Z | 2020-02-28T14:40:32Z | CONTRIBUTOR | null | This is a follow-up on [this review comment by jbrockmendel](https://github.com/pandas-dev/pandas/pull/32046#discussion_r382697823).
There's a `sort` fixture in `tests.indexes.interval.test_setops`, which could be used in other places as well. For instance in `test_difference_base` in `pandas/tests/indexes/common.py`.
There are probably many more places where it could be used.
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} | 1 | 2020-02-22T18:16:10Z | 2020-02-22T20:59:11Z | 2020-02-22T20:59:05Z | CONTRIBUTOR | null | I've seen quite a few test files that only use a class for historic reasons. As functions are easier to comprehend I think it makes sense to move in this direction.
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2020-02-22T21:07:32Z | 2020-03-09T17:45:22Z | 2020-03-03T01:50:02Z | MEMBER | null | The main motivation for this is that it is prerequisite for implementing frame-with-frame ops blockwise. It also fixes a few broken cases:
- dta - dta.astype(object) currently raises TypeError
- ditto dti - dti.astype(object)
- tda + dta.astype(object) returns ndarray of timestamps instead of DatetimeArray
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"description": "Req... | closed | false | null | [] | null | 2 | 2020-02-22T21:49:24Z | 2020-02-25T19:51:33Z | 2020-02-25T19:51:33Z | MEMBER | null | While working on a PR, find out a confusion when level names are integer/None, e.g.
```python
>>> mi = pd.MultiIndex.from_frame(pd.DataFrame([[0.5,1],[0.5,0], [0.6, 0], [0.6,1]]), names=[None, 0])
>>> mi
MultiIndex([(0.5, 1),
(0.5, 0),
(0.6, 0),
(0.6, 1)],
)
>>> mi.get_level_values(0)
Int64Index([1, 0, 0, 1], dtype='int64', name=0)
>>> mi.set_levels([0.2, 0.3], level=0)
MultiIndex([(0.5, 0.3),
(0.5, 0.2),
(0.6, 0.2),
(0.6, 0.3)],
)
```
Pandas has quite big tolerrance of names setting, however, if names is set by int, and happens to be valid position, then using `set_levels` to change level value could be confusing, and also using level name to only change level values for `None` becomes difficult, because by default, `None` should be used to set values for all levels, the same happens if names is `[0, 1]`, e.g.
```python
>>> mi = pd.MultiIndex.from_frame(pd.DataFrame([[0.5,1],[0.5,0], [0.6, 0], [0.6,1]]), names=[1, 0])
>>> mi
MultiIndex([(0.5, 1),
(0.5, 0),
(0.6, 0),
(0.6, 1)],
names=[1, 0])
>>> mi.set_levels([0.2,0.3], level=0)
MultiIndex([(0.5, 0.3),
(0.5, 0.2),
(0.6, 0.2),
(0.6, 0.3)],
)
```
And yes, these are really corner cases, but might be nice to resolve to let users explicitly set/get level values! I am not sure what is the best way to figure it out, I am thinking of something like: `get_level_values_by_name(0)` or `get_level_values_by_loc(0)`, or have an argument in `get_level_values`, e.g. `get_level_values(0, by_name=True)`, default is `False` which uses loc to get values.
All feedbacks are very welcome, if there is kind of consensus, i would like to work on it!
@jreback @WillAyd @TomAugspurger @jorisvandenbossche
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} | 1 | 2020-02-22T22:50:16Z | 2020-02-23T01:01:03Z | 2020-02-23T01:00:57Z | CONTRIBUTOR | null | Issue #29547
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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] | closed | false | null | [] | null | 4 | 2020-02-22T23:29:40Z | 2020-03-25T07:41:56Z | 2020-02-24T17:08:40Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
"toy": [np.nan, 'Batmobile', 'Bullwhip'],
"born": [pd.NaT, pd.Timestamp("1940-04-25"),
pd.NaT]})
#df
# born name toy
#0 NaT Alfred NaN
#1 1940-04-25 Batman Batmobile
#2 NaT Catwoman Bullwhip
df.dropna(inplace=True)
#df
# born name toy
#1 1940-04-25 Batman Batmobile
# A.
dat_df = pd.DataFrame(data=np.zeros(1), columns=['Score'])
dat_df['SID'] = df['name']
#dat_df
# Score SID
#0 0.0 NaN
# B.
dat_df = pd.DataFrame(data=np.zeros(1), columns=['Score'])
dat_df['SID'] = df['name'].values
#dat_df
# Score SID
#0 0.0 Batman
```
#### Problem description
Have a look at the code in block A and B. In A, df['name'] was used, while in B, df['name'].values was used. Apperantly, output of A was not correct, where it seems that the first term in the original df (before applying dropna()) was assign to the new dat_df.
I tested this issue with version 0.18.1, 0.25.1, and 1.0.1.
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```
In file included from pandas/_libs/src/ujson/python/date_conversions.h:7:0,
from pandas/_libs/src/ujson/python/date_conversions.c:4:
/home/vsts/miniconda3/envs/pandas-dev/include/python3.7m/datetime.h:204:25: warning: ‘PyDateTimeAPI’ defined but not used [-Wunused-variable]
static PyDateTime_CAPI *PyDateTimeAPI = NULL;
```
https://github.com/pandas-dev/pandas/pull/32163#issuecomment-590010308 (will check CI)
Still same warning coming from np_datetime and np_datetime_strings
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} | 3 | 2020-02-23T05:30:11Z | 2020-05-06T12:49:41Z | 2020-04-07T10:04:15Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
x = np.array([0,1,2,3,4,5], dtype=np.uint8) # some data
y = np.array([0,1,2,3,4,5], dtype=np.int32) # some other data
g = ['foo', 'bar', 'baz', 'foo', 'bar', 'baz'] # grouping column
df = pd.DataFrame({'x': x, 'y': y, 'g': g}) # construct dataframe from np arrays preserving dtypes
# dtypes maintained through a groupby agg as expected
df.groupby('g').agg('first').dtypes
# Out[7]:
# x uint8
# y int32
# dtype: object
# dtypes maintained through a groupby agg as expected when agg_funcs is dict
df.groupby('g').agg({'x':'first', 'y':'first'}).dtypes
# Out[8]:
# x uint8
# y int32
# dtype: object
# Use new extension dtypes
df = pd.DataFrame({'x': pd.array(x, copy=False), 'y': pd.array(y, copy=False), 'g': g})
# dtypes maintained through a groupby agg as expected when agg_funcs is a scalar
df.groupby('g').agg('first').dtypes
# Out[10]:
# x UInt8
# y Int32
# dtype: object
# dtypes are not maintained through a groupby agg when agg_funcs is a dict and column dtypes are extension dtypes
df.groupby('g').agg({'x':'first', 'y':'first'}).dtypes
# Out[11]:
# x float64
# y float64
# dtype: object
```
#### Problem description
When using Extension dtypes, StringDtype, BooleanDtype, Int64Dtype, Int32Dtype, etc., in a DataFrame, the dtypes are modified to float64 when performing a `groupby.agg` with a dict defining the reducing function per column. This change in dtypes doesn't occur with numpy dtypes, and it doesn't occur when using a single agg func as scalar instead of the dict.
#### Expected Output
```python
df.groupby('g').agg({'x':'first', 'y':'first'}).dtypes
# Out[11]:
# x UInt8
# y Int32
# dtype: object
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.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 : 1.0.1
numpy : 1.14.0
pytz : 2018.7
dateutil : 2.7.5
pip : 18.1
setuptools : 40.6.2
Cython : 0.29.1
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.3.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.1.3
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.36.2
</details>
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] | closed | false | null | [] | null | 3 | 2020-02-23T06:12:58Z | 2020-02-24T19:26:28Z | 2020-02-24T19:26:27Z | NONE | null | Pls provide your directory structure, then try:
```
import os
os.getcwd()
# your current working directory will be displayed
os.path.exists('Department_of_Recreation_and_Parks__Facility_and_Park_Information.csv')
# Must be True, otherwise it is unrelated to pandas
```
Not sure why you're caring `matplotlib`. If you trying a script taken from somewhere, you can attach the link.
_Originally posted by @sinhrks in https://github.com/pandas-dev/pandas/issues/10457#issuecomment-116166868_ | {
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"default"... | closed | false | null | [] | null | 6 | 2020-02-23T07:26:48Z | 2020-03-22T20:33:47Z | 2020-03-22T20:33:47Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from scipy.sparse import csr_matrix
mil = 1000000
big_csr_diag_1s = csr_matrix((mil, mil), dtype="float")
# Following line takes around 15 seconds to run
big_csr_diag_1s.setdiag(1)
# At this point, big_csr_diag_1s is just a completely-sparse matrix with the only
# nonzero values being values of 1 on its diagonal (and there are 1 million of
# these values; I don't think this should be *too* bad to store in a sparse data
# structure).
# The following line runs for at least 5 minutes (I killed it after that point):
pd.DataFrame.sparse.from_spmatrix(big_csr_diag_1s)
```
#### Problem description
It seems like the [scipy csr matrix](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html) is being converted to dense somewhere in `pd.DataFrame.sparse.from_spmatrix()`, which results in that function taking a large amount of time (on my laptop, at least).
I _think_ this seems indicative of an efficiency problem, but if constructing the sparse DataFrame in this way really is expected to take a huge amount of time then I can close this issue. Thanks!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-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 : 45.2.0.post20200210
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.12.0
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 : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 13 | 2020-02-23T10:31:51Z | 2020-05-18T22:53:40Z | 2020-05-18T22:53:35Z | MEMBER | null | - [ ] xref #30928
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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`df.loc[df.id==123, 'num'].fillna(0, inplace=True)` doesn't work ,
but `df.loc[df.id==123, 'num'] = 123` works
why not edit the **fillna** function to adapt it in the future.
It seems like a bug.
_Originally posted by @shuiyuejihua in https://github.com/pandas-dev/pandas/issues/14858#issuecomment-428979311_
Hi, I met with the same problem. Now that `df.loc[df.id==123, 'num'] = 123` works, which means the operation takes effect on original `df`, why fillna(inplace=True) doesn't work? This is really wired , and seems haven't been fixed. My pandas version is `0.25.3`.
Any comment or explaination are welcome, thanks! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32205/events | https://github.com/pandas-dev/pandas/issues/32205 | 569,531,435 | MDU6SXNzdWU1Njk1MzE0MzU= | 32,205 | Simplify test_value_counts_unique_nunique_null in pandas/tests/base/test_ops.py | {
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For more info see the mentioned PR and the unresolved comments in the bottom.
The other tests in this file have a similar problem, but should probably be a separate PR. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32206/events | https://github.com/pandas-dev/pandas/issues/32206 | 569,540,193 | MDU6SXNzdWU1Njk1NDAxOTM= | 32,206 | Clean up null checking | {
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"description": "np.nan, ... | open | false | null | [] | null | 0 | 2020-02-23T18:51:58Z | 2020-05-07T03:55:20Z | null | MEMBER | null | From the discussion in https://github.com/pandas-dev/pandas/pull/31939 it was pointed out that there are places where we check for nullness in an unnecessarily verbose way, e.g.: https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/hashtable_class_helper.pxi.in#L1036.
Ideally we should just be able to use `checknull` from `pandas/_libs/missing.pyx`, but would need to make sure this catches all cases, such as `decimal.Decimal("nan")` (which I think isn't included at the moment). | {
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} | 8 | 2020-02-23T19:16:43Z | 2020-04-06T23:26:48Z | 2020-04-06T23:26:48Z | NONE | null | #### Code Sample
Create a new spreadsheet with 1 column "testcol" in LibreOffice / OpenOffice & Excel, save as `test.ods`/`test.xlsx`:
```
testcol
this is great
4 spaces
1 trailing
1 leading
2 spaces multiple times
```
For simplicity here as zip: (1 ods, 1 xlsx): [spreadsheets.zip](https://github.com/pandas-dev/pandas/files/4242180/spreadsheets.zip)
```python
import pandas as pd
for i in pd.read_excel('test.ods', engine='odf')['testcol']:
print(repr(i))
# output:
'this is great'
'4 spaces'
'1 trailing '
'1 leading'
'2 spaces multiple times'
for i in pd.read_excel('test.xlsx')['testcol']:
print(repr(i))
# output:
'this is great'
'4 spaces'
'1 trailing '
' 1 leading'
'2 spaces multiple times'
```
#### Problem description
When reading `.ods` files (OpenOffice or LibreOffice) multiple spaces are collapsed into one, leading ones are lost, trailing ones preserved.
#### Expected Output
see excel output above.
#### 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 : en_US.UTF-8
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 : 45.2.0
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
Not sure why that says `odfpy: None`, `pip list` tells me it's 1.4.1
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#### Problem description
The `pd.style.background_gradient` messes up the color order. That is it display wrongs colors that obviously don't conform the order of underlying numbers.
#### Code Sample, a copy-pastable example if possible
```python
import io
import pandas as pd
pd.read_csv(io.StringIO('''round,letter,source,findings,punctuation,see,reference
round 1,0.9853724610779391,0.9967806193222191,0.9857700345095259,0.966600495940578,0.98430677224734,0.572541233138048
round 2,0.9999946348522148,0.9995960833535309,0.994186634099717,0.987516415231207,0.9903793223872329,0.863724309623997
round 3,0.999963099705787,0.999681116612525,0.993544442544026,0.990503466426927,0.983378536954797,0.924636377166516
'''))
aucs.style.background_gradient(cmap='viridis_r').format("{:.2%}")
```
<img width="578" alt="Screen Shot 2020-02-23 at 13 09 22" src="https://user-images.githubusercontent.com/8426290/75120156-26f87680-563e-11ea-824f-1222be131eee.png">
Notice that 57% appears to be yellow, while on the left of it 98% is green (that is it suggest that it's a lower number).
#### Expected Output
This is how same table was plotted in seaborn. Find 10 differences ;)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.16.2
pytz : 2018.4
dateutil : 2.7.3
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.10
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 2.2.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.3.1
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.10
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2020-02-24T00:27:59Z | 2020-02-26T12:39:56Z | 2020-02-26T12:39:56Z | MEMBER | null | `to_datetime` raises when we pass in `pd.NA` but it should probably convert to `pd.NaT`, I think (or at least this would be better than an error)?
```python
In [1]: import pandas as pd
In [2]: pd.to_datetime([pd.NA])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-1af314749017> in <module>
----> 1 pd.to_datetime([pd.NA])
~/pandas/pandas/core/tools/datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
755 result = _convert_and_box_cache(arg, cache_array)
756 else:
--> 757 result = convert_listlike(arg, format)
758 else:
759 result = convert_listlike(np.array([arg]), format)[0]
~/pandas/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
447 errors=errors,
448 require_iso8601=require_iso8601,
--> 449 allow_object=True,
450 )
451
~/pandas/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1848 dayfirst=dayfirst,
1849 yearfirst=yearfirst,
-> 1850 require_iso8601=require_iso8601,
1851 )
1852 except ValueError as e:
~/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
479 @cython.wraparound(False)
480 @cython.boundscheck(False)
--> 481 cpdef array_to_datetime(ndarray[object] values, str errors='raise',
482 bint dayfirst=False, bint yearfirst=False,
483 object utc=None, bint require_iso8601=False):
~/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
701
702 except TypeError:
--> 703 return array_to_datetime_object(values, errors,
704 dayfirst, yearfirst)
705
~/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime_object()
839 else:
840 if is_raise:
--> 841 raise
842 return values, None
843 return oresult, None
~/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
674 iresult[i] = NPY_NAT
675 else:
--> 676 raise TypeError(f"{type(val)} is not convertible to datetime")
677
678 except OutOfBoundsDatetime:
TypeError: <class 'pandas._libs.missing.NAType'> is not convertible to datetime
In [3]: pd.__version__
Out[3]: '1.1.0.dev0+572.gaa6f241f5'
```
#### Expected Output
```
DatetimeIndex(['NaT'], dtype='datetime64[ns]', freq=None)
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} | 7 | 2020-02-24T00:30:05Z | 2020-02-26T21:25:40Z | 2020-02-26T12:39:57Z | MEMBER | null | - [x] closes #32213
- [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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} | 1 | 2020-02-24T01:05:07Z | 2020-02-26T02:14:14Z | 2020-02-26T02:13:16Z | MEMBER | null | There is a lot of de-duplication to be done, but pytest-based habits dont quite work here. So baby steps. | {
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