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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 0 | "2021-06-15T18:22:53Z" | "2021-06-16T01:46:04Z" | "2021-06-16T01:46:04Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```Python
with open("test.csv", "w") as cf:
cf.write("A,B,B\n1,1,1")
pandas.read_csv("test.csv", engine="python", dtype=str) # used to work, no longer works
pandas.read_csv("test.csv", engine="python", dtype="str") # used to work, no longer works
```
Now produces
```
File ".../pandas/io/parsers/python_parser.py", line 427, in _infer_columns
and self.dtype.get(old_col) is not None
AttributeError: 'str' object has no attribute 'get'
```
#### Problem description
Documentation for read_csv says that dtype may be a "type name or dict", but the new parser behavior demands a dict in this scenario.
I believe the bug was introduced in https://github.com/pandas-dev/pandas/commit/76792f10a32d157d47721731c5ce2a3192af73f2
#### Expected Output
A dataframe using str dtypes
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2dd9e9ba9009a40191c0c0b96262fa3939d609f0
python : 3.9.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Apr 12 20:57:45 PDT 2021; root:xnu-6153.141.28.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0rc1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 56.2.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.20
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-06-15T20:29:35Z" | "2021-10-03T13:49:09Z" | "2021-10-03T13:49:09Z" | MEMBER | null | `pandas/tests/io/test_fsspec.py::test_read_csv` and `pandas/tests/io/test_fsspec.py::test_markdown_options` fails with fsspec version 2021.6.0.
Based on the changelog, the culprit might be
> Better testing and folder handling for Memory (654)
but more investigation needed to see if this is a new bug or something we should fix in the failing tests
Temporary fix in #42023 | {
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} | 5 | "2021-06-15T21:26:44Z" | "2021-06-16T08:45:50Z" | "2021-06-16T01:46:05Z" | MEMBER | null | - [x] closes #42022
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
regression on master, so no whatsnew | {
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} | 1 | "2021-06-15T21:50:27Z" | "2021-07-28T01:54:30Z" | "2021-07-28T01:31:16Z" | MEMBER | null | Some low-hanging fruit
```
In [1]: import numpy as np
...: import pandas._libs.algos as libalgos
...:
...: np.random.seed(0)
...: inds = np.random.randint(0, 100, 500000)
...: ngroups = inds.max() + 1
In [2]: %timeit libalgos.groupsort_indexer(inds, ngroups)
```
`1.06 ms ± 11.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # this pr`
` 1.2 ms ± 39.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # master`
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Preliminary to a pretty nice de-duplication. | {
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- use of assert statements outside of test contexts; addressed by replacing with explicit value checks and exception raising
- improved type hinting; several instances where this was previously missing or incorrect, notably `DataFrame.merge` for which the correct use of strings in the `indicator` argument did not match the boolean hint
Thanks for your time, happy to discuss or revise any of these as you see fit if they aren't consistent with the contribution guidelines. | {
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} | 3 | "2021-06-16T02:12:25Z" | "2021-06-16T11:40:22Z" | "2021-06-16T11:09:06Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Ref: https://github.com/pandas-dev/pandas/pull/39935#discussion_r651989373
Doing e.g.
print(DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}).apply('sum'))
print(DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}).agg('sum'))
produce a Series with `name=None`. This was introduced in 1.3, so does not need a whatsnew. | {
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} | 5 | "2021-06-16T04:36:32Z" | "2021-06-18T15:44:18Z" | "2021-06-18T15:43:42Z" | CONTRIBUTOR | null | - [x] closes #41404
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
In this PR, I will try to solve issue #41404.
I would appreciate it if you could review this.
## About the issue
In the current `pandas.util.hash_pandas_object` function implementation, the hash_key argument is ignored when the input object type is a DataFrame.
This is a reproducible example.
```py
df = pd.DataFrame({"x": list("abc")})
new_hash_key = "a"*16
print("Series")
print(pd.util.hash_pandas_object(df["x"]).values)
print(pd.util.hash_pandas_object(df["x"], hash_key=new_hash_key).values)
print("DataFrame")
print(pd.util.hash_pandas_object(df).values)
print(pd.util.hash_pandas_object(df, hash_key=new_hash_key).values)
```
The output is here.
```
Series
[ 4578374827886788867 17338122309987883691 5473791562133574857]
[14372590553486244284 12017144478307119910 34790805896362026]
DataFrame
[ 4578374827886788867 17338122309987883691 5473791562133574857]
[ 4578374827886788867 17338122309987883691 5473791562133574857]
```
The last output should be
```
[14372590553486244284 12017144478307119910 34790805896362026]
```
## About the fix
When the input object is a DataFrame, the hash values are calculated by applying `hash_array` function for each Series.
However, optional arguments, including `hash_key`, are not passed to the hash_array function in the current implementation.
To fix this, I passed the optional arguments to the hash_array function. | {
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} | 5 | "2021-06-16T11:12:37Z" | "2021-06-17T06:22:02Z" | "2021-06-16T22:16:02Z" | CONTRIBUTOR | null | should be backported to 1.3.0 as it references the features add there.
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} | 0 | "2021-06-16T13:33:28Z" | "2021-06-28T13:24:28Z" | "2021-06-28T13:24:28Z" | CONTRIBUTOR | null | PR #40312 introduced jinja2 template inheritance for the styler HTML parsing.
Essentially we went from a structure like:
```
# main.template
{% block foobar %}
<html>
{% endblock %}
```
to
```
# main.template
{% include "sub.template" %}
# sub.template
{% block foobar %}
<html>
{% endblock %}
```
The problem with that is that Styler offers (and documents) the ability to subclass it and extend the main template so that defined blocks can be overwritten.
In jinja2 the `included` blocks of an extended template cannot be overwritten.
Hence a patch is to point the docs to extending the `sub` templates instead, and amending the `from_custom_template` method to amend each sub template instead of the main.
This fixes all functionality but is not backwards compatible becuase of the arg change. | {
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] | open | false | null | [] | null | 4 | "2021-06-16T13:43:33Z" | "2021-08-21T02:32:59Z" | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
I had to do several additional manual data cleaning steps to parse certain markdown tables using the read_table function. The most cumbersome was not having an option to strip trailing spaces between values and separators. See mock code example at the end of this description.
#### Describe the solution you'd like
I would like the read_table function to have a skiptrailingspace boolean parameter that would automatically strip trailing whitespace in the same way the skipinitialspace parameter enables stripping leading whitespace.
#### API breaking implications
It would involve adding a new named parameter to read_table - so if users were calling with positional arguments it would be a breaking change if it wasn't made the last argument to the function.
#### Describe alternatives you've considered
Manual data cleaning is tolerable but more onerous. Another alternative that would be ideal from my perspective would be a read_markdown function that could extract indexed tables from markdown text in the same way the read_html function does for html. I suppose a related alternative is to use another Python package to render the markdown then use read_html but that is also onerous.
#### Additional context
See code example below for my current manual work-around.
```python
import pandas as pd
import io
annoying_table = """
| Heading 1 | Heading 2 | Heading 3 |
| --------- | --------- | --------- |
| 3.14 | cat | 72 |
| 42 | mat | 87.3 |
| 1234.56 | rat | 128 |"""
df = (
pd.read_table(
filepath_or_buffer=io.StringIO(annoying_table),
sep="|",
skipinitialspace=True,
# Would like a skiptrailingspace=True too
skiprows=[2],
)
.convert_dtypes()
.iloc[:, 1:-1]
)
df.columns = df.columns.str.strip()
print(df)
print(df.loc[0,"Heading 2"]) # output "cat "
# Hacky work-around to strip trailing spaces
for column in df.select_dtypes("string").columns:
df[column] = df[column].str.strip()
print(df.loc[0,"Heading 2"]) # output "cat"
```
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https://api.github.com/repos/pandas-dev/pandas/issues/42055 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42055/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42055/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42055/events | https://github.com/pandas-dev/pandas/issues/42055 | 922,682,512 | MDU6SXNzdWU5MjI2ODI1MTI= | 42,055 | BUG: IndexError when slicing a series, if a level of MultiIndex contains only NaN values | {
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} | 2 | "2021-06-16T14:22:36Z" | "2021-07-15T04:24:14Z" | "2021-07-15T04:24:14Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series([0, 1], index=[[pd.NA, pd.NA], ["bar", "baz"]])
s.loc[pd.IndexSlice[:, "bar"]]
```
##### Traceback
```python-traceback
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-1-c6419722729f> in <module>
2
3 s = pd.Series([0, 1], index=[[pd.NA, pd.NA], ["bar", "baz"]])
----> 4 s.loc[pd.IndexSlice[:, "bar"]]
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexing.py in __getitem__(self, key)
887 # AttributeError for IntervalTree get_value
888 return self.obj._get_value(*key, takeable=self._takeable)
--> 889 return self._getitem_tuple(key)
890 else:
891 # we by definition only have the 0th axis
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexing.py in _getitem_tuple(self, tup)
1058 def _getitem_tuple(self, tup: Tuple):
1059 with suppress(IndexingError):
-> 1060 return self._getitem_lowerdim(tup)
1061
1062 # no multi-index, so validate all of the indexers
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexing.py in _getitem_lowerdim(self, tup)
789 # we may have a nested tuples indexer here
790 if self._is_nested_tuple_indexer(tup):
--> 791 return self._getitem_nested_tuple(tup)
792
793 # we maybe be using a tuple to represent multiple dimensions here
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexing.py in _getitem_nested_tuple(self, tup)
845 raise ValueError("Too many indices")
846 with suppress(IndexingError):
--> 847 return self._handle_lowerdim_multi_index_axis0(tup)
848
849 # this is a series with a multi-index specified a tuple of
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexing.py in _handle_lowerdim_multi_index_axis0(self, tup)
1078 try:
1079 # fast path for series or for tup devoid of slices
-> 1080 return self._get_label(tup, axis=axis)
1081 except (TypeError, InvalidIndexError):
1082 # slices are unhashable
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexing.py in _get_label(self, label, axis)
1071 def _get_label(self, label, axis: int):
1072 # GH#5667 this will fail if the label is not present in the axis.
-> 1073 return self.obj.xs(label, axis=axis)
1074
1075 def _handle_lowerdim_multi_index_axis0(self, tup: Tuple):
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/generic.py in xs(self, key, axis, level, drop_level)
3731 if isinstance(index, MultiIndex):
3732 try:
-> 3733 loc, new_index = index._get_loc_level(
3734 key, level=0, drop_level=drop_level
3735 )
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexes/multi.py in _get_loc_level(self, key, level, drop_level)
3064 indexer = slice(None, None)
3065 ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)]
-> 3066 return indexer, maybe_mi_droplevels(indexer, ilevels, drop_level)
3067 else:
3068 indexer = self._get_level_indexer(key, level=level)
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexes/multi.py in maybe_mi_droplevels(indexer, levels, drop_level)
2981 for i in sorted(levels, reverse=True):
2982 try:
-> 2983 new_index = new_index._drop_level_numbers([i])
2984 except ValueError:
2985
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexes/base.py in _drop_level_numbers(self, levnums)
1639 # set nan if needed
1640 mask = new_codes[0] == -1
-> 1641 result = new_levels[0].take(new_codes[0])
1642 if mask.any():
1643 result = result.putmask(mask, np.nan)
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/indexes/base.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
749 # Note: we discard fill_value and use self._na_value, only relevant
750 # in the case where allow_fill is True and fill_value is not None
--> 751 taken = algos.take(
752 self._values, indices, allow_fill=allow_fill, fill_value=self._na_value
753 )
~/.cache/pypoetry/virtualenvs/env-d6X_GUCN-py3.9/lib/python3.9/site-packages/pandas/core/algorithms.py in take(arr, indices, axis, allow_fill, fill_value)
1655 else:
1656 # NumPy style
-> 1657 result = arr.take(indices, axis=axis)
1658 return result
1659
IndexError: cannot do a non-empty take from an empty axes.
```
#### Problem description
The example runs as expected, when the index contains a non-NaN value such as "foo"
```python
import pandas as pd
s3 = pd.Series([0, 1], index=[[pd.NA, "foo"], ["bar", "baz"]])
s3[pd.IndexSlice[:, "bar"]]
```
#### Expected Output
Output identical to
`pd.Series(0, index=[pd.NA])`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.41-1-MANJARO
Version : #1 SMP PREEMPT Fri May 28 19:10:32 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
Cython : None
pytest : 6.2.4
hypothesis : 6.14.0
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Code:
```python
series = pd.Series([30, 10, 20], index=['c', 'a', 'b'])
order = series.argsort()
order
```
Output:
```
c 1
a 2
b 0
dtype: int64
```
#### Problem description
It appears that the argsort output str index order (c, a, b) remains the same as the input data, while the numeric indices (1, 2, 0) are referring to the input data in a different order. This means the str index no longer corresponds with the values (the numeric indices) in the resulting Series. This behavior is confusing, since it sort of looks like c would be the 1st element (0-indexed), a the 2nd, and b the 0th, which is incorrect.
There's actually another example of this here: https://www.geeksforgeeks.org/python-pandas-series-argsort/. The str indices in the result of the argsort don't give any useful information - only the order of the numeric indices is relevant.
#### Expected Output
I'm not sure if it would make more sense if str indices were reordered to match the numeric indices, like this:
```
a 1
b 2
c 0
dtype: int64
```
This might be nice, for example, to get the indices of the largest N elements: `order.index[-N:]`. The common workflow `series[series.argsort()]` still seems to work, but I'm not sure of all the other indexing implications.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.25.1.el7.x86_64
Version : #1 SMP Tue Apr 13 18:55:45 EDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 4 | "2021-06-16T21:42:31Z" | "2021-06-17T17:07:44Z" | "2021-06-17T13:26:23Z" | MEMBER | null | Broken off a branch working towards #13745
This doesn't have noticeable user-facing impact on its own since this is a smaller part of the merge operation. Some timings:
```
import numpy as np
import pandas._libs.join as libjoin
np.random.seed(0)
arr1 = np.random.randint(0, 100, 100000)
arr2 = np.random.randint(0, 100, 100000)
```
Master:
```
In [2]: %timeit libjoin.inner_join(arr1, arr2, 100)
1.19 s ± 17.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [3]: %timeit libjoin.left_outer_join(arr1, arr2, 100)
1.22 s ± 16.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [4]: %timeit libjoin.full_outer_join(arr1, arr2, 100)
1.26 s ± 33.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
This pr:
```
In [2]: %timeit libjoin.inner_join(arr1, arr2, 100)
729 ms ± 17.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [3]: %timeit libjoin.left_outer_join(arr1, arr2, 100)
714 ms ± 11 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [4]: %timeit libjoin.full_outer_join(arr1, arr2, 100)
715 ms ± 6.99 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
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Previously, any "Truthy" value other than 1 and True was used as the
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.zeros(1000))
df.iloc[0] = 1000
df.rolling(10).std()
Out[124]:
0
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
.. ...
995 0.000004
996 0.000004
997 0.000004
998 0.000004
999 0.000004
[1000 rows x 1 columns]
```
#### Problem description
If we have a relatively big number at the top, and the remaining is zeros, then the rolling result is not zero
#### Expected Output
```python
df.iloc[0] = 0
df.rolling(10).std()
Out[125]:
0
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
.. ...
995 0.0
996 0.0
997 0.0
998 0.0
999 0.0
[1000 rows x 1 columns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-53-generic
Version : #60-Ubuntu SMP Thu May 6 07:46:32 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.0
IPython : 7.23.1
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_pickle.html
#### Documentation problem
How to use storage_option param here to integrate with GCP bucket?
#### Suggested fix for documentation
Please add some examples to pandas.DataFrame.to_{any_file_format}.html
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} | 13 | "2021-06-17T11:07:52Z" | "2021-09-14T12:35:25Z" | "2021-09-14T12:35:18Z" | CONTRIBUTOR | null | - [x] closes #41047
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
In this PR, I try to fix issue #41047.
I would appreciate it if you could review this PR.
## About the issue
In the UTC format, "Z" is used as a special timezone "+00:00".
But current `pd.to_datetime` function can not recognize the last "Z" when the `infer_datetime_format` is `True`.
Note that `pd.to_datetime` can properly interpret "Z" as "+00:00" when the `infer_datetime_format` is unspecified.
The problem happens in the following codes of datetimes.py:
```py
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = format_is_iso(format)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
if format is not None:
res = _to_datetime_with_format(
arg, orig_arg, name, tz, format, exact, errors, infer_datetime_format
)
if res is not None:
return res
```
Here, the `_guess_datetime_format_for_array` returns a format string like '%Y-%m-%d %H:%M:%SZ' depending on the input strings.
The resulting format is estimated by `format_is_iso` function.
This result is used to judge whether the function specialized for iso format is used or not.
But current `format_is_iso` can not recognize the last "Z" in a format string, so it returns `False` if the format has "Z" regardless the format is valid iso8601 format or not.
Because of this misjudgment, the wrong function (`_to_datetime_with_format`) is used for the iso string, and the timestamp that does not have timezone information is returned.
## About the fix
I fixed the format_is_iso function to recognize the last "Z" in the format string to solve this problem. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42070 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42070/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42070/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42070/events | https://github.com/pandas-dev/pandas/issues/42070 | 923,841,994 | MDU6SXNzdWU5MjM4NDE5OTQ= | 42,070 | BUG: error writing sparse DataFrame to HDF | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
filepath = r"<path where the file should be stored>"
data = [1,2,None,None,None,3]
sparse_data = pd.arrays.SparseArray(data)
df = pd.DataFrame(dict(a=sparse_data))
df.to_hdf(filepath, "data", format="t", data_columns=True)
```
```python
>>> File "...\lib\site-packages\pandas\io\pytables.py", line 2323, in _get_atom
>>> itemsize = dtype.itemsize # type: ignore[attr-defined]
>>> AttributeError: 'SparseDtype' object has no attribute 'itemsize'
```
#### Problem description
the code-example tells it all... saving a sparse-DataFrame to HDF raises an error
#### Expected Output
a properly exported DataFrame
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en
LOCALE : German_Austria.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.06.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.17.0
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df_product = pd.read_excel("tmp/Presentaciones.xls")
```
#### Problem description
I got an error when I tried to open a product.xls with pd.read_excel ("NDC database file - Excel version (zip format)" downloaded from https://www.fda.gov/drugs/drug-approvals-and-databases/national-drug-code-directory), I tried different engine, but i always got an error
pandas version: 1.2.4
xlrd version: 2.0.1
openpyxl version: 3.0.7
#### Expected Output
#### Output of ``pd.read_excel("tmp/Presentaciones.xls")``
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/usr/local/lib/python3.9/site-packages/pandas/util/_decorators.py", line 299, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/pandas/io/excel/_base.py", line 336, in read_excel
io = ExcelFile(io, storage_options=storage_options, engine=engine)
File "/usr/local/lib/python3.9/site-packages/pandas/io/excel/_base.py", line 1071, in __init__
ext = inspect_excel_format(
File "/usr/local/lib/python3.9/site-packages/pandas/io/excel/_base.py", line 965, in inspect_excel_format
raise ValueError("File is not a recognized excel file")
ValueError: File is not a recognized excel file
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} | 3 | "2021-06-17T12:32:05Z" | "2021-06-18T05:09:13Z" | "2021-06-17T23:59:40Z" | CONTRIBUTOR | null | - [x] closes #29072
This allows you to quickly make the index and/or column headers stick in place when scrolling in HTML.
Additional args allow the control of subselecting specific multiindex levels and controlling the width /or height of the sticking elements.
https://user-images.githubusercontent.com/24256554/122395757-99017700-cf77-11eb-93f0-f3c401afca4e.mov
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} | 3 | "2021-06-17T13:49:20Z" | "2021-11-20T22:48:59Z" | null | MEMBER | null | #41962 enabled auto-cancel for GitHub Actions on both master and PRs. Ideally, we should only have this feature turned on for PRs.
Auto-cancel is achieved with the `concurrency` feature. It looks like this:
```yaml
concurrency:
group: ${{ github.ref }}-xxx
cancel-in-progress: true
```
The group attribute specifies a group name. So only one job can run for each group.
To disable it for master, we need to select the appropriate workflow/job variables from https://docs.github.com/en/actions/reference/context-and-expression-syntax-for-github-actions for the group name so that it's different for each commit to the master but identical for each PR.
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} | 3 | "2021-06-17T14:41:53Z" | "2021-06-18T05:13:57Z" | "2021-06-17T17:48:59Z" | CONTRIBUTOR | null | should be backported 1.3.0
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I see ``<stdin>:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.``. When you planed change default dtype for empty Series?
Thank you in advanced!
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
More of a cleanup than a performance improvement. Perf only seems to improve for multiindexes.
Will try to squeeze out more improvement.
Here are the asv's (run 3 times)
```
asv continuous -f 1.1 upstream/master HEAD -b ^io.csv.ToCSV
```
```
before after ratio
[5153334c] [a7a839bd]
<perf-to-csv~2^2> <perf-to-csv>
- 28.3±5ms 13.6±0.8ms 0.48 io.csv.ToCSVDatetime.time_frame_date_formatting
- 2.77±0.1s 1.10±0.01s 0.40 io.csv.ToCSVIndexes.time_head_of_multiindex
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
before after ratio
[5153334c] [a7a839bd]
<perf-to-csv~2^2> <perf-to-csv>
- 29.8±6ms 15.9±0.3ms 0.53 io.csv.ToCSVDatetime.time_frame_date_formatting
- 2.51±0.2s 1.25±0.06s 0.50 io.csv.ToCSVIndexes.time_head_of_multiindex
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
before after ratio
[5153334c] [a7a839bd]
<perf-to-csv~2^2> <perf-to-csv>
- 934±30ms 767±30ms 0.82 io.csv.ToCSVIndexes.time_multiindex
- 3.49±0.08s 1.08±0.03s 0.31 io.csv.ToCSVIndexes.time_head_of_multiindex
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 7 | "2021-06-17T19:21:57Z" | "2021-06-21T17:01:22Z" | "2021-06-21T13:08:37Z" | MEMBER | null | - [ ] closes #41797
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | "2021-06-17T19:50:51Z" | "2021-06-17T21:34:32Z" | "2021-06-17T21:34:32Z" | MEMBER | null | Seeing
```
~/work/pandas/pandas/pandas/core/indexes/base.py in get_indexer(self, target, method, limit, tolerance)
106
3411 # potential fastpath
107
3412 # get an indexer for unique categories then propagate to codes via take_nd
108
-> 3413 categories_indexer = self.get_indexer(target.categories)
109
3414 indexer = algos.take_nd(categories_indexer, target.codes, fill_value=-1)
110
3415
111
112
RecursionError: maximum recursion depth exceeded while calling a Python object
113
<<<-------------------------------------------------------------------------
```
along with a bunch of other categorical related failures. Guessing cause may be #41934, cc @jbrockmendel | {
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} | 5 | "2021-06-17T20:38:12Z" | "2021-06-26T08:47:20Z" | "2021-06-17T21:34:32Z" | MEMBER | null | - [x] closes #42088
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] closes #12694
rendered whatsnew:
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} | 4 | "2021-06-17T21:15:30Z" | "2021-06-28T23:11:15Z" | "2021-06-28T13:24:29Z" | CONTRIBUTOR | null | - [x] closes #42053
More info there - note that this makes the broken functionality re-available but is not backwards compatible due to inherited jinja2 templates.
The documentation is updated for the correct application.
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} | 8 | "2021-06-17T21:28:55Z" | "2021-07-19T15:23:35Z" | "2021-07-15T04:18:39Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df1 = pd.DataFrame(pd.Series([True, False, True, True], dtype='bool'))
df2 = pd.DataFrame(pd.Series([1,0,1], dtype='int64'))
new_df = pd.concat([df1,df2]) # dtype changed from int64 to object
assert new_df[0].dtype == 'object'
```
#### Problem description
Previously, the `concat` call would produce a series with dtype `int64`, converting the bools to 1s and 0s. Now, all the values are maintained, so the dtype is `object`.
#### Expected Output
I'm not sure if this behavior change is expected or not, but I didn't see anything in the release notes that made me think it was expected, so I'd expect the result to allow `assert new_df[0].dtype == 'int64'`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2dd9e9ba9009a40191c0c0b96262fa3939d609f0
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0rc1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.06.0
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 3 | "2021-06-18T00:16:55Z" | "2021-06-18T04:49:57Z" | "2021-06-18T01:49:32Z" | MEMBER | null | ```
import numpy as np
import pandas._libs.lib as lib
np.random.seed(0)
inds = np.random.randint(0, 100, 1000000)
ngroups = inds.max() + 1
%timeit lib.generate_slices(inds, ngroups)
```
Master:
`1.86 ms ± 44 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)`
This pr:
`859 µs ± 127 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)`
Adding the `cython` decorators makes a huge difference here
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} | 2 | "2021-06-18T02:44:56Z" | "2021-06-21T18:28:18Z" | "2021-06-21T18:28:18Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas (1.3.0rc1).
- [x] (optional) I have confirmed this bug exists on the master branch of pandas
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = pd.array([0, 1, 2, 3], dtype="Int32")
df = expected = pd.DataFrame({"data": pd.Series(data)})
result = pd.DataFrame(index=df.index)
result.loc[df.index, "data"] = df["data"]
print(df["data"].dtype) # prints: Int32
print(result["data"].dtype) # prints: float64 <--
```
#### Problem description
In my mind, this behavior seems unexpected because the provided `dtype` should be preserved and not coerced to the default type for an empty `Series`. This occurs for the nullable integer dtypes as well as `Float32`/`Float64`.
I came across this when trying to implement an ExtensionDtype that ended up failing on `BaseSetitemTest. test_setitem_with_expansion_dataframe_column`: https://github.com/pandas-dev/pandas/blob/648eb40abc53bcbdb2a6a1e68fd309c3d8021f8a/pandas/tests/extension/base/setitem.py#L335-L343
Interestingly, in the tests for `IntegerArray` and `FloatingArray`, the test data includes `NaN` values which do not result in the coercion to `float64`:
```python
import pandas as pd
data = pd.array([0, pd.NaT, 2, 3], dtype="Int32")
df = expected = pd.DataFrame({"data": pd.Series(data)})
result = pd.DataFrame(index=df.index)
result.loc[df.index, "data"] = df["data"]
print(df["data"].dtype) # prints: Int32
print(result["data"].dtype) # prints: Int32 <--
```
My expectation was that the `dtype` should be preserved in such cases, with/without `NaN` values.
#### Expected Output
I would expect that the dtype of the `pd.Series` being added to `result` would be preserved, in the case of the minimal example, `result["data"]` should be `Int32Dtype`.
```python
print(df["data"].dtype) # prints: Int32
print(result["data"].dtype) # prints: Int32 <--
```
#### Output of ``pd.show_versions()``
This was generated from the latest release candidate, but it appears to also occur on the master branch (1.4.0.dev0+56.g648eb40abc)
<details>
INSTALLED VERSIONS
------------------
commit : 2dd9e9ba9009a40191c0c0b96262fa3939d609f0
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
Version : Darwin Kernel Version 17.7.0: Fri Oct 30 13:34:27 PDT 2020; root:xnu-4570.71.82.8~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0rc1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.05.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 9 | "2021-06-18T08:06:17Z" | "2021-09-04T18:57:08Z" | "2021-08-03T17:15:14Z" | CONTRIBUTOR | null | Whilst `Styler.hide_index()` hides column header name (along with the index), the index name row persists when `Styler.hide_columns()` is used to hide column headers.
The index names rows should also be hidden, since currently there is no way to hide it independently.
What to do:
1) Find `pandas/io/formats/style_render.py`
2) Find the method for the `StyleRenderer` called `_translate_header` and read the documentation what it is trying to structure.
3) Go to the section in the method labelled `# 2` and insert `self.hide_columns_` into the conditional.
4) Add a test to `pandas/tests/io/formats/style/test_style.py` that addresses this.
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] | open | false | null | [] | null | 2 | "2021-06-18T09:36:11Z" | "2021-08-21T02:37:23Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```
single_index_no_duplicates = pd.Index([0, 1, 2])
single_index_with_duplicates = pd.Index([0, 1, 1])
multi_index_no_duplicates = pd.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0)])
multi_index_with_duplicates = pd.MultiIndex.from_tuples([(0, 0), (1, 0), (1, 0)])
>>> pd.Series(range(3), single_index_no_duplicates).loc[0]
0
>>> pd.Series(range(3), single_index_with_duplicates).loc[0]
0
>>> pd.Series(range(3), multi_index_no_duplicates).loc[(0,0)]
0
>>> pd.Series(range(3), multi_index_with_duplicates).loc[(0,0)]
0 0 0
dtype: int64
```
#### Problem description
When indexing a unique index value with `.loc` on a `MultiIndex` containing duplicated values,
the return type is a `Series `while it is a raw value for single `Index`.
#### Expected Output
I think we should expect a raw value to keep consistent with `Index` behavior.
```
>>> pd.Series(range(3), multi_index_with_duplicates).loc[(0,0)]
0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-55-generic
Version : #62~20.04.1-Ubuntu SMP Wed Jun 2 08:55:04 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2019.3
dateutil : 2.7.3
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-06-18T11:48:15Z" | "2021-07-16T11:31:48Z" | "2021-07-16T11:31:48Z" | MEMBER | null | Currently, when using `assert_produces_warning` with `match=`, the error message is not helpful. e.g.:
```
E AssertionError: Did not see warning 'FutureWarning' matching The default value of regex wilt change from True to False in a future version\.$
```
I think it should show the diff of the message, like pytest does | {
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} | 3 | "2021-06-18T13:15:37Z" | "2021-06-21T13:03:57Z" | "2021-06-21T13:03:57Z" | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
index = pd.DatetimeIndex(['2018-12-31', '2019-03-31', '2019-06-30', '2019-09-30', '2019-12-31', '2020-03-31'], freq='Q-DEC')
print(index[::2].intersection(index[1::2]))
```
#### Problem description
The produced output is:
DatetimeIndex(['2019-06-30', '2019-12-31'], dtype='datetime64[ns]', freq='2Q-DEC')
This is clearly incorrect, as there is no overlap between the indices index[::2] and index[1::2].
The issue is related to the freq attribute, which is set to '2Q-DEC' for both index[::2] and index[1::2]. If I remove or change the freq attribute, then it works correctly.
#### Expected Output
DatetimeIndex([], dtype='datetime64[ns]', freq=None)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.21.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : 1.3.0
numba : 0.52.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42105 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42105/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42105/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42105/events | https://github.com/pandas-dev/pandas/issues/42105 | 924,936,996 | MDU6SXNzdWU5MjQ5MzY5OTY= | 42,105 | BUG: error 'PandasArray' object has no attribute '_str_replace' | {
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df["CleanedCol"] = df[colname].str.replace(r'[^A-Za-z0-9]+', ' ')
details of the error is
accessor.py", line 1312, in replace
result = self._array._str_replace(
AttributeError: 'PandasArray' object has no attribute '_str_replace'
output of pd.show_version()
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.1.5
numpy : 1.17.4
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 52.0.0.post20210125
Cython : 0.29.21
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.2.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.4.15
tables : 3.5.1
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} | 3 | "2021-06-18T14:59:24Z" | "2021-06-21T14:31:12Z" | "2021-06-21T14:31:12Z" | MEMBER | null | https://github.com/pandas-dev/pandas/issues/39171#issuecomment-773477244
In the user guide, let's use
```
idx, cols = pd.factorize(df['col'])
df.reindex(cols, axis=1).to_numpy()[np.arange(len(df)), idx]
```
instead of the current workaround
https://pandas.pydata.org/pandas-docs/dev/user_guide/indexing.html#looking-up-values-by-index-column-labels
cc @erfannariman
also cc @quanghm in case you have further comments on this | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42107 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42107/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42107/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42107/events | https://github.com/pandas-dev/pandas/issues/42107 | 925,004,194 | MDU6SXNzdWU5MjUwMDQxOTQ= | 42,107 | BUG: 1.3.0rc Series.mode errors with BooleanDtype series containing nans | {
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} | 2 | "2021-06-18T15:19:31Z" | "2021-06-21T13:19:43Z" | "2021-06-21T13:19:43Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.Series([True, False, True, pd.NA], dtype='boolean').mode()
```
#### Problem description
The above `mode` call produces the following error: `TypeError: int() argument must be a string, a bytes-like object or a number, not 'NAType'`.
This occurs whether `pd.NA` or `None` or `np.nan` is used.
#### Expected Output
The mode call should not raise an error.
```python
assert pd.Series([True, False, True, pd.NA], dtype='boolean').mode() is True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2dd9e9ba9009a40191c0c0b96262fa3939d609f0
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0rc1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.06.0
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 3 | "2021-06-18T20:41:31Z" | "2021-06-18T22:17:17Z" | "2021-06-18T22:17:17Z" | NONE | null | #### Is your feature request related to a problem?
I wish the CSV files exported from Pandas have cell sizes automatically fitted to the data. Using Microsoft Excel to open a CSV file exported from Pandas results in a cluttered spreadsheet. Some data in a cell are hidden until I manually increase the cell size.
#### Describe the solution you'd like
df.to_csv() should have a new parameter called autoFit that automatically resizes the cells before exporting the data.
#### API breaking implications
Well, I can't answer this specifically.
#### Describe alternatives you've considered
None at the moment
#### Additional context
Google sheets have a fit-to-data option that resizes each cell based on the length of its content.
```python
# None available. I was hoping this is something someone in the community can do.
```
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
Straight forward issue. Not all `.c` files are generated and shouldn't be ignored. Problematic for code searching tools that are set to respect `.gitignore` files. Ran into this when doing `rg` to search for `on_bad_lines` and never saw the matches in `_libs/src/tokenizer.c` | {
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Not 100% what tests I could write for this. This works locally. git status shows no untracked files while `rg on_bad_lines` now shows results from `pandas/_libs/src/parser/tokenizer.c` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42118 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42118/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42118/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42118/events | https://github.com/pandas-dev/pandas/issues/42118 | 925,326,831 | MDU6SXNzdWU5MjUzMjY4MzE= | 42,118 | BUG: Creating DataFrame frome Series with DatetimeIndex results in NaN-filled df | {
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] | closed | false | null | [] | null | 3 | "2021-06-19T06:16:59Z" | "2021-06-19T19:41:11Z" | "2021-06-19T19:41:11Z" | NONE | null | - [x] I have checked that this issue has not already been reported (but not 100% sure either - search returns many tickets).
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# Work when creating a df without datetime index.
pd.DataFrame({'a':pd.Series([1,2])})
Out[11]:
a
0 1
1 2
# Work when working with a datetime index and only with values of the Series
# (either with 'to_numpy()' or 'values').
pd.DataFrame({'a':pd.Series([1,2]).values},
index=[pd.Timestamp('2021/01/01 08:00'), pd.Timestamp('2021/01/01 09:00')])
Out[12]:
a
2021-01-01 08:00:00 1
2021-01-01 09:00:00 2
# Does not work when working directly with Series and datetime index.
pd.DataFrame({'a':pd.Series([1,2])},
index=[pd.Timestamp('2021/01/01 08:00'), pd.Timestamp('2021/01/01 09:00')])
Out[13]:
a
2021-01-01 08:00:00 NaN
2021-01-01 09:00:00 NaN
```
#### Problem description
In my real case problem, I am generating data through dict completion in which are handled pd.Series.
Something like:
```python
var = pd.Series(list(range(1,5)))
data = { n : (var.shift(-n) - var)/var for n in range(1,3)}
df = pd.DataFrame(data, index=pd.date_range(pd.Timestamp('2021/01/01 08:00'), periods=4, freq='1h'))
```
And this results in a df filled with NaN
Is this a bug or a feature?
#### Expected Output
A df not filled with NaN.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-55-generic
Version : #62~20.04.1-Ubuntu SMP Wed Jun 2 08:55:04 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : 0.6.3
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/42119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42119/events | https://github.com/pandas-dev/pandas/issues/42119 | 925,368,531 | MDU6SXNzdWU5MjUzNjg1MzE= | 42,119 | DOC: read_excel skiprows does not work as documented | {
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] | closed | false | null | [] | null | 0 | "2021-06-19T10:47:32Z" | "2021-06-19T11:45:16Z" | "2021-06-19T11:45:16Z" | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html
#### Documentation problem
Both, stable and dev documentations read
> **skiprows : _list-like, int, or callable, optional_**
> Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file.
but the phrase "number of lines to skip (int) at the start of the file" does not resemble the behavior that I observe.
Consider the demo [data.xlsx](https://github.com/pandas-dev/pandas/files/6680539/data.xlsx):
Now, if I wanted to only read the highlighted cells containing 1,2,3 I would basically want to skip the first _5 lines at the start of the file_.
Based on the documentation I would then use `skiprows=5` (and `nrows=3`), but that is not how `skiprows` works:
```python
import pandas as pd
print( pd.read_excel( 'data.xlsx', engine='openpyxl', usecols='A', skiprows=0, names=['with skiprows=0:'] ) )
print( pd.read_excel( 'data.xlsx', engine='openpyxl', usecols='A', skiprows=2, names=['with skiprows=2:'] ) )
print( pd.read_excel( 'data.xlsx', engine='openpyxl', usecols='A', skiprows=5, names=['with skiprows=5:'] ) )
```
prints:
```
with skiprows=0:
0 22
1 33
2 1
3 2
4 3
5 4
with skiprows=2:
0 1
1 2
2 3
3 4
with skiprows=5:
0 2
1 3
2 4
```
pandas 1.0.4
openpyxl 3.0.6
#### Suggested fix for documentation
Based on my observations I would suggest the following formulation:
> [...] or number of **non-empty** lines to skip (int) at the start of the file.
#### NB
It would actually be helpful to get the documented behavior, i.e. being able to select rows based on the rows in the file (corresponding to the coordinates within the excel file).
This is implemented for column selection via the `usecols` keyword but not for rows.
I am planning on opening a separate request to ask about that functionality.
#### _Update_
I just realized that it was the interference with the default `header=0` that confused me.
Of course, I would want to and should have included `header=None` in my example.
With `header=None` I get the the desired selection for `skiprows=5` (and also `skiprows=3` due to the empty cells)
```python
print( pd.read_excel( 'data.xlsx', engine='openpyxl', usecols='A', skiprows=0, header=None, names=['with skiprows=0:'] ) )
print( pd.read_excel( 'data.xlsx', engine='openpyxl', usecols='A', skiprows=3, header=None, names=['with skiprows=3:'] ) )
print( pd.read_excel( 'data.xlsx', engine='openpyxl', usecols='A', skiprows=5, header=None, names=['with skiprows=5:'] ) )
```
produces
```
with skiprows=0:
0 11
1 22
2 33
3 1
4 2
5 3
6 4
with skiprows=3:
0 1
1 2
2 3
3 4
with skiprows=5:
0 1
1 2
2 3
3 4
```
So the documentation is right.
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