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```styler = DataFrame([[1]]).style```
doing
```styler.to_latex(hrules=True)``` (or any other kw argument which impacts `table_styles`)
results in:
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\begin{tabular}{lr}
\toprule
{} & {0} \\
\midrule
0 & 1 \\
\bottomrule
\end{tabular}
```
Now doing
```styler.to_latex(hrules=False)```
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\toprule
{} & {0} \\
\midrule
0 & 1 \\
\bottomrule
\end{tabular}
```
because the `table_styles` attribute on `styler` has been overwritten by first call which dominates the second.
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} | 3 | "2021-06-30T07:31:53Z" | "2021-07-02T21:03:14Z" | "2021-07-02T20:30:47Z" | MEMBER | null | Reverts pandas-dev/pandas#42317 which was a revert to backport for 1.3.0 release, this puts those changes back on master.
does not close perf issue as will still exist on master | {
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} | 8 | "2021-06-30T07:45:04Z" | "2021-07-13T20:58:54Z" | "2021-06-30T14:39:23Z" | CONTRIBUTOR | null | - [x] closes #42320
- [x] tests added / passed
- [x] whatsnew entry not needed if included in 1.3.0 (although will be needed for 1.3.1)
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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
Added a light mode version of the favicon based upon the .svgs in the static folder as well as added a small js scipt to the template page to detect the theme change.
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* Standardize the [official federal holiday names](https://www.opm.gov/policy-data-oversight/pay-leave/federal-holidays/#url=2021) | {
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} | 3 | "2021-06-30T20:55:22Z" | "2021-08-09T01:50:17Z" | "2021-08-08T23:30:36Z" | MEMBER | null | - [x] closes #27591
- [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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https://api.github.com/repos/pandas-dev/pandas/issues/42330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42330/events | https://github.com/pandas-dev/pandas/pull/42330 | 934,688,511 | MDExOlB1bGxSZXF1ZXN0NjgxNzIxODMw | 42,330 | BENCH: update imports to allow testing against 1.2.5 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42331 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42331/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42331/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42331/events | https://github.com/pandas-dev/pandas/issues/42331 | 934,688,784 | MDU6SXNzdWU5MzQ2ODg3ODQ= | 42,331 | BUG: Unsorted and Monotonic Indexes respond differently on non-existant key(s) slicing | {
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] | open | false | null | [] | null | 1 | "2021-07-01T10:31:01Z" | "2021-08-21T04:06:28Z" | null | CONTRIBUTOR | null | For a python list, bad-indexing can result in `IndexError`:
```
>>> ls = ["a", "b", "c"]
>>> ls[1]
"b"
>>> ls[5]
IndexError("list index out of range")
```
Slicing behaves differently and returns empty list with bad indexes:
```
>>> ls[2:5]
["c"]
>>> ls[4:5]
[]
```
For a **monotonic** `Index` pandas behaves in the same way either when a single key or list of keys is given compared with a slice:
```
>>> ser = Series([1,2,3], index=["a", "b", "c"])
# single key
>>> ser.loc["a"]
1
>>> ser.loc["!"]
KeyError("!")
# list of keys
>>> ser.loc[["a", "b"]]
Series([1, 2], index=["a", "b"])
>>> ser.loc[["!", "a"]]
KeyError("['!'] not in index")
>>> ser.loc[["!", "!!"]]
KeyError("None of [Index(['!', '!!'], dtype='object')] are in the [index]")
# key slicing
>>> ser.loc[slice("a","b")]
Series([1, 2], index=["a", "b"])
>>> ser.loc[slice("a", "!")]
Series([], dtype: int64)
>>> ser.loc[slice("!", "!!")]
Series([], dtype: int64)
```
For a non-monotonic (**unsorted**) index the behaviour is the same except for the empty return:
```
>>> ser = Series([1,3,2], index=["a", "c", "b"])
# key slicing
>>> ser.loc[slice("a", "b")
Series([1, 3, 2], index=["a", "c", "b"])
>>> ser.loc[slice("a", "!")]
KeyError("!")
>>> ser.loc[slice("!", "!!")]
KeyError("!")
```
Anyone with ideas?
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} | 7 | "2021-07-01T10:49:41Z" | "2021-08-31T22:43:44Z" | "2021-08-31T07:52:56Z" | 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
df = pd.DataFrame(columns=["id1", "id2", "time", "values"], dtype="int")
df_ok = df.groupby(["id1", "id2"]).sum()
print("Dataframe using .count() with groupby column names retained")
print(df_ok.index.names)
df_ok_2 = df.groupby(["id1", "id2"]).agg(**{
"start": pd.NamedAgg(column="time", aggfunc="min"),
})
print()
print("Dataframe using aggfunc='min' with groupby columns retained")
print(df_ok_2.index.names)
df_bad = df.groupby(["id1", "id2"]).agg(**{
"start": pd.NamedAgg(column="time", aggfunc="min"),
"peak_time": pd.NamedAgg(column="values", aggfunc="idxmax")
})
print()
print("Dataframe using aggfunc='min' and aggfunc='idxmax' with groupby column names lost")
print(df_bad.index.names)
df_bad = df.groupby(["id1", "id2"]).agg(**{
"peak_time": pd.NamedAgg(column="values", aggfunc="idxmax")
})
print()
print("Dataframe using only aggfunc='idxmax' with groupby columns lost entirely")
print(df_bad.index.names)
```
Output:
```
Dataframe using .count() with groupby column names retained
['id1', 'id2']
Dataframe using aggfunc='min' with groupby columns retained
['id1', 'id2']
Dataframe using aggfunc='min' and aggfunc='idxmax' with groupby column names lost
[None, None]
Dataframe using only aggfunc='idxmax' with groupby columns lost entirely
[None]
```
#### Problem description
The index returned by `DataFrame.groupby().agg()` is inconsistent depending on the aggregation applied to the groupby for an empty dataframe. This makes it difficult to use the output of these functions without adding subsequent if/else blocks to re-build the index names.
In the above examples, the provided DataFrame is empty. The behaviour of the returned DataFrames differs by how I handle the groupby:
1. If I use a function like .count(), the 'groupby' columns are both retained, and their names are kept, as I expect
2. If I use .agg() with 'min' as the function, the same good behaviour occurs
3. If I use .agg() with two functions, one being 'min' and the other 'idxmin', two index columns are returned but their names are lost, becoming 'level_0' and 'level_1'
4. If I use .agg() with only 'idxmin' as the function, only a single index column is returned, even though I grouped by two columns.
In Pandas 1.1.5, the third case behaved well, remembering the column names; the behaviour changed in 1.2.0.
#### Expected Output
The groupby columns and their names are remembered in all cases.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.31.1.el7.x86_64
Version : #1 SMP Tue Jun 15 10:20:52 CDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.5
numpy : 1.19.4
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 41.6.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.06.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 0 | "2021-07-01T14:13:09Z" | "2021-08-16T20:26:59Z" | "2021-08-16T20:26:59Z" | MEMBER | null | This seems to have resulted in a performance regression for the existing ASV benchmark: https://pandas.pydata.org/speed/pandas/#rolling.EWMMethods.time_ewm?python=3.8&Cython=0.29.21&p-constructor='DataFrame'&p-constructor='Series'&p-window=1000&p-dtype='float'&p-method='mean'&commits=ab687aec-f066cda6
_Originally posted by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/40072#issuecomment-787456038_ | {
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} | 9 | "2021-07-01T15:53:09Z" | "2021-07-02T13:36:36Z" | "2021-07-01T18:17:50Z" | MEMBER | null | - [ ] xref #42240
- [ ] 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
Reverts the rest of #41929 | {
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} | 3 | "2021-07-02T04:56:30Z" | "2021-07-06T19:06:22Z" | "2021-07-06T16:52:51Z" | MEMBER | null | - [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
close #42273 | {
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} | 1 | "2021-07-02T05:43:53Z" | "2021-07-15T21:22:42Z" | "2021-07-15T21:22:34Z" | CONTRIBUTOR | null | Currently, the [user guide for groupby](https://pandas.pydata.org/docs/dev/user_guide/groupby.html#dataframe-column-selection-in-groupby) shows the following code, but it is unclear where the "C" and "D" columns come from.
In the rendered documentation, the preceding definition of `df` does not have "C" and "D" columns.
```python
In [50]: arrays = [
....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
....: ["one", "two", "one", "two", "one", "two", "one", "two"],
....: ]
....:
In [51]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"])
In [52]: df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 3, 3], "B": np.arange(8)}, index=index)
In [53]: df
Out[53]:
A B
first second
bar one 1 0
two 1 1
baz one 1 2
two 1 3
foo one 2 4
two 2 5
qux one 3 6
two 3 7
```
Instead, the code under the heading "DataFrame column selection in GroupBy" is referring to a completely different dataframe which is in the RST file but is suppressed in the rendered documentation.
This pull request un-suppresses that relevant dataframe so that readers can see where the "C" and "D" columns come from. | {
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} | 3 | "2021-07-02T10:35:15Z" | "2021-07-04T13:33:00Z" | "2021-07-03T22:46:35Z" | CONTRIBUTOR | null | follows up an oversigh from #40628.
`False` was deprecated as an input so the example should be updated. | {
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} | 16 | "2021-07-02T13:45:44Z" | "2021-07-28T12:08:05Z" | "2021-07-28T12:08:04Z" | MEMBER | null | Tracking issue for the 1.3.1 release.
https://github.com/pandas-dev/pandas/milestone/87
Currently scheduled for July 25, 2021 (date flexible on severity of regressions)
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression
significant performance regressions between 1.2.5 and 1.3.0
```
before after ratio
[7c48ff44] [f00ed8f4]
<v1.2.5^0> <v1.3.0^0>
+ 2.43±0.03ms 28.4±1ms 11.66 indexing.InsertColumns.time_assign_list_like_with_setitem
+ 5.79±0.08ms 29.9±0.4ms 5.17 frame_methods.MaskBool.time_frame_mask_bools
+ 88.7±0.9μs 453±5μs 5.11 inference.ToTimedelta.time_convert_int
+ 82.6±2ms 386±1ms 4.67 frame_ctor.FromDicts.time_nested_dict_int64
+ 363±10μs 1.26±0.01ms 3.48 stat_ops.FrameOps.time_op('prod', 'int', 1)
+ 537±7μs 1.84±0.01ms 3.43 stat_ops.FrameOps.time_op('mean', 'int', 1)
+ 341±3μs 1.09±0.01ms 3.21 stat_ops.FrameOps.time_op('sum', 'int', 1)
+ 643±10μs 1.95±0.01ms 3.03 rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'mean')
+ 626±6μs 1.88±0.03ms 3.00 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'mean')
+ 678±9μs 2.00±0.04ms 2.95 rolling.EWMMethods.time_ewm('Series', 10, 'int', 'mean')
+ 696±10μs 2.01±0.01ms 2.89 rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'mean')
+ 592±9μs 1.68±0.01ms 2.84 stat_ops.FrameOps.time_op('mean', 'int', 0)
+ 758±10μs 2.11±0.02ms 2.79 rolling.EWMMethods.time_ewm('DataFrame', 10, 'float', 'mean')
+ 1.34±0.01ms 3.73±0.1ms 2.78 stat_ops.FrameOps.time_op('std', 'int', 0)
+ 804±10μs 2.21±0.03ms 2.75 rolling.EWMMethods.time_ewm('DataFrame', 10, 'int', 'mean')
+ 479±4μs 1.32±0.04ms 2.75 stat_ops.FrameOps.time_op('sum', 'int', 0)
+ 213±4ns 583±8ns 2.74 tslibs.timestamp.TimestampProperties.time_freqstr(None, 'B')
+ 218±2ns 592±4ns 2.72 tslibs.timestamp.TimestampProperties.time_freqstr(<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+ 1.32±0.03ms 3.58±0.04ms 2.70 stat_ops.FrameOps.time_op('var', 'int', 0)
+ 221±2ns 595±9ns 2.69 tslibs.timestamp.TimestampProperties.time_freqstr(tzlocal(), 'B')
+ 216±2ns 582±6ns 2.69 tslibs.timestamp.TimestampProperties.time_freqstr(datetime.timezone(datetime.timedelta(seconds=3600)), 'B')
+ 212±4ns 567±10ns 2.68 tslibs.timestamp.TimestampProperties.time_freqstr(tzfile('/usr/share/zoneinfo/Asia/Tokyo'), 'B')
```
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage | {
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] | closed | false | null | [] | null | 2 | "2021-07-02T15:06:03Z" | "2021-07-02T15:12:17Z" | "2021-07-02T15:11:57Z" | 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.
#### Problem description
Using a package manager (pip 21.1.1 on python 3.8), pandas 1.3.0 is not installable.
This project has neither a source distribution nor any non-3.7 versions for the 1.3.0 version of pandas according to: https://pypi.org/project/pandas/#files
#### Expected Output
`pip install pandas==1.3.0` would work if python 3.8 is platform.
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https://api.github.com/repos/pandas-dev/pandas/issues/42345 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42345/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42345/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42345/events | https://github.com/pandas-dev/pandas/issues/42345 | 935,876,620 | MDU6SXNzdWU5MzU4NzY2MjA= | 42,345 | BUG: regression for 1.3.0: saving a dataframe with the "pickle" package using pandas 1.2.5 would not open with "pickle" again with pandas 1.3.0 | {
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} | 9 | "2021-07-02T15:33:25Z" | "2021-07-06T19:40:38Z" | "2021-07-06T19:40:38Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
I created a notebook here to illustrate the issue
https://colab.research.google.com/drive/1por9_BTeLYvZxNzLOBv_jIsFh44mnz4N?usp=sharing
Main error message:
```
/usr/local/lib/python3.7/dist-packages/pandas/_libs/internals.pyx in pandas._libs.internals.BlockManager.__cinit__()
TypeError: __cinit__() takes at least 2 positional arguments (0 given)
```
#### Problem description
Dataframes saved with pandas 1.2.5 and "pickle" are no longer loadable with pandas 1.3.0
Related to #42343
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 57.0.0
Cython : 0.29.23
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.18
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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] | null | 9 | "2021-07-02T15:42:15Z" | "2021-10-31T01:10:37Z" | "2021-10-31T01:10:37Z" | CONTRIBUTOR | null | - [x] closes #6878
- [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
Probably the hardest part of this PR is choosing a good name for the new argument. As mentioned in #6878 the `name` keyword is already used in `Series.reset_index`, although to rename the data column. It could be argued that using `name` also in this PR would not cause confusion, since it's hard to assume one can rename a column's name in `DataFrame.reset_index` (which column would it be?).
Anyway, I've opted for `names`. I've also thought of `rename` but that sounds like asking for a boolean. `new_name` is an option too, although a bit ugly. | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
#### Problem description
The list of assets published on Pypi for pandas 1.3.0 does not contain assets for Python 3.8 and Python 3.9. See the list [here](https://pypi.org/project/pandas/1.3.0/#files)
For comparison, in Pandas 1.2.5, the list of published assets is [here](https://pypi.org/project/pandas/1.2.5/#files).
#### Expected Output
I expected the same list of assets as the one of Pandas 1.2.5 for Pandas 1.3.0. | {
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] | open | false | null | [] | null | 1 | "2021-07-02T18:51:15Z" | "2021-08-21T04:08:40Z" | null | MEMBER | null | The 'level' argument in MultiIndex methods can be ambiguous in at least 2 ways.
1) If an index's level names are `[1, 0]`, then `level=0` may refer to either level depending on whether it is a position or a label #21677.
2) If an index level names are `["foo", "bar", ("foo, "bar")]`, then `level=("foo", "bar")` may refer to either the first two levels, or the last level #21120
One option to address the first one is adding keywords to specify e.g. level_num or level_name #10461
The new idea I'd like to discuss is having a `pd.Label` class to treat something ambiguous as a label. So in the first case, passing `level=pd.Label(0)` would indicate that you want the second level. In the second case, `level=pd.Label(("foo", "bar"))` would indicate that this should be interpreted as a single label and not a sequence of labels.
Following an appropriate deprecation cycle, the default would be to interpret ambiguous cases as positional/sequence unless pd.Label is explicitly used.
pd.Label has a couple of other potential uses
A) If `frame.index` (or `series.index.levels[0]`) contains tuples, then `df.loc[a, b]` may either be selecting a single key `(a, b)` from the index, or may be selecting `a` from the index and `b` from the columns. `df.loc[pd.Label((a, b))` would disambiguate
B) in `__getitem__/__setitem__` pd.Label could indicate to use .loc and not fallback to .iloc (not that useful since the user could just use .loc directly) | {
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} | 1 | "2021-07-02T21:59:52Z" | "2021-07-05T23:21:16Z" | "2021-07-04T18:13:18Z" | MEMBER | null | - [x] closes #42287
- [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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} | 2 | "2021-07-02T23:42:06Z" | "2021-07-14T23:50:56Z" | "2021-07-14T23:20:45Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] whatsnew entry
There are a bunch of issues that boil down to this weird behavior. I need to track down the full list (@attack68 do you have such a list handy?) | {
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} | 2 | "2021-07-03T03:07:51Z" | "2021-07-12T12:43:43Z" | "2021-07-12T12:43:43Z" | NONE | null | Using `df.to_dict(orient="records")` with large dataframes is significantly slower in pandas 1.3.0 vs 1.2.5.
Could you please advice on what might be the cause of this issue?
### Test dataframe
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100823 entries, 0 to 262141
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Class 100823 non-null object
1 x 100823 non-null float64
2 y 100823 non-null float64
3 z 100823 non-null float64
4 rgb 100823 non-null object
5 distance 100823 non-null float64
6 treecluster 100820 non-null float64
7 normal 100823 non-null object
8 color 100823 non-null object
9 rgb_distance 100823 non-null object
10 responsibility 100820 non-null object
11 vp_codes 100820 non-null float64
12 rgb_treecluster 100823 non-null object
dtypes: float64(6), object(7)
memory usage: 10.8+ MB
```
### Simple timing test
### Profiling
#### Pandas 1.2.5
```
5547864 function calls (5547672 primitive calls) in 1.791 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
1310699 0.568 0.000 0.831 0.000 cast.py:137(maybe_box_datetimelike)
1411522 0.351 0.000 1.181 0.000 frame.py:1601(<genexpr>)
100824 0.288 0.000 0.288 0.000 frame.py:1596(<genexpr>)
1 0.280 0.280 1.759 1.759 frame.py:1600(<listcomp>)
2621687 0.263 0.000 0.263 0.000 {built-in method builtins.isinstance}
1 0.028 0.028 1.791 1.791 <string>:2(<module>)
100823 0.010 0.000 0.010 0.000 {method 'items' of 'dict' objects}
83/3 0.002 0.000 0.002 0.001 {built-in method _abc._abc_subclasscheck}
13 0.000 0.000 0.001 0.000 indexing.py:782(_getitem_lowerdim)
26 0.000 0.000 0.000 0.000 generic.py:5467(__setattr__)
```
#### Pandas 1.3.0
```
35794233 function calls (35794206 primitive calls) in 15.844 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
1310699 2.127 0.000 11.738 0.000 common.py:1578(_is_dtype_type)
806581 2.013 0.000 5.492 0.000 base.py:425(find)
1310696 1.942 0.000 8.020 0.000 common.py:1744(pandas_dtype)
2218073 1.415 0.000 1.740 0.000 base.py:208(construct_from_string)
12703947 1.410 0.000 1.410 0.000 {built-in method builtins.isinstance}
1310699 1.066 0.000 14.387 0.000 cast.py:173(maybe_box_native)
1310699 0.962 0.000 12.927 0.000 common.py:996(is_datetime_or_timedelta_dtype)
1411522 0.598 0.000 14.985 0.000 frame.py:1823(<genexpr>)
1 0.498 0.498 15.815 15.815 frame.py:1822(<listcomp>)
1310699 0.344 0.000 0.535 0.000 common.py:146(<lambda>)
100824 0.313 0.000 0.313 0.000 frame.py:1818(<genexpr>)
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} | 0 | "2021-07-03T03:28:17Z" | "2021-07-04T21:42:51Z" | "2021-07-04T21:33:46Z" | MEMBER | null | ```
In [2]: dti = pd.date_range('2016-01-01', periods=10**5, freq="s")
In [3]: dti2 = dti.delete(50000)
In [4]: %timeit dti.union(dti2)
1.77 ms ± 7.97 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master
1.55 ms ± 9.29 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
``` | {
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} | 1 | "2021-07-03T07:19:32Z" | "2021-07-04T21:34:16Z" | "2021-07-04T21:34:12Z" | CONTRIBUTOR | null | - [ ] 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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} | 0 | "2021-07-03T07:57:15Z" | "2021-07-04T21:32:44Z" | "2021-07-04T21:32:43Z" | MEMBER | null | The PyPy builds on conda-forge are failing with
```
In file included from pandas/_libs/algos.c:663:
pandas/_libs/src/klib/khash_python.h: In function 'complexobject_hash':
pandas/_libs/src/klib/khash_python.h:282:38: error: '_PyHASH_IMAG' undeclared (first use in this function)
282 | Py_uhash_t combined = realhash + _PyHASH_IMAG * imaghash;
| ^~~~~~~~~~~~
pandas/_libs/src/klib/khash_python.h:282:38: note: each undeclared identifier is reported only once for each function it appears in
```
This seems to be related to https://github.com/pandas-dev/pandas/pull/41988 cc @realead
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} | 3 | "2021-07-03T13:44:19Z" | "2021-07-20T22:16:49Z" | "2021-07-20T22:16:44Z" | 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
# Comes from private repo, but I will paste snippets where it gives errors
data.loc[:, 'update_status'] = data.apply(
my_function,
func_arg=some_value
)
```
#### Problem description
GitHub Actions started failing after July 3rd (see image below). Realized that pandas has been upgraded to 1.3.0 on July 2nd. **Reverted to pinning pandas to <= 1.2.5 and it started working again.**
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
Issue with 1.3.0 but works fine with 1.2.5
</details>
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} | 3 | "2021-07-03T15:15:04Z" | "2021-10-16T18:27:35Z" | null | CONTRIBUTOR | null | ```
In [2]: import pandas as pd
In [3]: pd.__version__
Out[3]: '1.4.0.dev0+143.g5675cd8ab2'
In [4]: s = pd.concat([pd.Series(list('abc'))] * 100_000)
In [5]: %timeit s.str.upper()
48.6 ms ± 420 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [6]: s1 = pd.concat([pd.Series(list('abc'), dtype='string[pyarrow]')] * 100_000)
In [7]: %timeit s1.str.upper()
308 ms ± 12.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [10]: s2 = s.astype('string[pyarrow]')
In [11]: %timeit s2.str.upper()
1.38 ms ± 21.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [12]: s1._mgr.blocks[0].values._data.num_chunks
Out[12]: 100000
In [13]: s2._mgr.blocks[0].values._data.num_chunks
Out[13]: 1
```
If you naively concat series, then the pyarrow storage is not-rechunked. This can lead to dramatic performance issues.
e.g. you get 100k chunks above, vs 1 in the astype operation. | {
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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
added a if branch to check the ascending type, as the existing one accepts value with string as well instead of boolean.
k=df.sort_values(by=['col1'],ascending="False") does not give exception with str.
added piece of code to raise exception if the type is not boolean
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idea is to disallow `foo: Series | AnyArrayLike`, as `Series` is array-like already
This came out of this review: https://github.com/pandas-dev/pandas/pull/41955#discussion_r663390463 | {
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} | 5 | "2021-07-03T19:11:00Z" | "2021-07-05T06:34:40Z" | "2021-07-04T21:32:44Z" | CONTRIBUTOR | null | - [x] closes #42355
- [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] what's new entry
`_PyHASH_Imag` is not defined in PyPy, thus we define and use our own `_PandasHASH_Imag`.
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} | 0 | "2021-07-04T00:30:52Z" | "2021-08-05T02:24:22Z" | null | MEMBER | null | Codecov supports multiple coverage reports through flags. (https://docs.codecov.com/docs/flags) By modifying the CI ASV Check to do ```coverage run ... ``` instead, we should be able to have code coverage with the ASV in addition to regular test coverage, which will help us catch perf regressions.
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} | 1 | "2021-07-04T01:18:43Z" | "2021-07-08T12:59:20Z" | "2021-07-08T12:59:20Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# Returns an empty Series/DataFrame when the one you pass in has length > 1 and an index with only one unique value
x = pd.Series(0, index=pd.date_range('2021-06-29', '2021-06-29')).repeat(5)
x.truncate('2021-06-28', '2021-07-01')
# If you add another unique index value, you get the expected behavior
x = pd.Series(0, index=pd.date_range('2021-06-29', '2021-06-29')).repeat(5)
y = pd.Series(0, index=pd.date_range('2021-06-01', '2021-06-01'))
x = x.append(y)
x.truncate('2021-06-28', '2021-07-01')
```
#### Problem description
I would expect truncate to not do any filtering in the first case given that all index values are between `before` and `after`. The fix is trivial, so I'll submit a PR.
#### Expected Output
```
2021-06-29 0
2021-06-29 0
2021-06-29 0
2021-06-29 0
2021-06-29 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.9.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.5
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.3
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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} | 3 | "2021-07-04T03:12:03Z" | "2021-07-08T12:59:24Z" | "2021-07-08T12:59:20Z" | CONTRIBUTOR | null | …alue (#42365)
- [x] closes #42365
- [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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} | 17 | "2021-07-04T04:26:25Z" | "2021-09-26T10:14:32Z" | "2021-09-26T10:14:31Z" | CONTRIBUTOR | null | This fixes the following warning from pyright: `TypeVar "..." appears only once in generic function signature (reportInvalidTypeVarUse)`.
It would be great to enable pyright on the CI. It takes only ~90sec to run pyright on the entire codebase with the following configuration in pyproject.toml (ignores most type errors for now):
```
[tool.pyright]
pythonVersion = "3.8"
include = ["pandas"]
ignore = ["pandas/tests"]
reportOptionalMemberAccess = false
reportOptionalOperand = false
reportGeneralTypeIssues = false
reportMissingImports = false
reportUnboundVariable = false
reportMissingModuleSource = false
reportOptionalSubscript = false
reportOptionalCall = false
reportOptionalIterable = false
reportPropertyTypeMismatch = false
```
The following errors would need to be taken care of before then:
pandas/pandas/core/frame.py:4575:9 - error: Method "_reindex_axes" cannot override final method defined in class "NDFrame"
pandas/pandas/core/frame.py:10736:9 - error: Method "mask" cannot override final method defined in class "NDFrame"
pandas/pandas/core/series.py:5449:9 - error: Method "mask" cannot override final method defined in class "NDFrame"
pandas/pandas/core/computation/expr.py:268:26 - error: TypeVar bound type cannot be generic
pandas/pandas/core/groupby/generic.py:676:9 - error: Method "describe" cannot override final method defined in class "GroupBy"
pandas/pandas/core/groupby/generic.py:856:9 - error: Method "pct_change" cannot override final method defined in class "GroupBy" | {
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- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
d1 = pd.DataFrame({"a": [1.0, 2.0], 'b': [3, np.nan]})
print('\nd1:')
print(d1)
print('\nd1.where(pd.notnull(d1), None, inplace=True):')
d1.where(pd.notnull(d1), None, inplace=True)
print('\nd1:')
print(d1)
d2 = pd.DataFrame({"a": [1.0, 2.0], 'b': [3, np.nan]})
print('\nd2:')
print(d2)
print('\nd2.where(pd.notnull(d2), None, inplace=True):')
d2.where(pd.notnull(d2), -1, inplace=True)
print('\nd2:')
print(d2)
```
#### Problem description
The result shows as follows. This means that if I set inplace to True, and the value of parameter "other" is `None`, then the manipulation isn't effective on the dataframe itself. This issue is kind of related to [BUG#42295](https://github.com/pandas-dev/pandas/issues/42295). Similarly, the bug doesn't show up if the value of `other` is a float/int type value.
```
d1:
a b
0 1.0 3.0
1 2.0 NaN
d1.where(pd.notnull(d1), None, inplace=True):
d1:
a b
0 1.0 3.0
1 2.0 NaN
d2:
a b
0 1.0 3.0
1 2.0 NaN
d2.where(pd.notnull(d2), None, inplace=True):
d2:
a b
0 1.0 3.0
1 2.0 -1.0
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 57.0.0
Cython : 0.29.23
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.18
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 0 | "2021-07-04T18:27:58Z" | "2021-07-05T08:57:04Z" | "2021-07-05T08:57:04Z" | MEMBER | null | xref #41989
There are 17 failures in Python 3.9 Windows build relate to `pyarrow`
`pyarrow.lib.ArrowNotImplementedError: Support for codec 'snappy' not built` | {
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} | 2 | "2021-07-04T18:49:54Z" | "2021-07-08T13:08:56Z" | "2021-07-08T13:08:56Z" | CONTRIBUTOR | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html
#### Documentation problem
Two of the * symbols used for the multiplication and power operators emphasize the comma in between them instead of being displayed: (+, -, /, , *) instead of (+, -, /, *, **).
#### Suggested fix for documentation
IDK should be easy to fix, though. | {
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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.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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} | 2 | "2021-07-05T02:57:41Z" | "2021-10-16T19:28:23Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
from scipy import sparse
X = sparse.random(100, 100, density=0.2, format="csr")
df = pd.DataFrame({"a": np.arange(100)})
df["X_sum"] = X.sum(axis=1)
df
```
Before 1.3.0, this worked fine. As of 1.3.0, displaying df fails with:
<details>
<summary> traceback </summary>
```pytb
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/usr/local/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
/usr/local/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
/usr/local/lib/python3.8/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698 """A pprint that just redirects to the normal repr function."""
699 # Find newlines and replace them with p.break_()
--> 700 output = repr(obj)
701 lines = output.splitlines()
702 with p.group():
/usr/local/lib/python3.8/site-packages/pandas/core/frame.py in __repr__(self)
993 else:
994 width = None
--> 995 self.to_string(
996 buf=buf,
997 max_rows=max_rows,
/usr/local/lib/python3.8/site-packages/pandas/core/frame.py in to_string(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, min_rows, max_cols, show_dimensions, decimal, line_width, max_colwidth, encoding)
1129 decimal=decimal,
1130 )
-> 1131 return fmt.DataFrameRenderer(formatter).to_string(
1132 buf=buf,
1133 encoding=encoding,
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in to_string(self, buf, encoding, line_width)
1051
1052 string_formatter = StringFormatter(self.fmt, line_width=line_width)
-> 1053 string = string_formatter.to_string()
1054 return save_to_buffer(string, buf=buf, encoding=encoding)
1055
/usr/local/lib/python3.8/site-packages/pandas/io/formats/string.py in to_string(self)
23
24 def to_string(self) -> str:
---> 25 text = self._get_string_representation()
26 if self.fmt.should_show_dimensions:
27 text = "".join([text, self.fmt.dimensions_info])
/usr/local/lib/python3.8/site-packages/pandas/io/formats/string.py in _get_string_representation(self)
38 return self._empty_info_line
39
---> 40 strcols = self._get_strcols()
41
42 if self.line_width is None:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/string.py in _get_strcols(self)
29
30 def _get_strcols(self) -> list[list[str]]:
---> 31 strcols = self.fmt.get_strcols()
32 if self.fmt.is_truncated:
33 strcols = self._insert_dot_separators(strcols)
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in get_strcols(self)
538 Render a DataFrame to a list of columns (as lists of strings).
539 """
--> 540 strcols = self._get_strcols_without_index()
541
542 if self.index:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in _get_strcols_without_index(self)
802 int(self.col_space.get(c, 0)), *(self.adj.len(x) for x in cheader)
803 )
--> 804 fmt_values = self.format_col(i)
805 fmt_values = _make_fixed_width(
806 fmt_values, self.justify, minimum=header_colwidth, adj=self.adj
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_col(self, i)
816 frame = self.tr_frame
817 formatter = self._get_formatter(i)
--> 818 return format_array(
819 frame.iloc[:, i]._values,
820 formatter,
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_array(values, formatter, float_format, na_rep, digits, space, justify, decimal, leading_space, quoting)
1238 )
1239
-> 1240 return fmt_obj.get_result()
1241
1242
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in get_result(self)
1269
1270 def get_result(self) -> list[str]:
-> 1271 fmt_values = self._format_strings()
1272 return _make_fixed_width(fmt_values, self.justify)
1273
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in _format_strings(self)
1516
1517 def _format_strings(self) -> list[str]:
-> 1518 return list(self.get_result_as_array())
1519
1520
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in get_result_as_array(self)
1480 float_format = lambda value: self.float_format % value
1481
-> 1482 formatted_values = format_values_with(float_format)
1483
1484 if not self.fixed_width:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_values_with(float_format)
1454 values = self.values
1455 is_complex = is_complex_dtype(values)
-> 1456 values = format_with_na_rep(values, formatter, na_rep)
1457
1458 if self.fixed_width:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_with_na_rep(values, formatter, na_rep)
1425 mask = isna(values)
1426 formatted = np.array(
-> 1427 [
1428 formatter(val) if not m else na_rep
1429 for val, m in zip(values.ravel(), mask.ravel())
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in <listcomp>(.0)
1426 formatted = np.array(
1427 [
-> 1428 formatter(val) if not m else na_rep
1429 for val, m in zip(values.ravel(), mask.ravel())
1430 ]
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/usr/local/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
/usr/local/lib/python3.8/site-packages/pandas/core/frame.py in _repr_html_(self)
1045 decimal=".",
1046 )
-> 1047 return fmt.DataFrameRenderer(formatter).to_html(notebook=True)
1048 else:
1049 return None
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in to_html(self, buf, encoding, classes, notebook, border, table_id, render_links)
1027 render_links=render_links,
1028 )
-> 1029 string = html_formatter.to_string()
1030 return save_to_buffer(string, buf=buf, encoding=encoding)
1031
/usr/local/lib/python3.8/site-packages/pandas/io/formats/html.py in to_string(self)
70
71 def to_string(self) -> str:
---> 72 lines = self.render()
73 if any(isinstance(x, str) for x in lines):
74 lines = [str(x) for x in lines]
/usr/local/lib/python3.8/site-packages/pandas/io/formats/html.py in render(self)
619 self.write("<div>")
620 self.write_style()
--> 621 super().render()
622 self.write("</div>")
623 return self.elements
/usr/local/lib/python3.8/site-packages/pandas/io/formats/html.py in render(self)
76
77 def render(self) -> list[str]:
---> 78 self._write_table()
79
80 if self.should_show_dimensions:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/html.py in _write_table(self, indent)
246 self._write_header(indent + self.indent_delta)
247
--> 248 self._write_body(indent + self.indent_delta)
249
250 self.write("</table>", indent)
/usr/local/lib/python3.8/site-packages/pandas/io/formats/html.py in _write_body(self, indent)
393 def _write_body(self, indent: int) -> None:
394 self.write("<tbody>", indent)
--> 395 fmt_values = self._get_formatted_values()
396
397 # write values
/usr/local/lib/python3.8/site-packages/pandas/io/formats/html.py in _get_formatted_values(self)
583
584 def _get_formatted_values(self) -> dict[int, list[str]]:
--> 585 return {i: self.fmt.format_col(i) for i in range(self.ncols)}
586
587 def _get_columns_formatted_values(self) -> list[str]:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/html.py in <dictcomp>(.0)
583
584 def _get_formatted_values(self) -> dict[int, list[str]]:
--> 585 return {i: self.fmt.format_col(i) for i in range(self.ncols)}
586
587 def _get_columns_formatted_values(self) -> list[str]:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_col(self, i)
816 frame = self.tr_frame
817 formatter = self._get_formatter(i)
--> 818 return format_array(
819 frame.iloc[:, i]._values,
820 formatter,
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_array(values, formatter, float_format, na_rep, digits, space, justify, decimal, leading_space, quoting)
1238 )
1239
-> 1240 return fmt_obj.get_result()
1241
1242
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in get_result(self)
1269
1270 def get_result(self) -> list[str]:
-> 1271 fmt_values = self._format_strings()
1272 return _make_fixed_width(fmt_values, self.justify)
1273
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in _format_strings(self)
1516
1517 def _format_strings(self) -> list[str]:
-> 1518 return list(self.get_result_as_array())
1519
1520
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in get_result_as_array(self)
1480 float_format = lambda value: self.float_format % value
1481
-> 1482 formatted_values = format_values_with(float_format)
1483
1484 if not self.fixed_width:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_values_with(float_format)
1454 values = self.values
1455 is_complex = is_complex_dtype(values)
-> 1456 values = format_with_na_rep(values, formatter, na_rep)
1457
1458 if self.fixed_width:
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in format_with_na_rep(values, formatter, na_rep)
1425 mask = isna(values)
1426 formatted = np.array(
-> 1427 [
1428 formatter(val) if not m else na_rep
1429 for val, m in zip(values.ravel(), mask.ravel())
/usr/local/lib/python3.8/site-packages/pandas/io/formats/format.py in <listcomp>(.0)
1426 formatted = np.array(
1427 [
-> 1428 formatter(val) if not m else na_rep
1429 for val, m in zip(values.ravel(), mask.ravel())
1430 ]
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
</details>
I discovered this new behaviour due to [our tests starting to fail](https://dev.azure.com/theislab/scanpy/_build/results?buildId=1078&view=logs&jobId=3ddc076e-0ab2-5212-d413-e45088e3a21e&j=9510bc2d-a38a-5fa2-99ea-073a9399fe06&t=0770b0df-44e0-560a-3d20-4284697180f3). What was causing that was:
```python
df["sum"] = X.sum(axis=1)
df["log1p_sum"] = np.log1p(df["sum"])
```
failing with:
<details>
<summary> traceback </summary>
```pytb
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-4ada647d56a0> in <module>
1 df["sum"] = X.sum(axis=1)
----> 2 df["log1p_sum"] = np.log1p(df["sum"])
/usr/local/lib/python3.8/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
3605 else:
3606 # set column
-> 3607 self._set_item(key, value)
3608
3609 def _setitem_slice(self, key: slice, value):
/usr/local/lib/python3.8/site-packages/pandas/core/frame.py in _set_item(self, key, value)
3777 ensure homogeneity.
3778 """
-> 3779 value = self._sanitize_column(value)
3780
3781 if (
/usr/local/lib/python3.8/site-packages/pandas/core/frame.py in _sanitize_column(self, value)
4502
4503 if is_list_like(value):
-> 4504 com.require_length_match(value, self.index)
4505 return sanitize_array(value, self.index, copy=True, allow_2d=True)
4506
/usr/local/lib/python3.8/site-packages/pandas/core/common.py in require_length_match(data, index)
525 """
526 if len(data) != len(index):
--> 527 raise ValueError(
528 "Length of values "
529 f"({len(data)}) "
ValueError: Length of values (1) does not match length of index (100)
```
</details>
#### Problem description
This problem is being triggered because the result of `X.sum(axis=1)` when `X` is a scipy sparse matrix is not a 1d numpy ndarray, but a `np.matrix` with one column. This used to be handled by pandas, but now isn't.
This is a problem because it's a behaviour change that breaks existing code. As far as I can tell from the release notes, this was not an intentional behaviour change. It does look like some things around column assignment did change, and I imagine that assigning with deprecated numpy types was not considered.
#### Expected Output
I would expect this to not error, and for this to pass: `np.testing.assert_array_equal(df["X_sum"], np.ravel(X.sum(axis=1)))`
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.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 : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2020.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 56.0.0
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.06.0
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.18.2
xlrd : 1.2.0
xlwt : None
numba : 0.53.1
```
</details>
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} | 3 | "2021-07-05T03:30:42Z" | "2021-07-05T13:43:50Z" | "2021-07-05T08:57:04Z" | MEMBER | null | - [ ] closes #42370
- [ ] 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
Apparently its recommended to install pyarrow from conda forge instead of the default conda channel(xref https://github.com/ContinuumIO/anaconda-issues/issues/12314, https://arrow.apache.org/docs/python/install.html#using-conda), trying to see if this fixes it. I don't think this will mess up any other packages since conda-forge packages are published by package maintainers, so they shouldn't have build issues? | {
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} | 0 | "2021-07-05T03:53:11Z" | "2021-07-07T16:05:27Z" | "2021-07-07T15:39:33Z" | MEMBER | null | 2 logic-neutral prelims split off from other branches
1) instead of catching and re-issuing TypeError in NDFrame.xs, just issue it with the correct exception message in the first place
2) inside _get_loc_level there is a 'k' variable that is serving a dual purpose as a loop index and as a variable inside that loop. AFAICT this is for a small code-de-duplication in checking for slices, but it turns out we need to treat slices differently in the two cases. This changes the inside variable to `loc_level` | {
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} | 3 | "2021-07-05T04:31:10Z" | "2021-07-10T11:05:55Z" | "2021-07-09T13:09:07Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"a": list("abcde"), "b": list("abcde")}, index=list("aabbc"), dtype="category")
df.agg("-".join, axis=1)
```
Using pandas 1.2.5:
```
a a-a
a b-b
b c-c
b d-d
c e-e
dtype: object
```
Using pandas 1.3.0:
```
a b-b
b d-d
c e-e
dtype: object
```
It does not look like this is an issue if I use `df.apply` instead of `df.agg`.
#### Problem description
When a aggregation of the rows is run on a dataframe with categorical columns and non-unique indices, the result is the wrong length.
It's weird that the output isn't the right length. Since I'm computing a value per row, I expect the same number of rows in the output as in the input. It's especially weird that this only happens if the columns are categorical.
That is:
```python
df = pd.DataFrame({"a": list("abcde"), "b": list("abcde")}, index=list("aabbc"))
df.agg("-".join, axis=1)
```
```
a a-a
a b-b
b c-c
b d-d
c e-e
dtype: object
```
in both versions.
#### Expected Output
I would expect the same output between versions. The result given by 1.2.5 seems more correct to me at the moment.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.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 : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2020.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 56.0.0
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.06.0
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.18.2
xlrd : 1.2.0
xlwt : None
numba : 0.53.1
```
</details>
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} | 0 | "2021-07-05T04:37:37Z" | "2021-07-06T19:09:50Z" | "2021-07-06T19:09:50Z" | MEMBER | null | https://github.com/pandas-dev/pandas/runs/2985248923
```
=================================== FAILURES ===================================
__________ TestTimedeltaMultiplicationDivision.test_td_div_nan[nan1] ___________
[gw0] linux -- Python 3.9.5 /usr/share/miniconda/envs/pandas-dev/bin/python
self = <pandas.tests.scalar.timedelta.test_arithmetic.TestTimedeltaMultiplicationDivision object at 0x7f2d87ba9f40>
nan = nan
@pytest.mark.parametrize(
"nan",
[
np.nan,
pytest.param(
np.float64("NaN"),
marks=pytest.mark.xfail(
# Works on numpy dev only in python 3.9
is_numpy_dev and not compat.PY39,
raises=RuntimeWarning,
reason="https://github.com/pandas-dev/pandas/issues/31992",
),
),
float("nan"),
],
)
def test_td_div_nan(self, nan):
# np.float64('NaN') has a 'dtype' attr, avoid treating as array
td = Timedelta(10, unit="d")
result = td / nan
assert result is NaT
> result = td // nan
pandas/tests/scalar/timedelta/test_arithmetic.py:455:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> return Timedelta(self.value // other, unit='ns')
E RuntimeWarning: invalid value encountered in double_scalars
pandas/_libs/tslibs/timedeltas.pyx:1465: RuntimeWarning
``` | {
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<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.10-py2.py3-none-any.whl (98 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.7-py2.py3-none-any.whl (7.2 MB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.10 nodeenv-1.6.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.4.7
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.19.4 -> v2.20.0.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Passed
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42383 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42383/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42383/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42383/events | https://github.com/pandas-dev/pandas/issues/42383 | 936,802,795 | MDU6SXNzdWU5MzY4MDI3OTU= | 42,383 | Needs Triage: In aggregation, pd.Series.nunique outputs float instead of int. | {
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] | closed | false | null | [] | null | 2 | "2021-07-05T07:56:14Z" | "2021-07-07T02:39:51Z" | "2021-07-07T02:39:51Z" | NONE | null | Sorry, I'm very new to issuing. I'm not sure if it's a bug or improvement. Here it is.
When you count the number of unique values in a `groupby` aggregation, you'll get a `float` output when the values are `float` themselves.
```python
df = pd.DataFrame({
'a': 4 * list(range(1, 3)),
'b': map(float, list(range(1, 9)))
})
df
```
```python
a b
0 1 1.0
1 2 2.0
2 1 3.0
3 2 4.0
4 1 5.0
5 2 6.0
6 1 7.0
7 2 8.0
```
```python
df.groupby('a').agg({'b': pd.Series.nunique}).b
```
```python
a
1 4.0
2 4.0
Name: b, dtype: float64
```
#### Problem description
If the type of `b` is `int`, it outputs `int`, but not for the `float` type. Nevertheless, the unique number is `int` no matter what.
#### Expected Output
```python
a
1 4
2 4
Name: b, dtype: int64
```
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} | 7 | "2021-07-05T10:22:04Z" | "2021-07-18T16:23:39Z" | null | NONE | null | I can't find the docs about this.
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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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} | 6 | "2021-07-05T13:11:42Z" | "2021-07-30T08:07:43Z" | "2021-07-30T08:07:43Z" | 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Hi everyone, I'm running into this issue since pandas 1.3.0:
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.read_csv("path/to/any/csv", names=None, prefix=None)
```
raises
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-681798f605ab> in <module>()
----> 1 pd.read_csv("/content/sample_data/mnist_test.csv", names=None, prefix=None)
2 frames
/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in _refine_defaults_read(dialect, delimiter, delim_whitespace, engine, sep, error_bad_lines, warn_bad_lines, on_bad_lines, names, prefix, defaults)
1304
1305 if names is not lib.no_default and prefix is not lib.no_default:
-> 1306 raise ValueError("Specified named and prefix; you can only specify one.")
1307
1308 kwds["names"] = None if names is lib.no_default else names
ValueError: Specified named and prefix; you can only specify one.
```
#### Problem description
With `names=None` and `prefix=None` those parameters shouldn't be considered as specified, and the code should run as if they were not passed as keyword arguments.
This is due to the changes in this PR https://github.com/pandas-dev/pandas/pull/41446 that changed the default values of those two parameters from `None` to `no_default`
#### Expected Output
The code should load the csv using the default behavior as if `names` and `prefix` were not passed as keyword arguments
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 57.0.0
Cython : 0.29.23
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.18
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
When merging data frames on an **Int64** column, the resulting dtype differs between pandas 1.2.5 and 1.3.0.
* If all values are present on the left and right side, the Int64 dtype is retained successfully both in 1.2.5 and 1.3.0, for all merge methods (inner, left, right, outer)
* If there are specific values on both sides, dtype is converted depending on merge method:
* 1.2.5 keeps **Int64** for inner and left merges, converts to **int64** for outer and right merges
* 1.3.0 keeps **Int64** for inner and left merges, converts to **object** for outer and right merges
#### Code Sample, a copy-pastable example
#### ALL VALUES IDENTICAL: Correct behaviour
```python
# pandas 1.2.5
df1 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[9]:
# nr Int64
```
```python
# pandas 1.3.0
df1 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[1,2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[9]:
# nr Int64
```
#### SIDE-SPECIFIC VALUES PRESENT: Differing behaviour, regression on 1.3.0
```python
# pandas 1.2.5
df1 = pd.DataFrame(dict(nr=[1,2,3 ])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[ 2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[1]:
# nr int64
pd.merge(df1, df2, how="right", on="nr").dtypes
# Out[2]:
# nr int64
pd.merge(df1, df2, how="left", on="nr").dtypes
# Out[3]:
# nr Int64
pd.merge(df1, df2, how="inner", on="nr").dtypes
# Out[4]:
# nr Int64
```
```python
# pandas 1.3.0
df1 = pd.DataFrame(dict(nr=[1,2,3 ])).astype({"nr": "Int64"})
df2 = pd.DataFrame(dict(nr=[ 2,3,4])).astype({"nr": "Int64"})
pd.merge(df1, df2, how="outer", on="nr").dtypes
# Out[1]:
# nr object
pd.merge(df1, df2, how="right", on="nr").dtypes
# Out[2]:
# nr object
pd.merge(df1, df2, how="left", on="nr").dtypes
# Out[3]:
# nr Int64
pd.merge(df1, df2, how="inner", on="nr").dtypes
# Out[4]:
# nr Int64
```
#### Problem description
When merging on an Int64 column, the Int64 dtype is lost in certain circumstances (outer and right joins, side-specific values present).
* On pandas 1.2.5, the column drops to **int64** (mostly equivalent to Int64, except for NULLs)
* On pandas 1.3.0, the column turns into an **object** (string) column (not equivalent to Int64 at all)
I found this when matches of a merged Int64 key against other Int64 columns silently stopped matching after the update to 1.3.0.
On 1.2.5, there was no problem, as int64 columns match correctly against Int64 ones.
#### Expected Output
* Ideally, the **Int64** dtype should be retained in all the above cases
* If this is currently not possible, the 1.2.5 behaviour of converting to **int64** is preferred, as the column still mostly works as expected
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : German_Switzerland.1252
pandas : 1.3.0
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 51.0.0
Cython : None
pytest : None
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 : None
IPython : 7.25.0
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 : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.0b1
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-07-05T17:05:43Z" | "2021-08-17T16:04:17Z" | "2021-08-17T16:04:17Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
def agg(series):
if series.isna().values.all():
return None
return np.sum(series)
df = pd.DataFrame([1.0], index=[pd.Timestamp("2018-01-16 00:00:00+00:00")])
df.groupby(lambda x: 1).agg(agg)
```
#### Problem description
This raise with 1.3.0 and succeeded with 1.2.5
```
Traceback (most recent call last):
File "/tmp/t.py", line 12, in <module>
df.groupby(lambda x: 1).agg(agg)
File "/home/phil/src/pandas/pandas/core/groupby/generic.py", line 1010, in aggregate
result = gba.agg()
File "/home/phil/src/pandas/pandas/core/apply.py", line 164, in agg
return self.agg_list_like()
File "/home/phil/src/pandas/pandas/core/apply.py", line 355, in agg_list_like
new_res = colg.aggregate(arg)
File "/home/phil/src/pandas/pandas/core/groupby/generic.py", line 249, in aggregate
ret = self._aggregate_multiple_funcs(func)
File "/home/phil/src/pandas/pandas/core/groupby/generic.py", line 303, in _aggregate_multiple_funcs
results[key] = self.aggregate(func)
File "/home/phil/src/pandas/pandas/core/groupby/generic.py", line 265, in aggregate
return self._python_agg_general(func, *args, **kwargs)
File "/home/phil/src/pandas/pandas/core/groupby/groupby.py", line 1310, in _python_agg_general
result = self.grouper.agg_series(obj, f)
File "/home/phil/src/pandas/pandas/core/groupby/ops.py", line 1028, in agg_series
result = self._aggregate_series_fast(obj, func)
File "/home/phil/src/pandas/pandas/core/groupby/ops.py", line 1053, in _aggregate_series_fast
result, _ = sgrouper.get_result()
File "pandas/_libs/reduction.pyx", line 281, in pandas._libs.reduction.SeriesGrouper.get_result
cached_series, cached_index, islider, vslider)
File "pandas/_libs/reduction.pyx", line 88, in pandas._libs.reduction._BaseGrouper._apply_to_group
res = self.f(cached_series)
File "/home/phil/src/pandas/pandas/core/groupby/groupby.py", line 1296, in <lambda>
f = lambda x: func(x, *args, **kwargs)
File "/tmp/t.py", line 6, in agg
if series.isna().values.all():
File "/home/phil/src/pandas/pandas/core/series.py", line 5161, in isna
return generic.NDFrame.isna(self)
File "/home/phil/src/pandas/pandas/core/generic.py", line 7145, in isna
return isna(self).__finalize__(self, method="isna")
File "/home/phil/src/pandas/pandas/core/dtypes/missing.py", line 138, in isna
return _isna(obj)
File "/home/phil/src/pandas/pandas/core/dtypes/missing.py", line 177, in _isna
result, index=obj.index, name=obj.name, copy=False
File "/home/phil/src/pandas/pandas/core/series.py", line 430, in __init__
com.require_length_match(data, index)
File "/home/phil/src/pandas/pandas/core/common.py", line 528, in require_length_match
"Length of values "
ValueError: Length of values (1) does not match length of index (0)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c8b79e1bd30fb4d36e5c1f894757d59c57837528
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-17-amd64
Version : #1 SMP Debian 4.19.194-2 (2021-06-21)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0+4.gc8b79e1bd3
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 57.1.0
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.14.1
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: 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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} | 6 | "2021-07-05T17:07:02Z" | "2021-08-15T09:17:52Z" | "2021-08-15T09:17:52Z" | NONE | null | closes https://github.com/pandas-dev/pandas/issues/42390
This fixes a regression introduced in c355ed1 where cache is not
initialized with correct state of islider and vslider.
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} | 2 | "2021-07-05T22:34:24Z" | "2021-07-05T22:37:54Z" | "2021-07-05T22:37:54Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```
df['col'].progress_apply(lambda x: x+1)
```
#### Problem description
generates error message when run in notebook under vscode (but works fine when run in a normal jupyter notebook)
dataframe object has no attribute _is_builtin_func
#### Expected Output
Should show a progress bar and perform the apply lambda
#### Output of ``pd.show_versions()``
1.3.0
</details>
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} | 4 | "2021-07-06T01:19:33Z" | "2021-11-20T23:20:47Z" | "2021-07-06T19:40:38Z" | MEMBER | null | - [x] closes #42345
- [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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} | 33 | "2021-07-06T07:57:36Z" | "2021-09-09T17:59:38Z" | "2021-09-09T17:59:38Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
{
"duration":[pd.Timedelta(i, unit="days") for i in range(1,6)],
"value":[1,0,1,2,1]
}
)
df.groupby("value").sum()
```
results in:
```
Empty DataFrame
Columns: []
Index: [0, 1, 2]
```
The same behaviour occurs whether it's sum, mean, median etc
Note the following gives close to the expected result:
```python
df.groupby("value")["duration"].sum()
```
```
value
0 2 days
1 9 days
2 4 days
Name: duration, dtype: timedelta64[ns]
```
Also, this problem may be unique to Timedeltas as the following similar example, has no problem:
```python
df = pd.DataFrame(
{
"duration":[i for i in range(1,6)],
"value":[1,0,1,2,1]
}
)
df.groupby("value").sum()
```
gives:
```
duration
value
0 2
1 9
2 4
```
#### Problem description
In v1.3.0 (and master, commit dad3e7) when performing a groupby operation on a dataframe a timedelta column goes missing.
This behaviour does not occur in pandas 1.2.5
#### Expected Output
The expected output is given by pandas 1.2.5:
```python
duration
value
0 2 days
1 9 days
2 4 days
```
It is a dataframe, with one column "duration", indexed by the groupby keys
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
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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} | 4 | "2021-07-06T09:31:51Z" | "2021-07-13T21:18:24Z" | "2021-07-13T21:18:24Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
npa = np.random.RandomState(0).randint(1000, size=(20, 8))
df = pd.DataFrame(npa, columns=[f'c{i}' for i in range(8)])
print(df.iloc[:6, :3])
# c0 c1 c2
# 0 684 559 629
# 1 9 723 277
# 2 600 396 314
# 3 600 849 677
# 4 115 976 755
# 5 99 984 177
print(df.iloc[:6, :3].astype('int32'))
# c0 c1 c2
# 0 684 600 115
# 1 559 396 976
# 2 629 314 755
# 3 9 600 99
# 4 723 849 984
# 5 277 677 177
```
#### Problem description
The order of output result is changed unexpectedly after `astype` is called. It may caused by data continuity as `df.iloc[:6, :3].copy().astype('int32')` works as expected.
#### Expected Output
```python
print(df.iloc[:6, :3].astype('int32'))
# c0 c1 c2
# 0 684 559 629
# 1 9 723 277
# 2 600 396 314
# 3 600 849 677
# 4 115 976 755
# 5 99 984 177
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu May 6 00:48:39 PDT 2021; root:xnu-6153.141.33~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : zh_CN.UTF-8
pandas : 1.3.0
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.23
pytest : 6.2.2
hypothesis : 6.3.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.2
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.0
</details>
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} | 10 | "2021-07-06T09:52:01Z" | "2021-07-06T11:58:10Z" | "2021-07-06T11:58:10Z" | 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
from pandas.core.dtypes.cast import find_common_type
import pandas
s = pandas.Series(["int", "float"])
find_common_type(s)
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
#### Problem description
The exception is raised in 1.3.0 whereas the method works correctly in 1.2.5.
#### Expected Output
Output for 1.2.5
```python
from pandas.core.dtypes.cast import find_common_type
import pandas
s = pandas.Series(["int", "float"])
find_common_type(s)
dtype('float64')
```
#### Output of ``pd.show_versions()``
<details>
pandas : 1.3.0
numpy : 1.20.3
pytz : 2020.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.05.0
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : 0.15.0
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.18
tables : 3.6.1
tabulate : None
xarray : 0.18.2
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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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.
---
#### Code Sample, a copy-pastable example
```python
start = pd.Timestamp('2021-07-05 08:10:11')
end = pd.Timestamp('2021-07-07 12:13:14')
dates = pd.date_range(start, end, freq=pd.offsets.BusinessHour(start="08:30"))
print(dates)
DatetimeIndex(['2021-07-05 08:30:00', '2021-07-05 09:30:00',
'2021-07-05 10:30:00', '2021-07-05 11:30:00',
'2021-07-05 12:30:00', '2021-07-05 13:30:00',
'2021-07-05 14:30:00', '2021-07-05 15:30:00',
'2021-07-05 16:30:00', '2021-07-06 09:00:00',
'2021-07-06 10:00:00', '2021-07-06 11:00:00',
'2021-07-06 12:00:00', '2021-07-06 13:00:00',
'2021-07-06 14:00:00', '2021-07-06 15:00:00',
'2021-07-06 16:00:00', '2021-07-07 08:30:00',
'2021-07-07 09:30:00', '2021-07-07 10:30:00',
'2021-07-07 11:30:00'],
dtype='datetime64[ns]', freq='BH')
dates.to_series().groupby(dates.date).min()
2021-07-05 2021-07-05 08:30:00
2021-07-06 2021-07-06 09:00:00
2021-07-07 2021-07-07 08:30:00
dtype: datetime64[ns]
```
#### Problem description
Making use the `start` argument in offsets.BusinessHour produces the wrong start time for each second day. So in the example above, I would've expected `2021-07-06` to start on `08:30:00` instead of `09:00:00`
#### Expected Output
See the description above.
pandas version 1.3.0.
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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.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
npa = np.random.RandomState(0).randint(1000, size=(20, 8))
df = pd.DataFrame(npa, columns=[f'c{i}' for i in range(8)])
pd.concat([df.iloc[:6, 3:5], df.iloc[:6, 6:8]], axis=1).shift(periods=2, axis=1, freq=None, fill_value=0)
```
```
Traceback (most recent call last):
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-4-9916dba4e800>", line 1, in <module>
pd.concat([df.iloc[:6, 3:5], df.iloc[:6, 6:8]], axis=1).shift(periods=2, axis=1, freq=None, fill_value=0)
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 5313, in shift
return super().shift(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/generic.py", line 9179, in shift
new_data = self._mgr.shift(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 393, in shift
result = self.reindex_indexer(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 669, in reindex_indexer
new_blocks = self._slice_take_blocks_ax0(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 714, in _slice_take_blocks_ax0
sl_type, slobj, sllen = _preprocess_slice_or_indexer(
File "/Users/wenjun/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 2033, in _preprocess_slice_or_indexer
raise TypeError(type(slice_or_indexer), dtype)
TypeError: (<class 'list'>, None)
```
#### Problem description
`shift` should work without exception. When `df._data._consolidate_inplace()` is called before shift, it works well. Thus consolidation might be missing, just like #35488.
#### Expected Output
```
c3 c4 c6 c7
0 0 0 192 835
1 0 0 754 804
2 0 0 705 486
3 0 0 537 845
4 0 0 709 847
5 0 0 755 797
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu May 6 00:48:39 PDT 2021; root:xnu-6153.141.33~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : zh_CN.UTF-8
pandas : 1.3.0
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.23
pytest : 6.2.2
hypothesis : 6.3.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.2
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.0
</details>
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- [x] 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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} | 6 | "2021-07-06T12:20:41Z" | "2021-08-05T05:52:23Z" | "2021-08-05T05:52:23Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
df = pandas.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.insert(1, "newcol", df)
df
col1 newcol col2
0 1 col1 3
1 2 col2 4
```
#### Problem description
Dataframe should take `int, Series, or array-like` as a `value` and throw an exception in case DataFrame is passed.
#### Expected Output
Behavior in 1.2.5
```python
import pandas
df = pandas.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.insert(1, "newcol", df)
ValueError: Wrong number of items passed 2, placement implies 1
```
#### Output of ``pd.show_versions()``
<details>
pandas : 1.3.0
numpy : 1.20.3
pytz : 2020.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.05.0
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : 0.15.0
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.18
tables : 3.6.1
tabulate : None
xarray : 0.18.2
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 2 | "2021-07-06T15:16:48Z" | "2021-07-14T18:19:04Z" | "2021-07-14T18:19:04Z" | NONE | null | This bug is not present in Pandas < 1.3.0.
In 1.3.0, calling `Series.isin()` will fail if
* the `Series` `dtype` is an extension dtype (`pd.Float64Dtype()`, `pd.Int64Dtype()`, ...)
* the `Series` contains any 'missing' values (`numpy.nan`, `pd.na`)
The following code snippet tests a few `dtypes`, determining if each of them supports `isin` with missing values:
```python
import pandas as pd
import numpy as np
for dtype in (float, int, pd.Float64Dtype(), pd.Int64Dtype(), object):
x = pd.Series([0, 1, 2, 3, 4], dtype=dtype)
options = [1, 2, 3]
print(f"\nTesting with dtype = {x.dtype}:")
x.isin(options) # This works everytime - no missing values
x.iloc[1] = np.nan # Set a value to NA
try:
x.isin(options) # This no longer works
except Exception as err:
print(f"Error! {err}")
else:
print("OK")
# Now, show the actual stack trace
print("\nStacktrace for dtype=Int64")
dtype = pd.Int64Dtype()
x = pd.Series([0, 1, 2, 3, 4], dtype=dtype)
options = [1, 2, 3]
x.iloc[1] = np.nan # Set a value to NA
x.isin(options)
```
The output is:
```
Testing with dtype = float64:
OK
Testing with dtype = int64:
OK
Testing with dtype = Float64:
Error! boolean value of NA is ambiguous
Testing with dtype = Int64:
Error! boolean value of NA is ambiguous
Testing with dtype = object:
OK
Stacktrace for dtype=Int64
Traceback (most recent call last):
File "...dev/pd_1_3_isin_bug.py", line 31, in <module>
x.isin(options)
File "..._dev_venv/lib/python3.7/site-packages/pandas/core/series.py", line 5024, in isin
result = algorithms.isin(self._values, values)
File "..._dev_venv/lib/python3.7/site-packages/pandas/core/algorithms.py", line 475, in isin
return comps.isin(values)
File "..._dev_venv/lib/python3.7/site-packages/pandas/core/arrays/masked.py", line 408, in isin
if libmissing.NA in values:
File "pandas/_libs/missing.pyx", line 446, in pandas._libs.missing.NAType.__bool__
TypeError: boolean value of NA is ambiguous
```
```python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.13.1.el7.x86_64
Version : #1 SMP Fri Jun 12 14:34:17 EDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.3
pytz : 2019.3
dateutil : 2.8.0
pip : 21.0.1
setuptools : 40.8.0
Cython : 0.29.13
pytest : 5.1.1
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fsspec : 0.5.2
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 0.13.0
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.9
tables : 3.5.2
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.45.1
```
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] | open | false | null | [] | null | 2 | "2021-07-06T15:28:45Z" | "2021-07-11T07:23:50Z" | null | MEMBER | null | Right now the default `See Also` section for a groupby method will include `Series.groupby` and `DataFrame.groupby`, both of which do not link to anything (and are not particularly informative on their own without a link). See for example https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.all.html.
Based on https://github.com/pandas-dev/pandas/blob/e102f80c92df13fe39efbc59b53371c2cbd1d42e/pandas/core/groupby/groupby.py#L116-L122 seems like the expected `See Also` would be to instead (for example with method `all`) link to
`Series.all` and `DataFrame.all`? The `See Also` could still link to groupby material if thought helpful, eg https://pandas.pydata.org/docs/reference/api/pandas.Series.groupby.html#pandas.Series.groupby and https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby, but IMO most important is linking to the analogous `Series`/`DataFrame` doc which will usually be more informative.
Some groupby methods like `backfill` and `quantile` (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.quantile.html) do include custom See Also sections, but `Series.quantile` and `DataFrame.quantile` do not render as links. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42407 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42407/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42407/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42407/events | https://github.com/pandas-dev/pandas/pull/42407 | 938,027,651 | MDExOlB1bGxSZXF1ZXN0Njg0NDg1NjMx | 42,407 | CLN: random state updates after dropping numpy 1.17 | {
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} | 2 | "2021-07-06T15:39:44Z" | "2021-07-06T20:04:35Z" | "2021-07-06T19:44:37Z" | MEMBER | null | Followup to random state changes now that numpy 1.17 dropped
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https://api.github.com/repos/pandas-dev/pandas/issues/42408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42408/events | https://github.com/pandas-dev/pandas/issues/42408 | 938,030,231 | MDU6SXNzdWU5MzgwMzAyMzE= | 42,408 | BUG: to_sql() method inserts NULL for NaN which bypasses DEFAULT behaviour of database | {
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```
my_database_field varchar(255) NOT NULL
DEFAULT 'NVT'
```
However, when I try to insert data into this table using DataFrame.to_sql() I get the following error:
```
Cannot insert the value NULL into column 'my_database_field', table 'MYDB.dbo.my_table'; column does not allow nulls. INSERT fails.
```
Here is the code:
```
with engine.begin() as conn:
dataframe.to_sql(table_name, conn, index=False, if_exists='append')
```
The column 'my_database_field' has NaN values:
```
data['my_database_field']
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
..
4154 NaN
4155 NaN
4156 NaN
4157 NaN
4158 NaN
```
This seems to be because the to_sql method is replacing NaN values with NULLs (probably how it should be)
I want the option to insert NaN values and have the database use the DEFAULT value defined in the schema instead of inserting NULL directly when using the to_sql method. | {
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} | 4 | "2021-07-06T15:48:22Z" | "2021-07-06T21:33:52Z" | "2021-07-06T21:33:52Z" | NONE | null | #### Location of the documentation
"https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.to_latex.html"
#### Documentation problem
Last example under "CSS Conversion" which uses the `convert_css` parameter writes `css_convert` instead.
#### Suggested fix for documentation
Simply replace `css_convert` with `convert_css`
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We still use `pkg_resources` in mpl tests. But it doesn't affect end users. | {
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- [ ] 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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} | 1 | "2021-07-06T16:51:35Z" | "2021-07-06T19:25:55Z" | "2021-07-06T19:09:50Z" | MEMBER | null | - [x] closes #42381
- [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 | "2021-07-06T17:34:31Z" | "2021-07-06T18:27:22Z" | "2021-07-06T18:27:12Z" | NONE | null | #### Is your feature request related to a problem?
The idea is basically do same operations to a list of pandas objects, Series or DataFrames.
#### Describe the solution you'd like
There a a lot of time I do following operations
```python
df1.sum().plot()
df2.sum().plot()
df3.sum().plot()
```
I wish in pandas I could do following:
```python
dfs = MetaDf(df1, df2, df3)
dfs.sum().plot()
```
#### API breaking implications
This should not affect APIs.
#### Describe alternatives you've considered
I tried something here: https://github.com/wangzhe3224/compare
But it does not work well with chaining operations.
```python
import pandas as pd
from compare.core import Comp
a = pd.Series([1,2,3])
b = pd.Series([1,2,3])
c = Comp(a, b)
c.shift(1)
c.sum()
# we cannot do this yet...
# c.mean() + c.std()
# c.plot()
```
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} | 3 | "2021-07-06T19:44:22Z" | "2021-07-06T21:43:51Z" | "2021-07-06T21:33:52Z" | CONTRIBUTOR | null |
- [x] closes #42409
- [ ] 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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