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```
from asv_bench.benchmarks.frame_ctor import *
self3 = FromNDArray()
self3.setup()
%timeit self3.time_frame_from_ndarray()
19.8 µs ± 405 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
30.9 µs ± 1.38 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
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} | 3 | "2021-09-17T19:48:53Z" | "2021-09-29T13:30:54Z" | "2021-09-29T13:30:54Z" | NONE | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/dev/user_guide/visualization.html#colormaps
### Documentation problem
The first paragraph of the Colormaps section finishes with
> A visualization of the default matplotlib colormaps is available [here](https://matplotlib.org/2.0.2/examples/color/colormaps_reference.html).
Following that URL gets you to a page with a red banner warning
> You are reading an old version of the documentation (v2.0.2). For the latest version see https://matplotlib.org/stable/
### Suggested fix for documentation
Shouldn't the documentation point to the [stable color examples](https://matplotlib.org/stable/gallery/index.html#color) or perhaps more appropriately to the [tutorial Choosing colormaps in Matplotlib](https://matplotlib.org/stable/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py) | {
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} | 1 | "2021-09-17T20:22:45Z" | "2021-09-17T21:35:30Z" | "2021-09-17T21:35:27Z" | CONTRIBUTOR | null | I believe this should link https://pandas.pydata.org/pandas-docs/version/1.2.0/reference/api/pandas.json_normalize.html in https://pandas.pydata.org/docs/reference/api/pandas.io.json.read_json.html#pandas.io.json.read_json
`json_normalize` is great and it may help people working with JSON data who are likely to have an entry point of the `read_json` page | {
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} | 1 | "2021-09-17T21:06:07Z" | "2021-09-18T01:10:21Z" | "2021-09-17T23:46:23Z" | MEMBER | null | Makes it so we no longer need `indexers` in `JoinUnit` (and in fact, so that JoinUnit is only used for axis==1)
```
asv continuous -E virtualenv -f 1.01 master HEAD --append-samples --record-samples -b join_merge
[...]
before after ratio
[26064f0f] [5dedf170]
<master> <ref-concat-axis0>
- 15.8±0.9ms 15.0±0.4ms 0.95 join_merge.MergeAsof.time_on_int('backward', None)
- 16.8±0.4ms 15.9±0.3ms 0.94 join_merge.MergeAsof.time_on_int('forward', None)
- 19.6±0.9ms 18.5±0.2ms 0.94 join_merge.MergeAsof.time_on_int('nearest', None)
- 16.1±0.4ms 15.1±0.3ms 0.94 join_merge.MergeAsof.time_on_int('backward', 5)
- 17.8±0.9ms 16.7±0.4ms 0.94 join_merge.MergeAsof.time_on_int32('backward', None)
- 84.4±6ms 78.6±2ms 0.93 join_merge.MergeAsof.time_by_object('backward', 5)
- 83.1±3ms 77.3±2ms 0.93 join_merge.MergeAsof.time_by_object('backward', None)
- 20.1±1ms 18.6±0.5ms 0.93 join_merge.MergeAsof.time_on_uint64('nearest', 5)
- 20.2±0.8ms 18.7±0.3ms 0.93 join_merge.MergeAsof.time_on_int('nearest', 5)
- 17.5±1ms 16.2±0.3ms 0.92 join_merge.MergeAsof.time_on_int('forward', 5)
- 22.3±2ms 20.5±0.4ms 0.92 join_merge.MergeAsof.time_on_int32('nearest', 5)
- 18.2±0.4ms 16.8±0.4ms 0.92 join_merge.MergeAsof.time_on_int32('backward', 5)
- 21.8±1ms 19.9±0.4ms 0.91 join_merge.MergeAsof.time_on_int32('nearest', None)
- 19.0±0.9ms 17.2±0.3ms 0.91 join_merge.MergeAsof.time_on_int32('forward', None)
- 17.6±1ms 15.9±0.6ms 0.90 join_merge.MergeAsof.time_on_uint64('forward', 5)
- 335±10ms 300±9ms 0.89 join_merge.Merge.time_merge_dataframes_cross(False)
- 334±7ms 299±10ms 0.89 join_merge.Merge.time_merge_dataframes_cross(True)
- 19.4±2ms 17.2±1ms 0.89 join_merge.Join.time_join_dataframe_index_multi(True)
- 17.1±1ms 15.1±0.2ms 0.88 join_merge.MergeAsof.time_on_uint64('backward', None)
- 10.3±1ms 8.97±0.4ms 0.87 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(True)
- 19.9±0.7ms 17.4±0.4ms 0.87 join_merge.MergeAsof.time_on_int32('forward', 5)
- 18.6±1ms 15.9±0.2ms 0.86 join_merge.MergeAsof.time_on_uint64('forward', None)
- 8.44±1ms 7.20±0.7ms 0.85 join_merge.Join.time_join_dataframe_index_single_key_small(False)
- 1.98±0.09s 1.58±0.04s 0.80 join_merge.JoinIndex.time_left_outer_join_index
- 9.80±0.8ms 7.61±0.8ms 0.78 join_merge.Join.time_join_dataframe_index_single_key_bigger(False)
- 9.39±0.5ms 7.25±0.4ms 0.77 join_merge.Concat.time_concat_small_frames(1)
- 221±20ms 170±4ms 0.77 join_merge.MergeCategoricals.time_merge_cat
- 439±10ms 300±9ms 0.68 join_merge.MergeCategoricals.time_merge_object
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43627/events | https://github.com/pandas-dev/pandas/pull/43627 | 999,723,549 | PR_kwDOAA0YD84r6M1L | 43,627 | CLN: remove unused sparse reindex | {
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} | 1 | "2021-09-17T21:10:40Z" | "2021-09-17T23:03:33Z" | "2021-09-17T23:03:30Z" | MEMBER | null | The `BlockIndex` reindex method wasn't even returning anything, which raised suspicions :)
(reindexing actually dispatches through take) | {
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] | open | false | null | [] | null | 1 | "2021-09-17T21:52:36Z" | "2021-10-02T16:13:55Z" | null | MEMBER | null | Currently the constructor includes `sparse_index`, which is not publicly exposed (nor is there a publicly exposed function to make one). Because of this, I'd like to suggest deprecating `sparse_index`.
One related issue is that without `sparse_index` (or a preexisting `SparseArray`), there is no way to use the constructor to construct a `SparseArray` without using dense data (which seems to defeat the purpose of using a `SparseArray`.
I'd like to replace `sparse_index` with the parameters `indices` and `length` (giving equivalent functionality to what `sparse_index` provided). `indices` would refer to array locations with actual values, `length` would be length of the `SparseArray` (I'm not attached to these names if people prefer others, these just match naming in existing `SparseArray` code).
This would allow constructing something sparse without first constructing a dense array, for example
`arr = SparseArray([1, 2], indices=[5, 11], length=1_000_000)`
or
`arr = SparseArray(1, indices=[1000, 2000, 4000, 9000], length=1_000_000)`
without needing to materialize an array of length `1_000_000` | {
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] | open | false | null | [] | null | 5 | "2021-09-18T02:14:44Z" | "2021-10-09T14:29:29Z" | null | NONE | null | #### Is your feature request related to a problem?
I would like to be able to do arithmetic operations on intervals:
- `a + b`:
`pd.Interval(3, 6) + pd.Interval(5, 7) == pd.Interval(3, 7)`
- `a - b`:
`pd.Interval(3, 6) - pd.Interval(5, 7) == pd.Interval(3, 5)`
- `[a, b].union() / np.sum([a, b])`:
```python
intervals = pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 3), (2, 4), (5, 7)])
intervals.sum() == pd.arrays.IntervalArray.from_tuples([(0, 4), (5, 7)])
```
#### API breaking implications
None AFAIK, since those methods are not implemented yet
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} | 1 | "2021-09-18T02:54:08Z" | "2021-09-18T12:24:05Z" | "2021-09-18T12:24:05Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
In [1]: import pandas as pd
In [2]: res = pd.cut(pd.Series([10, 20]), [10, 20], labels=['a'])
In [3]: res
Out[3]:
0 NaN
1 a
dtype: category
Categories (1, object): ['a']
In [4]: excepted = pd.Series([float('nan'), 'a'], dtype='category')
In [5]: excepted
Out[5]:
0 NaN
1 a
dtype: category
Categories (1, object): ['a']
In [6]: res.equals(excepted)
Out[6]: False
In [7]: res == excepted
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-3cea8b164d71> in <module>
----> 1 res == excepted
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
67 other = item_from_zerodim(other)
68
---> 69 return method(self, other)
70
71 return new_method
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\arraylike.py in __eq__(self, other)
30 @unpack_zerodim_and_defer("__eq__")
31 def __eq__(self, other):
---> 32 return self._cmp_method(other, operator.eq)
33
34 @unpack_zerodim_and_defer("__ne__")
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\series.py in _cmp_method(self, other, op)
5500
5501 with np.errstate(all="ignore"):
-> 5502 res_values = ops.comparison_op(lvalues, rvalues, op)
5503
5504 return self._construct_result(res_values, name=res_name)
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\ops\array_ops.py in comparison_op(left, right, op)
268 ):
269 # Call the method on lvalues
--> 270 res_values = op(lvalues, rvalues)
271
272 elif is_scalar(rvalues) and isna(rvalues):
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
67 other = item_from_zerodim(other)
68
---> 69 return method(self, other)
70
71 return new_method
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\arrays\categorical.py in func(self, other)
143 msg = "Categoricals can only be compared if 'categories' are the same."
144 if not self._categories_match_up_to_permutation(other):
--> 145 raise TypeError(msg)
146
147 if not self.ordered and not self.categories.equals(other.categories):
TypeError: Categoricals can only be compared if 'categories' are the same.
In [8]: res.cat.categories == excepted.cat.categories
Out[8]: array([ True])
```
### Issue Description
The categorical data comparison behavior is weird.
I want to check the result of `pd.cut` method is right or not, so I create a new one from `pd.Series` with `dtype='category'`.
They are looked the same, base on **In [3]** and **In [5]**.
TypeError message shows their categories are not the same. But **In [8]** means their categories are equal.
I also consider the `NaN` could cause that, but the same problem happens.
```python
In [9]: res.dropna() == excepted.dropna()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-08dae9fc1d53> in <module>
----> 1 res.dropna() == excepted.dropna()
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
67 other = item_from_zerodim(other)
68
---> 69 return method(self, other)
70
71 return new_method
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\arraylike.py in __eq__(self, other)
30 @unpack_zerodim_and_defer("__eq__")
31 def __eq__(self, other):
---> 32 return self._cmp_method(other, operator.eq)
33
34 @unpack_zerodim_and_defer("__ne__")
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\series.py in _cmp_method(self, other, op)
5500
5501 with np.errstate(all="ignore"):
-> 5502 res_values = ops.comparison_op(lvalues, rvalues, op)
5503
5504 return self._construct_result(res_values, name=res_name)
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\ops\array_ops.py in comparison_op(left, right, op)
268 ):
269 # Call the method on lvalues
--> 270 res_values = op(lvalues, rvalues)
271
272 elif is_scalar(rvalues) and isna(rvalues):
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
67 other = item_from_zerodim(other)
68
---> 69 return method(self, other)
70
71 return new_method
C:\Software\miniconda3\envs\work\lib\site-packages\pandas\core\arrays\categorical.py in func(self, other)
143 msg = "Categoricals can only be compared if 'categories' are the same."
144 if not self._categories_match_up_to_permutation(other):
--> 145 raise TypeError(msg)
146
147 if not self.ordered and not self.categories.equals(other.categories):
TypeError: Categoricals can only be compared if 'categories' are the same.
In [10]: res.dropna().equals(excepted.dropna())
Out[10]: False
```
### Expected Behavior
I think `res.equals(excepted)` should return `True` and `res == excepted` should return `pd.Series([False, True])`.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : Chinese (Simplified)_China.936
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.4
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.2.5
hypothesis : None
sphinx : None
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 : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : 3.0.8
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.23
tables : None
tabulate : None
xarray : 0.19.0
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43632 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43632/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43632/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43632/events | https://github.com/pandas-dev/pandas/pull/43632 | 999,891,786 | PR_kwDOAA0YD84r6t40 | 43,632 | REF: implement mask_setitem_value | {
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] | closed | false | null | [] | null | 2 | "2021-09-18T03:36:51Z" | "2021-11-17T00:47:38Z" | "2021-11-17T00:47:32Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
cc @phofl I think this is what is needed to get #40380 working (https://github.com/pandas-dev/pandas/pull/40380#discussion_r680289744), could use your input when you have a chance.
This only does the masking for the non-tuple case. Need to figure out if we can construct test cases that require the masking with tuples. | {
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Fixes: Fixed a chart visualization coiormap section link that linked to an outdated version of matplotlib. Now links to a [stable version](https://matplotlib.org/stable/gallery/color/colormap_reference.html#sphx-glr-gallery-color-colormap-reference-py).
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} | 17 | "2021-09-18T09:24:34Z" | "2021-09-24T11:32:45Z" | "2021-09-23T16:22:27Z" | CONTRIBUTOR | null | - [x] closes #41596
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
```python
In [1]: import numpy as np
...: ...: import pandas as pd
...: ...: pd.__version__
...: ...:
...: ...: cols = list('abcdefghjkl')
...: ...: df = pd.DataFrame(np.random.randint(0, 100, size=(100, len(cols))), columns=cols)
...: ...: df_str = df.astype(str)
...: ...: df_string = df.astype('string')
In [2]: %timeit df_str.groupby('a')[cols[1:]].last()
2.88 ms ± 70.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [3]: %timeit df_string.groupby('a')[cols[1:]].last()
3.27 ms ± 12.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) #PR
54.4 ms ± 244 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) #master | {
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} | 0 | "2021-09-18T14:32:06Z" | "2021-10-18T23:15:56Z" | "2021-10-18T23:15:56Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
>>> df = DataFrame([[1], [2]])
>>> print(df.style.hide_index([0]).to_latex())
\begin{tabular}{lr}
& 0 \\
\\
1 & 2 \\
\end{tabular}
```
### Issue Description
its the header cells that render as empty spaces creating an unwanted line.
### Expected Behavior
no unwanted line.
### Installed Versions
master
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- [x] tests added / passed
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} | 9 | "2021-09-18T17:40:12Z" | "2021-09-21T16:51:33Z" | "2021-09-21T12:46:35Z" | MEMBER | null | Based on the logging in #43611, in both timeout cases, the last test `gw0` ran was before the hypothesis test in `test_parse_dates` (this is a giant test - 56 parameterizations, hypothesis does 100 examples by default).
Not sure why this would be the cause, couldn't find any issues about it that might explain a deadlock or something like that, but maybe this will help? Regardless of fixing timeouts, it makes sense for these to be slow
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} | 0 | "2021-09-18T17:44:49Z" | "2021-10-18T23:15:56Z" | "2021-10-18T23:15:56Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
midx = MultiIndex.from_product([["A", "B"], ["a", "b"], ["X", "Y"]], names=["zero", "one", "two"])
cidx = MultiIndex.from_product([["a", "b"], ["C", "D"], ["V", "W"]], names=["zero", "one", "two"])
df = DataFrame(np.arange(64).reshape(8,8), index=midx, columns=cidx)
styler = Styler(df)
styler.hide_index(level=1).hide_columns(level=0)
styler.hide_index([("A", "a", "X"), ("A", "b", "X")])
styler.hide_columns([("a", "C", "W"), ("a", "D", "V")])
styler
```
### Issue Description
When using a MultiIndex and combining with hiding different levels and hiding specific rows/columns, some bugs appear.
Output is quite strange on second row for HTML output.
Also using LaTeX output:
```
>>> styler.to_latex()
\begin{tabular}{llrrrrrr}
& one & C & D & \multicolumn{2}{r}{C} & \multicolumn{2}{r}{D} \\
& two & V & W & V & W & V & W \\
zero & two & & & & & & \\
\multirow[c]{2}{*}{A} & & Y & 8 & 11 & 12 & 13 & 14 & 15 \\
& & Y & 24 & 27 & 28 & 29 & 30 & 31 \\
\multirow[c]{4}{*}{B} & \multirow[c]{2}{*}{} & X & 32 & 35 & 36 & 37 & 38 & 39 \\
& & Y & 40 & 43 & 44 & 45 & 46 & 47 \\
& \multirow[c]{2}{*}{} & X & 48 & 51 & 52 & 53 & 54 & 55 \\
& & Y & 56 & 59 & 60 & 61 & 62 & 63 \\
\end{tabular}
```
The above is a LaTeX error but if the number of columns is expanded to 9 then it renders like:
### Expected Behavior
Clearly we want to align all the datapoints and have the hidden levels correctly hidden.
### Installed Versions
master
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] | closed | false | null | [] | null | 1 | "2021-09-18T18:56:35Z" | "2021-09-19T19:02:23Z" | "2021-09-19T19:02:23Z" | NONE | null | I would like to perform the task called rolling aggregate with lag... Starting with this dataframe
import pandas as pd
import numpy as np
df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
index = [pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03'),
pd.Timestamp('20130101 09:00:05'),
pd.Timestamp('20130101 09:00:06')])
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 2.0
2013-01-01 09:00:05 NaN
2013-01-01 09:00:06 4.0
I would need to perform
df.rolling("3s excluding first 1s").max() # or more precisely df.rolling("(-3s,-1s]").max()
#closed on -1s, open on -3s
My real application involved extremely large dataframe where timestamp also had no constant interval and I also had id. So my real application is really groupby rolling aggregate with lag. (shift approach/merge_as_of approach won't work in my case...)
The expected result is
B
2013-01-01 09:00:00 NaN
2013-01-01 09:00:02 0.0
2013-01-01 09:00:03 1.0
2013-01-01 09:00:05 2.0
2013-01-01 09:00:06 NaN
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} | 5 | "2021-09-18T19:29:29Z" | "2021-09-22T17:14:09Z" | "2021-09-21T21:37:42Z" | CONTRIBUTOR | null | - [x ] closes #43540
- [x ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x ] whatsnew entry
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} | 0 | "2021-09-18T20:05:49Z" | "2021-09-21T13:57:25Z" | "2021-09-21T13:57:25Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
df = DataFrame([[0], [1]], index=[0, 1])
styler = df.style
styler.applymap(lambda x: "color: red;" if x == 0 else "")
styler.applymap_index(lambda x: "color: blue;" if x == 0 else "")
print(styler.to_latex(convert_css=True))
\begin{tabular}{lr}
& 0 \\
\colorblue 0 & \color{red} 0 \\
1 & 1 \\
\end{tabular}
```
### Issue Description
the new method applymap_index has not been hooked up to the CSS conversion for styler.to_latex.
### Expected Behavior
should convert to `color{blue}`
### Installed Versions
master | {
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} | 2 | "2021-09-18T20:45:11Z" | "2021-10-19T05:31:54Z" | "2021-10-18T23:15:56Z" | CONTRIBUTOR | null | #### Follows #43686
- [x] closes #43637
- [x] closes #43644
- [x] closes #43703
- [x] tests added / passed
- [x] whatsnew entry (note part of the bug seems to have been created by hiding `levels` which is a new feature in 1.4.0).
This PR has two parts:
1) Addressing the bug fixes as per above.
The key change made here was to add a single line: `if r not in self.hidden_rows:` in the loop, and in `_translate_latex` update for the new object structure that is input to it.
2) Refactoring the code so that the main part of the code is now easier to grok:
```
def _translate(...):
# calls _translate_header()
# callls _translate_body()
def _translate_header(...):
# calls _generate_col_header_row()
# calls _generate_index_names_row()
def _translate_body(...):
# calls _generate_trimmed_row()
# calls _generate_body_row()
```
And tests were update and comprehensive tests for the issues added. | {
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When trying to figure out azure timeout issues, deadlock appeared to be occurring in parser code, so `pyarrow` makes sense as the culprit. Seems like tests with weird input cause issues, for example some of the `parse_dates` tests, or for a specific reproducer the test:
`pandas/tests/io/parser/common/test_ints.py::test_outside_int64_uint64_range`
On current `pyarrow` I can't reproduce, but azure uses 0.17.0, with which can reproduce a deadlock (just running the command `pandas/tests/io/parser/common/test_ints.py::test_outside_int64_uint64_range`) on macOS. Doesn't happen consistently, but will deadlock (to the point that need to sigkill to stop, which explains why `pytest-timeout` didn't catch it).
cc @lithomas1 if any thoughts here | {
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} | 4 | "2021-09-18T21:31:25Z" | "2021-11-11T03:16:07Z" | null | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-09-18T23:23:10Z" | "2021-09-20T15:49:26Z" | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
cython supports [line coverage](https://cython.readthedocs.io/en/latest/src/tutorial/profiling_tutorial.html#enabling-coverage-analysis) but pyx files are currently not included in the codecov reports. Having code coverage for cython code might help to remove unused cython code.
#### Describe the solution you'd like
pyx files are included in the codecov report. | {
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} | 2 | "2021-09-19T01:18:48Z" | "2021-09-29T16:39:56Z" | "2021-09-29T13:11:21Z" | MEMBER | null | Perf-neutral on the case that motivated implementing ExtensionIndex.searchsorted (#38083)
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} | 2 | "2021-09-19T03:02:14Z" | "2021-09-21T16:51:15Z" | "2021-09-21T14:08:48Z" | MEMBER | null | This helps some on #41023 because `__getitem__` uses `take` internally for `SparseArray`. However, the big gain there can follow after this to move towards not densifying on `__getitem__` with a slice
Benchmarks:
```
before after ratio
[e7e7b407] [4de1fe27]
<master> <perf_sparse_take>
- 72.7±6μs 38.8±4μs 0.53 sparse.Take.time_take(array([0]), False)
- 1.00±0.06ms 350±4μs 0.35 sparse.Take.time_take(array([-1, -1, -1, ..., -1, -1, -1]), False)
- 737±20μs 206±6μs 0.28 sparse.Take.time_take(array([ 0, 1, 2, ..., 99997, 99998, 99999]), False)
``` | {
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- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
'''
Create the following dataframe "df":
a b
----
1 4
1 2
2 5
2 1
'''
df = pd.DataFrame({'a': [1, 1, 2, 2], 'b': [4, 2, 5, 1]})
def f(x):
# Insert a new column "c" and sort each sub-dataframe "x" according to values in "c"
x['c'] = x['b'].tolist()
return x.sort_values('c')
df = df.groupby('a').apply(f).reset_index(drop=True)
'''
I got an unexpected dataframe "df":
a b c
-------
1 2 2
1 4 4
1 2 2
1 4 4
'''
```
### Issue Description
As shown in the reproducible example, when I use groupby and apply to insert a new column to the sub-dataframe of each group and sort the sub-dataframe according to the new column, I got a wrong result.
It seems to me that the sub-dataframe of the first group was simply duplicated. I feel this is a bug, but please correct me if I am wrong. Thank you so much for your help! I would really appreciate it!
### Expected Behavior
The expected dataframe "df" should look like:
```python-traceback
a b c
-------
1 2 2
1 4 4
2 1 1
2 5 5
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Aug 31 20:53:32 PDT 2020; root:xnu-4903.278.44~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 0.9999999
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 5.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 2.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.0.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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no whats new since this is a new feature for 1.4.0 anyway.
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} | 1 | "2021-09-19T08:01:34Z" | "2021-09-21T14:09:22Z" | "2021-09-21T13:57:25Z" | CONTRIBUTOR | null | - [x] closes #43648
no whats new needed since `apply_index` is new to 1.4.0 and this just makes it more complete.
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} | 1 | "2021-09-19T09:21:40Z" | "2021-09-21T12:44:21Z" | "2021-09-21T12:14:18Z" | CONTRIBUTOR | null | - [X] closes #43586
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} | 2 | "2021-09-19T10:30:38Z" | "2021-09-27T23:34:23Z" | "2021-09-27T23:34:23Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
arrays = [
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
s = pd.Series(np.random.randn(8), index=index)
s.rename(index={'one': 'yes'}, level='second', errors='raise')
```
### Issue Description
`rename` does never find index values when using the `level` parameter to replace. When adding `errors='raise'`, we get the following error:
```
Traceback (most recent call last):
File "/Users/bustawin/repos/venvs/bicing/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-15-54b8de4a3511>", line 1, in <module>
s.rename(index={'one': 'yes'}, level=1, errors='raise')
File "/Users/bustawin/repos/venvs/bicing/lib/python3.9/site-packages/pandas/core/series.py", line 4518, in rename
return super().rename(
File "/Users/bustawin/repos/venvs/bicing/lib/python3.9/site-packages/pandas/core/generic.py", line 1154, in rename
raise KeyError(f"{missing_labels} not found in axis")
KeyError: "['one'] not found in axis"
```
The example dataframe is taken from the [multi-index guide](https://pandas.pydata.org/docs/user_guide/advanced.html#creating-a-multiindex-hierarchical-index-object).
### Expected Behavior
The above should have produced the following dataframe:
```
first second
bar yes 0.220890
two -1.497161
baz yes 0.589343
two -0.604643
foo yes 0.090448
two 0.373825
qux yes 1.366876
two 1.460868
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.0.0
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.08.1
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.3.24
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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] | open | false | null | [] | null | 2 | "2021-09-19T16:30:43Z" | "2021-11-12T00:03:30Z" | null | CONTRIBUTOR | null | - [x] closes #43652
Trying to include cython codes in codecov reports.
Working with the CI for the first time.
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} | 1 | "2021-09-19T17:59:21Z" | "2021-09-20T15:24:43Z" | "2021-09-20T13:18:24Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-09-19T19:33:51Z" | "2021-10-11T15:51:20Z" | "2021-10-10T17:35:44Z" | CONTRIBUTOR | null | No one has requested this but I think its nice none-the-less, especially in finance / science. Kind of like background_gradient with slightly different vizualisations.
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] | open | false | null | [] | null | 1 | "2021-09-19T19:59:10Z" | "2021-10-01T22:43:30Z" | null | NONE | null | #### Is your feature request related to a problem?
With fillna (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html), the options are only front fill or back fill. In some instances, though, you might want to do an "inner" fill--meaning only fill between the first valid value and the last valid value.
For example, let's consider the data frame below. Let's imagine a store that was closed on weekends (so they never took inventory) and only sold two products. Product A was limited release, so we want to differentiate between 0 inventory (implying that it would come back) and NaN inventory (meaning it isn't actually sold).
|Date|Product A|Product B|
| --- | --- | --- |
|8/30/2021|NaN|20|
|8/31/2021|NaN|18|
|9/01/2021|25|17|
|9/02/2021|20|14|
|9/03/2021|14|13|
|9/04/2021|NaN|NaN|
|9/05/2021|NaN|NaN|
|9/06/2021|9|11|
|9/07/2021|3|9|
|9/08/2021|NaN|5|
|9/09/2021| NaN|0|
Now, let's imagine we want to fill in prices for weekends, but, if we do front fill, it will fill in a 3 for 9/8/2021 and 9/9/2021, which does not make sense. If we do back fill, we would fill in values for 8/30 and 8/31, which also doesn't make sense.
Ideally, we'd be able to do something like a front/back fill, but select "inner_only=True", to get the resulting dataframe of:
|Date|Product A|Product B|
| --- | --- | --- |
|8/30/2021|NaN|20|
|8/31/2021|NaN|18|
|9/01/2021|25|17|
|9/02/2021|20|14|
|9/03/2021|14|13|
|9/04/2021|14|13|
|9/05/2021|14|13|
|9/06/2021|9|11|
|9/07/2021|3|9|
|9/08/2021|NaN|5|
|9/09/2021| NaN|0|
#### Describe the solution you'd like
New parameter: inner_only
Docstring: Will perform the requested fill operation, but only between the first and last valid elements in the column.
#### API breaking implications
No API breaking implementations
#### Describe alternatives you've considered
Have written some workarounds for this--essentially getting the iloc of the first and last elements, doing the fill operation on the results only in between those, then replacing back to the dataframe.
```python
def inner_fill_df(df, fill_method="ffill", cols=[]):
if not cols or not len(cols):
cols = list(df.columns)
for col_name in cols:
first_valid_index = df.index.get_loc(df[col_name].first_valid_index())
last_valid_index = df.index.get_loc(df[col_name].last_valid_index())
if not last_valid_index:
last_valid_index = len(df[col_name])
df[col_name].iloc[first_valid_index:last_valid_index] = df[col_name].iloc[first_valid_index:last_valid_index].fillna(method=fill_method)
return df
```
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} | 0 | "2021-09-19T20:25:24Z" | "2021-09-28T23:39:12Z" | "2021-09-28T23:39:12Z" | CONTRIBUTOR | null | The following is not standardized in a PEP but is [generally agreed on by multiple type checkers](https://github.com/pytorch/pytorch/issues/47027#issuecomment-780898127) and is mentioned in the [scope of stub files in PEP484](https://www.python.org/dev/peps/pep-0484/#stub-files). See https://github.com/pytorch/pytorch/issues/47027#issuecomment-719036333 for more pointers. Pytorch (in [__init__.py](https://github.com/pytorch/pytorch/blob/88032d894311e5c0aed8bbc21a4306bc6be4af82/torch/__init__.py#L678)) and numpy (in [__init__.pyi](https://github.com/numpy/numpy/blob/df0b1bd75f0e8489912863d1f9dfde1846a7bf51/numpy/__init__.pyi#L198)) are two of the projects that already try to use redundant imports.
#### Is your feature request related to a problem?
When pandas becomes a py.typed library (#28142), static type checks need to know what is part of the public API and what isn't. PEP 484 outlines that imported modules are only part of the public API, if they follow the pattern of "import X as X" (for stub files). Pyright implemented this not only for stub files but also for "normal" python code (see https://github.com/microsoft/pyright/blob/main/docs/typed-libraries.md#library-interface).
Edit: mypy also complains about that when `implicit_reexport=false`
#### Describe the solution you'd like
Use redundant imports to signal what is part of the public API.
While this is not standardized for normal python code, pandas has/will have many `.pyi` files for its cython code (which is subject to PEP 484). It is probably more consistent to use redundant imports in the entire codebase, not only in `.pyi` files.
#### API breaking implications
Until pandas is a py.typed library, it would be good to have a clear statement what takes precedence to define the public API (in case there are mismatches between the documentation, import statements, or type annotations).
#### Additional context
When creating an empty py.typed file in the pandas directory and using pyright to validate the following code:
```python
import pandas as pd
pd.DataFrame()
```
`error: "DataFrame" is not exported from module "pandas" (reportPrivateImportUsage)`
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} | 1 | "2021-09-19T22:10:37Z" | "2021-09-20T16:59:13Z" | "2021-09-20T14:36:30Z" | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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An method with validation logic that should be in `__init__` ideally and should not be called by users. | {
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} | 9 | "2021-09-20T06:25:54Z" | "2021-10-21T00:50:26Z" | "2021-10-21T00:50:22Z" | CONTRIBUTOR | null | - [x] closes #43584
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What's new: On any instance of RangeIndex, sort_values will not sort as it is already sorted. | {
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} | 6 | "2021-09-20T06:45:26Z" | "2021-09-28T01:12:36Z" | "2021-09-27T23:34:23Z" | CONTRIBUTOR | null | - [x] closes #43659
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} | 1 | "2021-09-20T09:49:31Z" | "2021-11-13T17:03:31Z" | "2021-11-13T17:03:31Z" | MEMBER | null | The `DataFrame.convert_dtypes` method returns the same dataframe (with potentially updated dtypes in certain columns), but because of the internal use of `concat`, it doesn't necessarily preserve the class type of the dataframe for subclasses.
Noticed this in GeoPandas: https://github.com/geopandas/geopandas/issues/1870
This stems from the fact that the dataframe is basically decomposed into columns (Series), and then those are combined again into a DataFrame with `concat`. But at that point, you are concatting Series objects, and concat doesn't know anymore about the original dataframe class. Personally, I would say that the use of `concat` here is an implementation detail, and that `convert_dtypes` could easily preserve the original class of the calling dataframe.
Small reproducer without geopandas:
```python
class SubclassedDataFrame(DataFrame):
@property
def _constructor(self):
return SubclassedDataFrame
In [51]: df = SubclassedDataFrame({'a': [1, 2, 3]})
In [52]: type(df)
Out[52]: __main__.SubclassedDataFrame
In [53]: type(df.convert_dtypes())
Out[53]: pandas.core.frame.DataFrame
```
Note I am not using `pd._testing.SubclassedDataFrame` since for this subclass, each column is also a SubclassesSeries, and then `concat` will actually preserve the subclass. | {
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} | 2 | "2021-09-20T10:56:18Z" | "2021-09-23T00:15:27Z" | "2021-09-23T00:15:27Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
pd.date_range("01.01.2021", periods=4, freq='H', tz='Europe/Berlin')
pd.date_range("01.01.2021", periods=4, freq='H', tz='Etc/GMT+1')
```
### Issue Description
I use the date_range function to create a series of time values with time zone information. Since I don't want the DST, I do not use the Berlin time zone, but the equivalent GMT+1 zone. Comparing the results of both function calls, it seems that the time offset sign of the GMT is inverted. This leads to different results for above function calls, which is not correct in my opinion.
### Expected Behavior
I expect the same results for both function calls. But the time zone of GMT+1 is different from the time zone of Berlin.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : AMD64 Family 23 Model 1 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.3.3
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.54.0
</details>
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} | 6 | "2021-09-20T18:25:46Z" | "2021-10-19T05:32:03Z" | "2021-10-18T23:15:03Z" | CONTRIBUTOR | null | - [x] adds the last remaining options for #41395 for the existing args in Styler.to_latex.
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- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
@mzeitlin11 Any help you can provide on what tests I should add, or where to start digging for similar `GroupBy` tests would be appreciated. As stated in PR #41399, I'm not super familiar with Cython, so hoping that the previous contributors (stale) PR is close to what's needed (I suppose the tests will tell us).
Thanks!
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} | 8 | "2021-09-20T19:03:24Z" | "2021-09-25T15:37:51Z" | "2021-09-25T15:37:17Z" | CONTRIBUTOR | null | Run pyright in strict mode with a very long list of rules that need to be ignored. The idea is to enable more rules over time.
pre-commit seamlessly installs pyright (depends on node.js) but running pyright as part of the pre-commit slows the pre-commit down by ~85s.
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} | 9 | "2021-09-20T19:25:30Z" | "2021-09-23T14:42:43Z" | "2021-09-22T12:36:52Z" | CONTRIBUTOR | null | - [x] closes #43619
- [x] tests added / passed (but canceled due to another error)
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I couldn't run all tests, because of the following problem, which isn't related to my changes:
```
pandas/tests/io/test_orc.py terminate called after throwing an instance of 'orc::TimezoneError'
what(): Can't open /etc/localtime
Fatal Python error: Aborted
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} | 1 | "2021-09-20T22:58:46Z" | "2021-09-21T14:50:05Z" | "2021-09-21T12:48:26Z" | MEMBER | null | ```
from asv_bench.benchmarks.groupby import *
self = GroupByMethods()
self.setup("object", "any", "direct", 10)
%timeit self.time_dtype_as_field("object", "any", "direct", 10)
1.12 ms ± 60.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
11.4 ms ± 49.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
``` | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
Database / Linux_py37_IO (ci/deps/actions-37-db-min.yaml) (pull_request) is the offending job, taking up to 6 hours to resolve dependencies. Let's find out why.
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} | 5 | "2021-09-21T00:23:04Z" | "2021-09-23T00:04:48Z" | "2021-09-22T13:10:25Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
Hopefully, this makes the slow Database CI that takes 6+ hours go faster(most of the time is in the resolve environments step). Also turns on the database builds for commits to 1.3.x which was left out due to an oversight.
Unfortunately, this doesn't work for posix since the env doesn't resolve with strict channel priority. | {
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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_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
The goal is to provide an implementation for apply on lists. See https://github.com/pandas-dev/pandas/issues/41112#issuecomment-902366002 for why this necessitates a change to agg behavior for lists. It is possible to break this up into several PRs, but wanted to give a view as to what it would appear in totality.
Attached is a notebook demonstrating the differences in behavior (github doesn't allow attaching the notebook directly).
[udfs.pdf](https://github.com/pandas-dev/pandas/files/7199652/udfs.pdf)
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} | 3 | "2021-09-21T03:33:06Z" | "2021-09-21T20:35:57Z" | "2021-09-21T19:21:57Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
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} | 1 | "2021-09-21T03:57:50Z" | "2021-09-21T10:21:47Z" | "2021-09-21T10:21:47Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
data1 = {"TIMES": [3, 5, 2, 100, 20]}
df1 = pd.DataFrame(data=data1)
print("===The original dataframe===")
print(df1)
df1["RATIO"] = df1["TIMES"]/df1["TIMES"].sum()
df1["PERCENT"] = df1["RATIO"].apply(lambda x: format(x, ".2%"))
df1 = df1.sort_values(by="RATIO", ascending=True)
print("===The dataframe sort by RATIO===")
print(df1)
df1 = df1.sort_values(by="PERCENT", ascending=True)
print("===The dataframe sort by PERCENT===")
print(df1)
```
### Issue Description
This is what the code out. And when the dataframe is sorted by RATIO , everything is OK .But, when it comes to be sorted by PERCENT, something is wrong, and the sorted column is not correct as you can see below.
===The original dataframe===
TIMES
0 3
1 5
2 2
3 100
4 20
===The dataframe sort by RATIO===
TIMES RATIO PERCENT
2 2 0.015385 1.54%
0 3 0.023077 2.31%
1 5 0.038462 3.85%
4 20 0.153846 15.38%
3 100 0.769231 76.92%
===The dataframe sort by PERCENT===
TIMES RATIO PERCENT
2 2 0.015385 1.54%
4 20 0.153846 15.38%
0 3 0.023077 2.31%
1 5 0.038462 3.85%
3 100 0.769231 76.92%
### Expected Behavior
I expect when the dataframe is sorted by PERCENT, it looks as same as sorted by RATIO.Like this:
===The original dataframe===
TIMES
0 3
1 5
2 2
3 100
4 20
===The dataframe sort by RATIO===
TIMES RATIO PERCENT
2 2 0.015385 1.54%
0 3 0.023077 2.31%
1 5 0.038462 3.85%
4 20 0.153846 15.38%
3 100 0.769231 76.92%
===The dataframe sort by PERCENT===
TIMES RATIO PERCENT
2 2 0.015385 1.54%
0 3 0.023077 2.31%
1 5 0.038462 3.85%
4 20 0.153846 15.38%
3 100 0.769231 76.92%
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-19041-Microsoft
Version : #1237-Microsoft Sat Sep 11 14:32:00 PST 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : 4.10.0
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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https://api.github.com/repos/pandas-dev/pandas/issues/43681 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43681/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43681/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43681/events | https://github.com/pandas-dev/pandas/issues/43681 | 1,002,416,036 | I_kwDOAA0YD847v6ek | 43,681 | BUG: groupby/apply reuses the index which causes weird behavior | {
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] | open | false | null | [] | null | 2 | "2021-09-21T12:49:03Z" | "2021-10-01T22:52:16Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas
df = pandas.DataFrame(index=list(range(10)), columns=list("ABC"), data=1)
l = []
print(df.groupby(df.index < 4).apply(lambda col: l.append(col.index) or id(col.index)))
print(l)
sr = pandas.Series(index=list(range(10)), data=1)
l = []
print(sr.groupby(sr.index < 4).apply(lambda col: l.append(col.index) or id(col.index)))
print(l[0])
print(l[1])
df = pandas.DataFrame(index=list(range(10)), columns=list("ABC"), data=1)
l = []
print(df.groupby(df.index < 4).apply(lambda col: l.append(col.eval("index*2")) or id(col.index)))
print(l[0])
print(l[1])
print(len(l[0]),l[0].shape)
```
### Issue Description
The result for the Dataframe is quite puzzling as it appears the index of the column sent to the lambda is reused/mutated across the different calls of the groupby/apply (while this does not happend on the Series). I think the values object is also shared.
Even worse, when using the column to generate new Series, we see that as the index is shared, we end up with a Series (`l[0]` in the example) which a shape that is not consistent with the length of the Series.
```
False 2116454933648
True 2116454933648
dtype: int64
[Int64Index([0, 1, 2, 3], dtype='int64'), Int64Index([0, 1, 2, 3], dtype='int64')]
False 2116454934224
True 2116454934464
dtype: int64
Int64Index([4, 5, 6, 7, 8, 9], dtype='int64')
Int64Index([0, 1, 2, 3], dtype='int64')
False 2116454933456
True 2116454933456
dtype: int64
0 8
1 10
2 12
3 14
16
18
dtype: int64
0 0
1 2
2 4
3 6
dtype: int64
4 (6,)
```
### Expected Behavior
I would expect a similar result as for Series, ie having a different index/values object on each call of the lambda function for the Dataframe.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18363
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : French_Belgium.1252
pandas : 1.3.2
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.0
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
fsspec : 2021.08.1
fastparquet : 0.7.1
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.17.0
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-09-21T16:40:03Z" | "2021-09-21T18:03:03Z" | null | CONTRIBUTOR | null | Create a property function to decide what to do for EAs in `_ea_wrap_cython_operation` for groupby ops.
xref : https://github.com/pandas-dev/pandas/pull/43634#discussion_r712140219
https://github.com/pandas-dev/pandas/blob/439acc99b2968ec8062eee05af7346acea20915b/pandas/core/groupby/ops.py#L318-L333 | {
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} | 1 | "2021-09-21T17:24:45Z" | "2021-09-21T20:35:18Z" | "2021-09-21T19:20:52Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_methods import *
self3 = Dropna()
self3.setup("all", 0)
%timeit self3.time_dropna('all', 0)
125 ms ± 3.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
34.8 ms ± 269 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
``` | {
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} | 0 | "2021-09-21T18:28:55Z" | "2021-09-22T14:38:44Z" | "2021-09-22T13:13:02Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 0 | "2021-09-21T19:19:26Z" | "2021-09-21T22:11:48Z" | "2021-09-21T22:11:48Z" | CONTRIBUTOR | null | > > /home/runner/work/pandas/pandas/doc/source/whatsnew/v1.4.0.rst:372: WARNING: Inline interpreted text or phrase reference start-string without end-string.
/home/runner/work/pandas/pandas/doc/source/whatsnew/v1.4.0.rst:372: WARNING: Inline interpreted text or phrase reference start-string without end-string.
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} | 7 | "2021-09-21T19:33:07Z" | "2021-10-16T16:37:07Z" | "2021-10-16T16:13:29Z" | CONTRIBUTOR | null | Bit of a pre cursor to some other cleaning in #43649.
Prevents needing to pass variables around in worker functions, and has the added benefit of making the CSS classes user editable, by storing as a class attribute instead of module constants.
I ran the updated styler ASVs for this and "BENCHMARKS NOT SIGNIFICANTLY CHANGED" | {
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} | 1 | "2021-09-21T21:07:01Z" | "2021-09-22T00:45:31Z" | "2021-09-21T22:11:48Z" | MEMBER | null | - [x] closes #43685
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} | 1 | "2021-09-21T21:13:43Z" | "2021-10-01T13:16:09Z" | null | NONE | null | ### Reproducible Example
```python
import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
df = pd.DataFrame({'int_array_col': [[[1,2,3]], [[4,5,6]]]})
df.to_parquet('/tmp/test', engine='pyarrow')
pandas_parquet_table = pq.read_table('/tmp/test')
pyarrow_table = pa.Table.from_pandas(df)
writer = pa.BufferOutputStream()
pq.write_table(
pyarrow_table,
writer,
use_compliant_nested_type=True
)
reader = pa.BufferReader(writer.getvalue())
parquet_table = pq.read_table(reader)
print("Pandas:", pandas_parquet_table.schema.types)
print("Non-compliant Parquet:", pyarrow_table.schema.types)
print("Compliant Parquet:", parquet_table.schema.types)
assert pandas_parquet_table.schema.types == pyarrow_table.types
assert pandas_parquet_table.schema.types == parquet_table.schema.types
```python-traceback
Pandas: [ListType(list<item: list<item: int64>>)]
Non-compliant Parquet: [ListType(list<item: list<item: int64>>)]
Compliant Parquet: [ListType(list<element: list<element: int64>>)]
Traceback (most recent call last):
File "/Users/judahrand/test_dir/pandas_parquet.py", line 25, in <module>
assert pandas_parquet_table.schema.types == parquet_table.schema.types
AssertionError
```
### Issue Description
This method currently does not write adherent Parquet Logical Types for nested arrays as defined [here](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#nested-types). This can cause problems when trying to Parquet as in intermediate format, for example loading data into BigQuery which expects adherent data.
This was an issue in PyArrow itself, however, it was fixed in [ARROW-11497](https://github.com/apache/arrow/pull/9489). I believe that this flag should be set in Pandas if we are to claim that Pandas `.to_parquet()` method actually outputs Parquet.
### Expected Behavior
Output complaint Parquet.
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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_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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} | 0 | "2021-09-21T23:03:58Z" | "2021-09-22T01:55:29Z" | "2021-09-22T01:09:31Z" | MEMBER | null | Arbitrary EA-backed Indexes (e.g. Nullable) will not go down that path. | {
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] | open | false | null | [] | null | 9 | "2021-09-21T23:41:37Z" | "2021-10-01T22:52:50Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
# Series: Result 608202560.0
pd.to_numeric(pd.Series([608202549]), downcast='float')
#Series: Result 608202560.0
pd.to_numeric(pd.Series([608202549.0]), downcast='float')
# Single: Result 608202549
pd.to_numeric(608202549, downcast='float')
# List: Result array([608202560, 608202560])
pd.to_numeric([608202548, 608202549], downcast='float').astype(int)
# Range: 1st issue at 16777217
arr = np.arange(0, 6e8, dtype='int64')
a = pd.Series(arr)
b = pd.to_numeric(a, downcast='float').astype(int)
np.where(a != b)[0]
```
### Issue Description
When attempting to use `to_numeric()` on a Pandas Series or list with values >= 16777217, the resulting float is not expected given the input number.
```python
pd.to_numeric(pd.Series([608202549]), downcast='float')
0 608202560.0
>>> pd.to_numeric(pd.Series([608202549.0]), downcast='float')
0 608202560.0
```
This behavior does not occur if the value is passed as a scalar, but does also appear if a List is passed. By comparing values within a range, it seems the issue starts from 16,777,217 onward. This seems to have roots in the discussion about 32 bit floating point in this [Stack Overflow](https://github.com/pandas-dev/pandas/issues/23676#issuecomment-438488603) as well as another Pandas issue on float issues with rank #18271
**Edit-** I updated this issue to be clear that this is affecting both integers and floats larger than 16,777,217
### Expected Behavior
`to_numeric()` should yield the same functional number in float form as the original value(s)
### Installed Versions
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.4.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:27 PDT 2021; root:xnu-7195.141.2~5/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 54.2.0
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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} | 0 | "2021-09-21T23:47:46Z" | "2021-09-22T01:54:49Z" | "2021-09-22T01:10:42Z" | MEMBER | null | ```
from asv_bench.benchmarks.groupby import *
self4 = GroupByMethods()
self4.setup('uint', 'count', 'direct', 5)
%timeit self4.time_dtype_as_field('uint', 'count', 'direct', 5)
231 µs ± 5.23 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master
77.3 µs ± 619 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
``` | {
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} | 13 | "2021-09-22T01:48:27Z" | "2021-09-28T23:39:17Z" | "2021-09-28T23:39:12Z" | CONTRIBUTOR | null | - [x] closes #43664
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-09-22T03:49:33Z" | "2021-09-22T14:38:19Z" | "2021-09-22T13:10:57Z" | MEMBER | null | ```
from asv_bench.benchmarks.series_methods import *
self6 = NSort()
self6.setup("last")
%timeit self6.time_nsmallest('last')
10.6 ms ± 43.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
1.5 ms ± 17.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
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] | open | false | null | [] | null | 1 | "2021-09-22T10:20:41Z" | "2021-10-01T22:53:23Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
x = pd.Series([(1,3),(2,2),(3,1)])
# This succeeds as the max operation is performed on the first element of a tuple
assert x.max() == (3,1)
# This fails with `TypeError: reduction operation 'argmax' not allowed for this dtype`
assert x.idxmax() == 2
# Using numpy directly works as you'd expect
import numpy as np
assert np.argmax(x.values) == 2
```
### Issue Description
Using `max` on a series of tuples returns the tuple as if the max operation was performed on the first element of each tuple.
Using `idxmax` however, raises an operation not allowed TypeError.
### Expected Behavior
Even though there are several ways of achieving the desired result, I would expect consistency between the usage of `max` and `idxmax`, as it is for `max` and `argmax` in numpy.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1113-azure
Version : #126~16.04.1-Ubuntu SMP Tue Apr 13 16:55:24 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.06.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 1 | "2021-09-22T14:30:48Z" | "2021-11-16T13:38:04Z" | null | MEMBER | null | Closes #24877
This is a work-in-progress to see if it's realistic to require `ExtensionArray.astype` to always return an ExtensionArray (in practice this means returning a PandasArray if now a numpy array would be returned).
Further we will also need to see how this impacts external EAs, and how this change can be managed.
(datetimelike and categorical arrays were not yet updated) | {
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- [x] closes #36263
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 0 | "2021-09-22T19:34:18Z" | "2021-09-30T19:46:48Z" | "2021-09-30T19:46:48Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import numpy as np
import pandas as pd
rng = np.random.default_rng(42)
data = pd.Series(rng.uniform(size=(10, 2)).dot([1, 1j]))
gr = data.groupby(data.index % 2)
gr.mean() # TypeError: No matching signature found
```
### Issue Description
`gr.mean()` raises `TypeError: No matching signature found` when the underlying dtype is `complex128`. The other complex dtypes present the same behavior.
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/goyo/.local/lib/python3.9/site-packages/pandas/core/groupby/groupby.py", line 1684, in mean
result = self._cython_agg_general(
File "/home/goyo/.local/lib/python3.9/site-packages/pandas/core/groupby/generic.py", line 352, in _cython_agg_general
result = array_func(objvals)
File "/home/goyo/.local/lib/python3.9/site-packages/pandas/core/groupby/generic.py", line 340, in array_func
result = self.grouper._cython_operation(
File "/home/goyo/.local/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 978, in _cython_operation
return cy_op.cython_operation(
File "/home/goyo/.local/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 639, in cython_operation
return self._cython_op_ndim_compat(
File "/home/goyo/.local/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 481, in _cython_op_ndim_compat
res = self._call_cython_op(
File "/home/goyo/.local/lib/python3.9/site-packages/pandas/core/groupby/ops.py", line 557, in _call_cython_op
func(result, counts, values, comp_ids, min_count)
File "pandas/_libs/groupby.pyx", line 668, in pandas._libs.groupby.__pyx_fused_cpdef
TypeError: No matching signature found
```
### Expected Behavior
Since `data.mean()` works I would expect that it also works in the `GroupBy` object. The expected result is:
```python
>>> gr.mean()
0 0.438939+0.550293j
1 0.652316+0.653820j
dtype: complex128
```
Just like `.sum()`:
```python
>>> gr.sum() # this does work.
0 2.194697+2.751466j
1 3.261581+3.269100j
dtype: complex128
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-34-generic
Version : #36-Ubuntu SMP Thu Aug 26 19:22:09 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : None
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.8
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : None
</details>
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} | 1 | "2021-09-22T19:57:03Z" | "2021-09-22T22:16:49Z" | "2021-09-22T22:00:24Z" | MEMBER | null | This is never called outside of tests so not a big deal, but the code is redundant and in one place returns the wrong type. | {
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} | 0 | "2021-09-22T19:58:02Z" | "2021-10-18T23:15:56Z" | "2021-10-18T23:15:56Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
pd.options.styler.render.max_rows = 2
df = DataFrame(np.random.randn(5,1))
df.style.hide_index(df.index[:2])
```
### Issue Description
This does not render any rows since it loops through the first 2 rows, which are hidden, but then doesn't progress the loop passed index 2.
### Expected Behavior
Hidden rows should be deducted from the row count. The dataframe should show rows 3 and 4 above.
### Installed Versions
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] | null | 5 | "2021-09-22T20:01:31Z" | "2021-11-05T13:08:39Z" | "2021-11-05T13:08:39Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
pd.options.styler.render.max_columns = 2
df = DataFrame(np.random.randn(1,5))
df.style.hide_columns(df.columns[:2])
```
### Issue Description
The render is empty since the first two columns are looped but hidden, the loop index does not progress passed 2.
This is a similar issue to #43703 but recorded separately since requires different fixes.
### Expected Behavior
Render columns 3 and 4 with a trimmed column
### Installed Versions
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} | 0 | "2021-09-22T21:07:32Z" | "2021-09-23T16:17:35Z" | "2021-09-23T12:21:18Z" | MEMBER | null | Makes a big difference for Float64Index with integer key.
```
import numpy as np
import pandas as pd
idx = pd.Index(np.arange(10**7))
fidx = idx.astype('f8')
key = idx[len(idx) // 2]
key2 = idx[10]
%timeit idx.get_loc(key)
2.97 µs ± 36.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- master
2.3 µs ± 36.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
%timeit idx.get_loc(key2)
3.2 µs ± 151 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- master
2.1 µs ± 140 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) # <- PR
%timeit fidx.get_loc(key)
56.4 µs ± 1.22 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
2.11 µs ± 11.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
%timeit fidx.get_loc(key2)
54.7 µs ± 1.12 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
2 µs ± 21.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <- PR
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} | 4 | "2021-09-23T00:43:23Z" | "2021-09-27T23:32:45Z" | "2021-09-27T23:32:42Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-09-23T00:58:11Z" | "2021-09-23T10:47:39Z" | "2021-09-23T10:47:39Z" | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
s1 = pd.Series(['2020-01-01 00:00:00'])
s2 = pd.to_datetime(s1)
pd.testing.assert_series_equal(s1,s2,check_dtype=False)
```
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/nfs/vjawa/pandas/pandas/_testing/asserters.py", line 1078, in assert_series_equal
_testing.assert_almost_equal(
File "pandas/_libs/testing.pyx", line 53, in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
File "pandas/_libs/testing.pyx", line 168, in pandas._libs.testing.assert_almost_equal
raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
File "/home/nfs/vjawa/pandas/pandas/_testing/asserters.py", line 666, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series are different
Series values are different (100.0 %)
[index]: [0]
[left]: [2020-01-01 00:00:00]
[right]: <DatetimeArray>
['2020-01-01 00:00:00']
Length: 1, dtype: datetime64[ns]
```
### Issue Description
#### BUG: assert_series_equal b/w datetime column with equivalent object column fails with `check_dtype=False`
When trying to compare two series where one is an object and the other is an equivalent date-time created using `pd.to_datetime` , we get a unexpected `AssertionError`.
### Expected Behavior
I would expect that we pass the assertion.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 36d5c9b8e521fea564cdc44491253f9d9c903fe9
python : 3.9.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+745.g36d5c9b8e5
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : 0.29.24
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 : 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 | "2021-09-23T01:16:05Z" | "2021-09-29T13:30:19Z" | "2021-09-29T13:30:18Z" | NONE | null | ###
- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.rename.html?highlight=rename#pandas.DataFrame.rename
### Documentation problem
In the document, the developers have suggested a simple way to rename the columns. However, I think we should add a sentence to point out that the rename will only give out a new data frame rather than directly rename the old data frame.
### Suggested fix for documentation
In the document, the developers have suggested a simple way to rename the columns. However, I think we should add a sentence to point out that the rename will only give out a new data frame rather than directly rename the old data frame. | {
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} | 3 | "2021-09-23T03:06:36Z" | "2021-09-26T00:38:24Z" | "2021-09-23T12:12:16Z" | MEMBER | null | - [x] closes #43357
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] whatsnew entry
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- [x] tests added / passed
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This PR prevents issues where float64 arrays would be downcast to float32 arrays even if the values were out of range for float32. An equivalence check is performed to ensure all values are equal to 7 significant digits. If this fails, the original float64 is retained. This behavior is consistent with int where downcasting is not performed if the integer is out of bounds.
Pre Change:
```python
>>> pd.to_numeric(pd.Series([608202549.0]), downcast='float')
0 608202560.0
```
Post Change:
```python
>>> pd.to_numeric(pd.Series([608202549.0]), downcast='float')
0 608202549.0
``` | {
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} | 5 | "2021-09-23T05:12:45Z" | "2021-09-28T23:23:37Z" | "2021-09-28T22:55:20Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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The tests file will need to be rebased following #43705. | {
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} | 1 | "2021-09-23T09:37:44Z" | "2021-09-23T10:51:24Z" | "2021-09-23T10:51:24Z" | NONE | null | I wish that there is "non-inplace" mode for `DataFrame.insert`. Since we have inplace argument for `DataFrame.drop`, with is a dual operator to `DataFrame.insert`.
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} | 0 | "2021-09-23T10:02:23Z" | "2021-10-02T00:05:04Z" | "2021-10-02T00:05:04Z" | CONTRIBUTOR | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [X] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import io
import numpy as np
import pandas as pd
DATA = """\
id\tnum\n
1\t1.2 \n
1\t 2.1\n
2\t 1 \n
2\t 1.2 \n
"""
df = pd.read_csv(
io.StringIO(DATA),
float_precision="round_trip", skipinitialspace=True,
sep="\t", header=0,
dtype={1: np.float64},
)
print(df)
```
### Issue Description
```read_csv(..., float_precision="round_trip", )``` does parse fields with initial/trailing space and raises and error
```
Traceback (most recent call last):
File "pandas/_libs/parsers.pyx", line 1141, in pandas._libs.parsers.TextReader._convert_tokens
false_values=false_values)
TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/ales/.config/JetBrains/PyCharmCE2021.2/scratches/scratch_2.py", line 14, in <module>
df = pd.read_csv(
File "/home/ales/.envs/orange3/lib/python3.9/site-packages/pandas/io/parsers.py", line 686, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/ales/.envs/orange3/lib/python3.9/site-packages/pandas/io/parsers.py", line 458, in _read
data = parser.read(nrows)
File "/home/ales/.envs/orange3/lib/python3.9/site-packages/pandas/io/parsers.py", line 1196, in read
ret = self._engine.read(nrows)
File "/home/ales/.envs/orange3/lib/python3.9/site-packages/pandas/io/parsers.py", line 2155, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 847, in pandas._libs.parsers.TextReader.read
cdef:
File "pandas/_libs/parsers.pyx", line 862, in pandas._libs.parsers.TextReader._read_low_memory
else:
File "pandas/_libs/parsers.pyx", line 941, in pandas._libs.parsers.TextReader._read_rows
self.parser.line_fields[i] + \
File "pandas/_libs/parsers.pyx", line 1073, in pandas._libs.parsers.TextReader._convert_column_data
col_res, na_count = self._convert_with_dtype(
File "pandas/_libs/parsers.pyx", line 1149, in pandas._libs.parsers.TextReader._convert_tokens
ValueError: cannot safely convert passed user dtype of float64 for object dtyped data in column 1
```
### Expected Behavior
```float_precision="round_trip"``` should work the same as other float parsers.
### Installed Versions
<details>
```
INSTALLED VERSIONS
------------------
commit : e218f059caaf5d921c4c71b7a7889adfe6c74b9d
python : 3.9.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.14.2-1-MANJARO
Version : #1 SMP PREEMPT Wed Sep 8 14:11:01 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+497.ge218f059ca
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 56.0.0
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.21.6
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
```
</details>
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} | 3 | "2021-09-23T10:28:58Z" | "2021-10-02T00:05:11Z" | "2021-10-02T00:05:04Z" | CONTRIBUTOR | null | - [x] closes #43713
- [x] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [X] whatsnew entry
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} | 9 | "2021-09-23T10:31:57Z" | "2021-10-17T17:08:53Z" | "2021-10-17T17:08:53Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
pip install pandas (installs 1.3.3)
```
### Issue Description
I tried to install on 3.10.0rc2
```
ultiarray\scalartypes.obj
scalartypes.c
numpy\core\include\numpy/npy_3kcompat.h(227): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\common.h(239): warning C4244: 'return': conversion from 'npy_uintp' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(425): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(426): warning C4267: '=': conversion from 'size_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(1899): warning C4244: 'function': conversion from 'npy_double' to 'npy_float', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(1902): warning C4244: 'function': conversion from 'npy_double' to 'npy_float', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2577): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2561): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2577): warning C4244: '=': conversion from 'Py_ssize_t' to 'int', possible loss of data
numpy\core\src\multiarray\scalartypes.c.src(2967): error C2440: 'function': cannot convert from 'double' to 'PyObject *'
numpy\core\src\multiarray\scalartypes.c.src(2967): warning C4024: '_Py_HashDouble': different types for formal and actual parameter 1
numpy\core\src\multiarray\scalartypes.c.src(2967): error C2198: '_Py_HashDouble': too few arguments for call
numpy\core\src\multiarray\scalartypes.c.src(2976): error C2440: 'function': cannot convert from 'double' to 'PyObject *'
numpy\core\src\multiarray\scalartypes.c.src(2976): warning C4024: '_Py_HashDouble': different types for formal and actual parameter 1
numpy\core\src\multiarray\scalartypes.c.src(2975): error C2198: '_Py_HashDouble': too few arguments for call
numpy\core\src\multiarray\scalartypes.c.src(2982): error C2440: 'function': cannot convert from 'double' to 'PyObject *'
numpy\core\src\multiarray\scalartypes.c.src(2982): warning C4024: '_Py_HashDouble': different types for formal and actual parameter 1
numpy\core\src\multiarray\scalartypes.c.src(2981): error C2198: '_Py_HashDouble': too few arguments for call
numpy\core\src\multiarray\scalartypes.c.src(2967): error C2440: 'function': cannot convert from 'npy_longdouble' to 'PyObject *'
numpy\core\src\multiarray\scalartypes.c.src(2967): warning C4024: '_Py_HashDouble': different types for formal and actual parameter 1
numpy\core\src\multiarray\scalartypes.c.src(2967): error C2198: '_Py_HashDouble': too few arguments for call
numpy\core\src\multiarray\scalartypes.c.src(2976): error C2440: 'function': cannot convert from 'npy_longdouble' to 'PyObject *'
numpy\core\src\multiarray\scalartypes.c.src(2976): warning C4024: '_Py_HashDouble': different types for formal and actual parameter 1
numpy\core\src\multiarray\scalartypes.c.src(2975): error C2198: '_Py_HashDouble': too few arguments for call
numpy\core\src\multiarray\scalartypes.c.src(2982): error C2440: 'function': cannot convert from 'npy_longdouble' to 'PyObject *'
numpy\core\src\multiarray\scalartypes.c.src(2982): warning C4024: '_Py_HashDouble': different types for formal and actual parameter 1
numpy\core\src\multiarray\scalartypes.c.src(2981): error C2198: '_Py_HashDouble': too few arguments for call
numpy\core\src\multiarray\scalartypes.c.src(2997): error C2440: 'function': cannot convert from 'double' to 'PyObject *'
numpy\core\src\multiarray\scalartypes.c.src(2997): warning C4024: '_Py_HashDouble': different types for formal and actual parameter 1
numpy\core\src\multiarray\scalartypes.c.src(2997): error C2198: '_Py_HashDouble': too few arguments for call
numpy\core\src\multiarray\scalartypes.c.src(3913): warning C4244: 'return': conversion from 'npy_intp' to 'int', possible loss of data
```
### Expected Behavior
Works
### Installed Versions
Not relevant | {
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} | 2 | "2021-09-23T12:17:53Z" | "2021-09-23T18:36:07Z" | null | CONTRIBUTOR | null | ```
[ 47.57%] ···· For parameters: None, 'B'
/home/runner/work/pandas/pandas/asv_bench/benchmarks/tslibs/timestamp.py:58: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
self.ts = Timestamp("2017-08-25 08:16:14", tzinfo=tz, freq=freq)
For parameters: datetime.timezone.utc, 'B'
/home/runner/work/pandas/pandas/asv_bench/benchmarks/tslibs/timestamp.py:58: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
self.ts = Timestamp("2017-08-25 08:16:14", tzinfo=tz, freq=freq)
For parameters: datetime.timezone(datetime.timedelta(seconds=3600)), 'B'
/home/runner/work/pandas/pandas/asv_bench/benchmarks/tslibs/timestamp.py:58: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
self.ts = Timestamp("2017-08-25 08:16:14", tzinfo=tz, freq=freq)
For parameters: <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B'
/home/runner/work/pandas/pandas/asv_bench/benchmarks/tslibs/timestamp.py:58: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
self.ts = Timestamp("2017-08-25 08:16:14", tzinfo=tz, freq=freq)
For parameters: tzfile('/usr/share/zoneinfo/Asia/Tokyo'), 'B'
/home/runner/work/pandas/pandas/asv_bench/benchmarks/tslibs/timestamp.py:58: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
self.ts = Timestamp("2017-08-25 08:16:14", tzinfo=tz, freq=freq)
For parameters: tzlocal(), 'B'
/home/runner/work/pandas/pandas/asv_bench/benchmarks/tslibs/timestamp.py:58: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
self.ts = Timestamp("2017-08-25 08:16:14", tzinfo=tz, freq=freq)
[ 47.62%] ··· ...estamp.TimestampProperties.time_weekday_name ok
```
should fix the asv's
cc @jbrockmendel | {
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] | open | false | null | [] | null | 0 | "2021-09-23T14:10:56Z" | "2021-10-01T22:54:15Z" | null | NONE | null | ###
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
# resulting df has 2 rows and index [0, 1],
# when I believe it should have 3 and index [0, 1, 2]
```
### Issue Description
Maybe it's a matter of definition of _index alignment_, but I think `pd.DataFrame.insert` doesn't align the index of what is inserted properly. The documentation clearly says that inserting a `pd.Series` aligns its index (to the one of the dataframe, I assume, documentation is not clear and this is mentioned only next to the corresponding example). Anyway, **keys in the series not present in the dataframe are discarded** and this is also clearly depicted in the documentation, from where I've taken the example above.
I don't think this is correct and this is what is usually meant with _index alignment_. I think it should work more like `pd.concat(..., axis=1)` that I can't use because it seems to discard and recompute the index. But this is another story.
If this is the intended behavior, please clarify it the documentation and move the paragraph about it from the example section to the main documentation of the function.
### Expected Behavior
All the keys present in the `pd.Series` that is `insert`ed in the `pd.DataFrame` are all retained and in case some of them are not present in the dataframe, its index is extended and no-values are added where needed.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.7.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.08.1
fastparquet : 0.6.3
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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https://api.github.com/repos/pandas-dev/pandas/issues/43719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43719/events | https://github.com/pandas-dev/pandas/issues/43719 | 1,005,571,771 | I_kwDOAA0YD8477867 | 43,719 | BUG: Modifying values of a dataframe using lower level MultiIndex columns causes silently casting them to float64 | {
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] | open | false | null | [] | null | 1 | "2021-09-23T15:30:23Z" | "2021-10-01T22:55:48Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
import numpy as np
DTYPE = np.float32
# DTYPE = np.int32
def rand():
return (np.random.rand(5) * 10).astype(DTYPE)
def assert_dtype(d):
assert (d.dtypes == DTYPE).all(), f"\n{d.dtypes}"
df1 = pd.DataFrame({"x": rand(), "y": rand()})
assert_dtype(df1)
df1.loc[:, ["x", "y"]] = 2 * df1.loc[:, ["x", "y"]] - 1
assert_dtype(df1)
df2 = pd.DataFrame({("a", "x"): rand()})
assert_dtype(df2)
df2.loc[:, ("a", "x")] = 2 * df2.loc[:, ("a", "x")] - 1
assert_dtype(df2)
assert_dtype(df2.loc[:, "a"])
assert_dtype(2 * df2.loc[:, "a"] - 1)
df2.loc[:, "a"] = 2 * df2.loc[:, "a"] - 1
assert_dtype(df2) # FAILS
```
Output:
```
Traceback (most recent call last):
File "/home/omer/tmp/pd_err.py", line 30, in <module>
assert_dtype(df2) # FAILS
File "/home/omer/tmp/pd_err.py", line 11, in assert_dtype
assert (d.dtypes == DTYPE).all(), f"\n{d.dtypes}"
AssertionError:
a x float64
dtype: object
```
### Issue Description
When lower level MultiIndex columns are used for modifying a dataframe, they are cast to float64 even when destination and source have same types.
### Expected Behavior
dtype(s) of such assignments aren't cast if not needed.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 73c68257545b5f8530b7044f56647bd2db92e2ba
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.0-8-amd64
Version : #1 SMP Debian 5.10.46-4 (2021-08-03)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.3
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 20.3.4
setuptools : 44.1.1
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
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} | 0 | "2021-09-23T16:43:45Z" | "2021-10-21T05:15:26Z" | "2021-10-21T01:24:09Z" | CONTRIBUTOR | null | - [x] xref #41994 but provides an alternative workable solution.
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [x] No whatsnew entry needed since `apply_index` methods were added in 1.4.0 and this bug fix is an extension to that.
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} | 2 | "2021-09-23T17:05:39Z" | "2021-09-27T17:03:11Z" | "2021-09-23T18:42:16Z" | 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](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
Note: Env bellow is not the latest, but I also tested with 1.3.3 and behavior remains the same
- [ ] I have confirmed this bug exists on the master branch of pandas.
### Reproducible Example
```python
import pandas as pd
df = pd.DataFrame()
df['a'] = [1,2,3,4,5,6,7,8,None,None]
print(df['a'])
> Outputs:
0 1.0
1 2.0
2 3.0
3 4.0
4 5.0
5 6.0
6 7.0
7 8.0
8 NaN
9 NaN
Name: a, dtype: float64
```
### Issue Description
Pandas is replacing `None` with `NaN` these are no the same, this is horrible and will cause bugs
### Expected Behavior
I'd expected values I assign to an element or column in a pandas data frame to either:
1. Get assigned
2. Raise and error if they can't be assigned
### Installed Versions
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-34-generic
Version : #36~20.04.1-Ubuntu SMP Fri Aug 27 08:06:32 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.4
numpy : 1.21.2
pytz : 2019.3
dateutil : 2.7.3
pip : 20.0.2
setuptools : 57.4.0
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.23
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.53.1 | {
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