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https://api.github.com/repos/pandas-dev/pandas/issues/42821 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42821/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42821/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42821/events | https://github.com/pandas-dev/pandas/issues/42821 | 956,913,343 | MDU6SXNzdWU5NTY5MTMzNDM= | 42,821 | ENH: change default min_count value for pandas.core.resample.Resampler.sum() | {
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] | open | false | null | [] | null | 0 | "2021-07-30T16:45:00Z" | "2021-08-21T20:23:27Z" | null | NONE | null | #### Is your feature request related to a problem?
When using, pandas.core.resample.Resampler.sum(), the behavior I would expect based on using other resampler aggregation methods (e.g. mean, median,) is that if there is a gap in the time series values should be filled with NaNs, instead they are filled with 0's because the min_count parameter default is 0, I think it should be 1.
Furthermore,this could also be applied to the min() and max() Resampler aggregation methods. On a related note there seems to be an error in the documentation for the min() and max() methods. The source code shows the default value for the min_count parameter on min() and max() is 0, but the documentation says default is -1 (see: https://pandas.pydata.org/docs/reference/api/pandas.core.resample.Resampler.min.html )
#### Describe the solution you'd like
Change the default value for the min_count parameter to 1 for pandas.core.resample.Resampler.sum(), pandas.core.resample.Resampler.min() and pandas.core.resample.Resampler.max()
#### API breaking implications
Not sure if this would break things :/
#### Describe alternatives you've considered
An alternative would be to add a min_count parameter to other Resampler methods for consistency (e.g. mean, median, etc.)
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} | 0 | "2021-07-30T17:02:50Z" | "2021-07-31T00:46:15Z" | "2021-07-30T23:09:32Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 9 | "2021-07-30T17:51:58Z" | "2021-08-12T18:10:55Z" | "2021-08-11T12:55:42Z" | CONTRIBUTOR | null | - [x] closes #42501
- [x] tests added / passed
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- [x] whatsnew entry
Pass the provided copy argument through to the DataFrame constructor when constructing a DataFrame from a Series iterable, as is done with the other DataFrame construction call in the get_result method.
The new test in frame/methods/test_astype.py is as provided by the author of the issue with minor changes.
@TomAugspurger this updates your test `extension/decimal/test_decimal.py:test_astype_dispatches` to expect both frame and series astype calls to retain the context.prec value. The prior failure was caused by the copying of the DataFrame, and that copy no longer occurs. The underlying issue with copy and the dtype is unaddressed here. | {
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} | 5 | "2021-07-30T18:06:52Z" | "2021-08-28T21:12:28Z" | "2021-08-16T00:49:34Z" | MEMBER | null | Shaves a couple percent off of time_to_json_wide asv | {
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] | open | false | null | [] | null | 2 | "2021-07-30T18:42:32Z" | "2021-08-21T20:24:09Z" | null | CONTRIBUTOR | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html?highlight=loc#pandas.DataFrame.loc
#### Documentation problem
Surprisingly, the following code works:
```python
import pandas as pd
df = pd.DataFrame([["a", 1], ["b", 2], ["c", 3]], columns=["let", "num"]).set_index(
"let"
)
dfl = df.loc[set(["a", "b"]), :]
print(dfl)
dd = {"a": 10, "c": 12}
dfd = df.loc[dd, "num"]
print(dfd)
```
In the documentation, we don't say that you can pass a `set` or a `dict` as one of the arguments to `.loc` .
So either we update the documentation, or maybe we should be raising an Exception?
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} | 4 | "2021-07-30T19:01:21Z" | "2021-08-04T23:51:10Z" | "2021-08-04T23:51:06Z" | CONTRIBUTOR | null | This pull request adds DEGREESIGN (°) to tokens that are parsed and escaped in a backticked column. There are no separate issue reporting this, but this is similar to the EUROSIGN issue previously addressed.
- [ ] closes #xxxx
- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-07-31T17:21:49Z" | "2021-08-03T14:36:23Z" | "2021-08-02T10:54:41Z" | MEMBER | null | - [ ] closes #38703
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A possible step toward #35725. Internally, agg may use apply and apply may use agg. Straightening these out so that agg always uses agg and apply always uses apply before changing the behavior of agg (without impacting apply).
This PR emits FutureWarning but with (a) no alternative to not get the warning and (b) no clear way for users to know what the future result may be (it may not even change). The only alternative ways forward I see here are
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} | 1 | "2021-07-31T19:14:21Z" | "2021-08-10T19:57:17Z" | "2021-08-10T18:59:43Z" | MEMBER | null | - [x] closes #42738
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
we did this in 1.3 for DataFrame reductions and GroupBy reductions. AFAIK this is the last place we use this pattern.
cc @mroeschke
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} | 0 | "2021-07-31T19:31:29Z" | "2021-08-17T04:22:40Z" | "2021-08-08T03:35:50Z" | MEMBER | null | remove skipped mysql tests, simplify old sqlite tests
a small step towards #40686 | {
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] | open | false | null | [] | null | 4 | "2021-07-31T21:04:39Z" | "2021-08-21T20:25:03Z" | null | NONE | null | #### Is your feature request related to a problem?
I wish there was a way to preserve verticle screen space when I'm inspecting 'tall' data frames. These are data frames with many observaitons but few columns. Thus, they're _tall_. I use the word tall to avoid conflating with _long_ data.
#### Describe the solution you'd like
I believe this function accomplishes the goal:
```Python
def insp(df, n=5, parts='ht'):
if parts == 'ht':
display = pd.concat([df.head(n).reset_index().rename(columns={'index':'loc'}),
df.tail(n).reset_index().rename(columns={'index':'loc'})],
axis=1,
keys=['head','tail'])
if parts == 'hs':
sep='< head | sample >'
display = pd.concat([df.head(n).reset_index().rename(columns={'index':'loc'}),
df.sample(n).reset_index().rename(columns={'index':'loc'})],
axis=1,
keys=['head','sample'])
if parts == 'st':
sep='< head | sample >'
display = pd.concat([df.sample(n).reset_index().rename(columns={'index':'loc'}),
df.tail(n).reset_index().rename(columns={'index':'loc'})],
axis=1,
keys=['sample','tail'])
if parts == 'hst':
sep1 = '< head | sample >'
sep2 = '< sample | tail >'
display = pd.concat([df.head(n).reset_index().rename(columns={'index':'loc'}),
df.sample(n).reset_index().rename(columns={'index':'loc'}),
df.tail(n).reset_index().rename(columns={'index':'loc'})],
axis=1,
keys=['head','sample','tail'])
return(display)
```
#### API breaking implications
Not applicable and/or unsure.
#### Describe alternatives you've considered
So the above function works great. Try it out. Take a look over at this colab notebook that demonstrates:
https://colab.research.google.com/drive/1mcNLlG6RVbhoXGCMdVyUryxKSgcaRPNC?usp=sharing
But, I'd propose a new method. Something like `Pandas.DataFrame.inspect()` or `Pandas.DataFrame.insp()` - to compliment the `.head()` - `.sample()` - and `.tail()` methods.
#### Additional Context
Inspired by the first suggestion in this article:
https://towardsdatascience.com/pandas-hacks-that-i-wish-i-had-when-i-started-out-1f942caa9792
(See the first 'hack' of the three).
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} | 3 | "2021-07-31T23:31:25Z" | "2021-08-11T11:03:48Z" | "2021-08-10T21:45:16Z" | MEMBER | null | - [x] closes #42816
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 3 | "2021-08-01T06:56:22Z" | "2021-08-03T19:30:09Z" | "2021-08-03T17:15:14Z" | CONTRIBUTOR | null | - [x] closes #42101
- [x] closes #42190 (stale pr)
Fixes `hide_columns` so that the index header row is also hidden alongside column headers.
Can be back ported to 1.3.2
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] | open | false | null | [] | null | 1 | "2021-08-01T14:43:05Z" | "2021-08-21T20:25:41Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
dfx = pd.DataFrame({'a':[1,2,3], 'cat':['a', 'b', 'a']}).astype({'cat':'category'})
dfy = pd.DataFrame({'a':[3,4,5], 'cat':['a', 'a', 'a']}).astype({'cat':'category'}) # no 'b' here
dfz=pd.concat([dfx,dfy])
dfz['cat']
Out[55]:
0 a
1 b
2 a
3 a
4 a
5 a
Name: cat, dtype: object
```
#### Problem description
I am expecting (erroneously?) that resulting 'cat' column should be the merged categories of 'dfx' and 'dfy'.
#### Expected Output
```python
dfz['cat']
Out[58]:
0 a
1 b
2 a
3 a
4 a
5 a
Name: cat, dtype: category
Categories (2, object): ['a', 'b']
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-63-generic
Version : #71~20.04.1-Ubuntu SMP Thu Jul 15 17:46:08 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : 0.7.0
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : 0.18.2
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 6 | "2021-08-01T15:24:08Z" | "2021-08-19T14:27:59Z" | "2021-08-19T12:56:03Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
The idea is to incrementally move all of the relevant functions to operate block-wise, at which point we can also de-duplicate _get_cythonized_result with _cython_agg_general.
Fleshes out the asv, which currently checks only single-column, to measure ncols=1, 2, 5, 10. We take a performance hit on the ncols==1 case and improve all the others (ex some noise)
```
asv continuous -f 1.01 master HEAD -E virtualenv -b GroupByMethods --append-samples --record-samples
[...]
before after ratio
[a5f8c9a0] [8586317b]
<perf-from_records> <perf-gb>
+ 58.2±5μs 71.7±20μs 1.23 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation', 1)
+ 76.9±6μs 94.1±20μs 1.22 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct', 10)
+ 66.7±3μs 81.2±20μs 1.22 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation', 2)
+ 65.9±3μs 78.4±20μs 1.19 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'direct', 2)
+ 68.9±2μs 81.8±20μs 1.19 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct', 5)
+ 77.0±4μs 91.3±30μs 1.19 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation', 10)
+ 177±10μs 210±40μs 1.18 groupby.GroupByMethods.time_dtype_as_field('int', 'cumcount', 'transformation', 5)
+ 66.1±2μs 77.3±20μs 1.17 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct', 2)
+ 57.1±0.9μs 64.7±20μs 1.13 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'direct', 1)
+ 67.1±10μs 75.9±10μs 1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'direct', 5)
+ 65.4±3μs 72.6±20μs 1.11 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation', 2)
+ 56.7±1μs 62.8±20μs 1.11 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct', 1)
+ 77.2±2μs 85.4±20μs 1.11 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation', 10)
+ 66.0±10μs 73.0±10μs 1.11 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'direct', 2)
+ 78.2±4μs 86.4±20μs 1.10 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'direct', 10)
+ 20.8±0s 22.7±0s 1.09 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct', 10)
+ 69.3±2μs 75.6±20μs 1.09 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation', 5)
+ 76.6±10μs 82.6±20μs 1.08 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'direct', 10)
+ 105±20μs 113±30μs 1.08 groupby.GroupByMethods.time_dtype_as_field('float', 'max', 'direct', 5)
+ 13.4±0s 14.3±0s 1.07 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct', 10)
+ 59.4±3μs 63.4±20μs 1.07 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation', 1)
+ 70.2±2μs 74.6±20μs 1.06 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation', 5)
+ 20.9±0s 22.1±0s 1.06 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'transformation', 10)
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+ 13.2±0s 13.5±0.2s 1.03 groupby.GroupByMethods.time_dtype_as_group('uint', 'describe', 'direct', 10)
+ 13.2±0s 13.4±0.06s 1.02 groupby.GroupByMethods.time_dtype_as_group('uint', 'describe', 'transformation', 10)
- 438±100ms 431±4ms 0.98 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'transformation', 2)
- 621±100μs 588±20μs 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'transformation', 10)
- 2.39±0.3ms 2.27±0.04ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'rank', 'direct', 10)
- 635±100μs 598±10μs 0.94 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'direct', 10)
- 487±100μs 457±10μs 0.94 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'direct', 2)
- 196±60μs 182±4μs 0.93 groupby.GroupByMethods.time_dtype_as_group('uint', 'mean', 'direct', 2)
- 72.5±20μs 65.2±2μs 0.90 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummax', 'direct', 1)
- 320±50μs 287±10μs 0.90 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'direct', 10)
- 115±30μs 102±3μs 0.89 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'transformation', 1)
- 628±100μs 555±9μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct', 5)
- 164±40μs 145±3μs 0.88 groupby.GroupByMethods.time_dtype_as_group('object', 'tail', 'transformation', 1)
- 259±50μs 229±6μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'direct', 5)
- 300±60μs 265±10μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct', 5)
- 90.0±20μs 79.4±2μs 0.88 groupby.GroupByMethods.time_dtype_as_group('object', 'size', 'direct', 10)
- 174±40μs 153±2μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'transformation', 1)
- 744±100μs 655±20μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation', 10)
- 224±50μs 197±3μs 0.88 groupby.GroupByMethods.time_dtype_as_group('object', 'head', 'transformation', 10)
- 212±50μs 186±7μs 0.88 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'transformation', 2)
- 283±60μs 248±4μs 0.87 groupby.GroupByMethods.time_dtype_as_group('datetime', 'head', 'transformation', 2)
- 258±50μs 225±4μs 0.87 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'transformation', 5)
- 150±30μs 131±4μs 0.87 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummax', 'transformation', 10)
- 326±100μs 284±50μs 0.87 groupby.GroupByMethods.time_dtype_as_field('object', 'ffill', 'direct', 10)
- 214±50μs 186±3μs 0.87 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'direct', 2)
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- 123±30μs 106±4μs 0.86 groupby.GroupByMethods.time_dtype_as_group('uint', 'first', 'transformation', 1)
- 106±30μs 91.0±20μs 0.86 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'transformation', 2)
- 155±40μs 132±10μs 0.85 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummin', 'transformation', 10)
- 187±40μs 159±30μs 0.85 groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'transformation', 2)
- 262±70μs 223±3μs 0.85 groupby.GroupByMethods.time_dtype_as_group('uint', 'var', 'transformation', 5)
- 482±100μs 408±7μs 0.85 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'transformation', 10)
- 392±90μs 330±10μs 0.84 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'transformation', 5)
- 120±30μs 101±3μs 0.84 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct', 2)
- 120±40μs 100±2μs 0.83 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation', 2)
- 255±90μs 211±40μs 0.83 groupby.GroupByMethods.time_dtype_as_field('object', 'tail', 'transformation', 10)
- 119±30μs 98.6±20μs 0.83 groupby.GroupByMethods.time_dtype_as_field('datetime', 'count', 'transformation', 10)
- 124±30μs 101±20μs 0.82 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'direct', 10)
- 1.00±0.2ms 819±50μs 0.81 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'transformation', 10)
- 76.5±20μs 62.3±10μs 0.81 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummax', 'direct', 1)
- 409±90μs 332±5μs 0.81 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'transformation', 10)
- 399±90μs 324±9μs 0.81 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct', 2)
- 96.7±20μs 77.9±20μs 0.81 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummin', 'direct', 10)
- 83.5±20μs 67.2±10μs 0.81 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummax', 'direct', 5)
- 232±70μs 187±30μs 0.81 groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'transformation', 10)
- 156±50μs 125±2μs 0.80 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation', 10)
- 78.5±20μs 62.6±10μs 0.80 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummax', 'direct', 2)
- 85.0±20μs 67.7±10μs 0.80 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummin', 'transformation', 2)
- 83.9±20μs 66.8±10μs 0.80 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummax', 'transformation', 5)
- 112±30μs 88.8±2μs 0.79 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct', 1)
- 225±60μs 178±40μs 0.79 groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'direct', 10)
- 254±50μs 201±40μs 0.79 groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'direct', 10)
- 90.8±20μs 71.6±20μs 0.79 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummin', 'transformation', 5)
- 243±70μs 190±7μs 0.78 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'transformation', 1)
- 92.6±20μs 70.9±10μs 0.76 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummin', 'direct', 5)
- 236±60μs 180±40μs 0.76 groupby.GroupByMethods.time_dtype_as_field('object', 'cumcount', 'direct', 10)
- 4.31±1ms 3.29±0.6ms 0.76 groupby.GroupByMethods.time_dtype_as_group('int', 'quantile', 'transformation', 10)
- 382±100μs 289±50μs 0.76 groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'direct', 10)
- 515±200μs 388±10μs 0.75 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod', 'transformation', 10)
- 1.31±0.4ms 984±200μs 0.75 groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'transformation', 10)
- 135±40μs 102±20μs 0.75 groupby.GroupByMethods.time_dtype_as_field('float', 'sum', 'transformation', 5)
- 638±200μs 480±20μs 0.75 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct', 2)
- 251±70μs 189±30μs 0.75 groupby.GroupByMethods.time_dtype_as_field('object', 'count', 'direct', 10)
- 730±200μs 547±40μs 0.75 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation', 5)
- 819±300μs 612±20μs 0.75 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct', 10)
- 134±40μs 98.9±20μs 0.74 groupby.GroupByMethods.time_dtype_as_field('float', 'var', 'direct', 10)
- 647±200μs 478±10μs 0.74 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation', 2)
- 1.35±0.4ms 993±200μs 0.74 groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'direct', 10)
- 132±40μs 96.9±20μs 0.74 groupby.GroupByMethods.time_dtype_as_field('float', 'var', 'transformation', 10)
- 187±60μs 135±6μs 0.73 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumsum', 'direct', 2)
- 750±200μs 540±20μs 0.72 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'transformation', 2)
- 504±100μs 362±90μs 0.72 groupby.GroupByMethods.time_dtype_as_field('object', 'nunique', 'transformation', 10)
- 95.3±30μs 68.4±10μs 0.72 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummin', 'direct', 2)
- 90.4±30μs 64.7±10μs 0.72 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummin', 'direct', 1)
- 103±30μs 73.6±10μs 0.72 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummax', 'direct', 10)
- 115±40μs 76.3±10μs 0.66 groupby.GroupByMethods.time_dtype_as_field('uint', 'cummin', 'transformation', 10)
- 103±20μs 66.9±3μs 0.65 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumsum', 'transformation', 1)
- 339±70μs 120±30μs 0.35 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct', 2)
- 336±90μs 118±10μs 0.35 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct', 2)
- 339±70μs 116±30μs 0.34 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'transformation', 2)
- 314±60μs 107±20μs 0.34 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'transformation', 2)
- 335±80μs 108±6μs 0.32 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'direct', 2)
- 379±100μs 122±40μs 0.32 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'transformation', 2)
- 341±80μs 109±10μs 0.32 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'transformation', 2)
- 345±90μs 109±10μs 0.32 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'transformation', 2)
- 335±80μs 105±10μs 0.31 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'transformation', 2)
- 328±80μs 102±5μs 0.31 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct', 2)
- 338±90μs 105±5μs 0.31 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'direct', 2)
- 336±80μs 104±20μs 0.31 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct', 2)
- 330±80μs 102±4μs 0.31 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct', 2)
- 338±90μs 104±4μs 0.31 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'transformation', 2)
- 345±80μs 105±7μs 0.31 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'transformation', 2)
- 333±80μs 101±7μs 0.30 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation', 2)
- 391±100μs 107±5μs 0.27 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct', 2)
- 385±100μs 106±20μs 0.27 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct', 2)
- 436±100μs 119±20μs 0.27 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'transformation', 5)
- 377±100μs 102±20μs 0.27 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'transformation', 2)
- 465±100μs 122±30μs 0.26 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct', 5)
- 389±100μs 102±20μs 0.26 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct', 2)
- 437±100μs 113±7μs 0.26 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct', 5)
- 434±90μs 111±10μs 0.26 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'transformation', 5)
- 456±100μs 116±7μs 0.26 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct', 5)
- 469±100μs 118±10μs 0.25 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'transformation', 5)
- 457±100μs 114±20μs 0.25 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct', 5)
- 459±100μs 114±6μs 0.25 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'transformation', 5)
- 448±100μs 110±20μs 0.25 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'transformation', 5)
- 484±100μs 117±10μs 0.24 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'direct', 5)
- 464±100μs 111±4μs 0.24 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'direct', 5)
- 456±100μs 109±7μs 0.24 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'transformation', 5)
- 459±100μs 108±6μs 0.24 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'transformation', 5)
- 478±200μs 113±6μs 0.24 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct', 5)
- 461±100μs 108±8μs 0.24 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation', 5)
- 452±100μs 105±3μs 0.23 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct', 5)
- 528±200μs 121±30μs 0.23 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'transformation', 5)
- 582±100μs 127±30μs 0.22 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'transformation', 10)
- 534±100μs 117±30μs 0.22 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct', 5)
- 530±100μs 116±30μs 0.22 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct', 5)
- 628±100μs 133±30μs 0.21 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct', 10)
- 539±200μs 113±30μs 0.21 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'transformation', 5)
- 645±200μs 130±10μs 0.20 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'transformation', 10)
- 657±200μs 127±10μs 0.19 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'direct', 10)
- 621±100μs 120±30μs 0.19 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'transformation', 10)
- 654±200μs 126±10μs 0.19 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'transformation', 10)
- 651±200μs 123±10μs 0.19 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'transformation', 10)
- 649±200μs 121±8μs 0.19 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct', 10)
- 670±200μs 124±30μs 0.19 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct', 10)
- 663±200μs 122±6μs 0.18 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'transformation', 10)
- 659±200μs 119±6μs 0.18 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'transformation', 10)
- 665±200μs 120±9μs 0.18 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct', 10)
- 693±200μs 123±3μs 0.18 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'direct', 10)
- 663±100μs 117±7μs 0.18 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation', 10)
- 642±100μs 113±5μs 0.18 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct', 10)
- 755±200μs 125±30μs 0.17 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'transformation', 10)
- 772±200μs 122±30μs 0.16 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct', 10)
- 762±200μs 120±30μs 0.16 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct', 10)
- 786±200μs 123±6μs 0.16 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct', 10)
- 814±200μs 123±30μs 0.15 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'transformation', 10)
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} | 3 | "2021-08-01T16:02:04Z" | "2021-08-03T14:35:26Z" | "2021-08-03T07:28:09Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/42843 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42843/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42843/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42843/events | https://github.com/pandas-dev/pandas/pull/42843 | 957,553,904 | MDExOlB1bGxSZXF1ZXN0NzAwOTU4NDA0 | 42,843 | REGR: sample modifying `weights` inplace | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Targeting 1.4 and no whatsnew since only a regression on master.
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} | 8 | "2021-08-01T20:15:54Z" | "2021-11-20T16:19:43Z" | "2021-11-20T16:19:43Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Hi,
Following code below was working with pandas 1.2.5.
It raises now an error with pandas 1.3.1.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import random
# Right data
ts_open = pd.DatetimeIndex([
pd.Timestamp('2021/01/01 00:37'),
pd.Timestamp('2021/01/01 00:40'),
pd.Timestamp('2021/01/01 01:00'),
pd.Timestamp('2021/01/01 03:45'),
pd.Timestamp('2021/01/01 03:59'),
pd.Timestamp('2021/01/01 05:20')])
length = len(ts_open)
random.seed(1)
volume = random.sample(range(1, length+1), length)
df_smpl = pd.DataFrame({'volume' : volume, 'ts_open': ts_open})
# Left data
ts_full = pd.date_range(start=pd.Timestamp('2021/01/01 00:00'),
periods=7, freq='1h')
ts_event = ts_full[:-1]
length_ipr = len(ts_event)
random.seed(2)
volume = random.sample(range(20, length_ipr+20), length_ipr)
df_ipr = pd.DataFrame({'volume' : volume, 'timestamp': ts_event})
at = ts_full[1:]
df_smpl.index = df_smpl['ts_open']
# Test
df_full = pd.merge_asof(df_ipr, df_smpl,
left_on=at, right_index=True,
allow_exact_matches=False, direction='backward')
```
Error message:
```python
Traceback (most recent call last):
File "<ipython-input-152-b5845a330a4b>", line 30, in <module>
df_full = pd.merge_asof(df_ipr, df_smpl,
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 581, in merge_asof
op = _AsOfMerge(
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 1736, in __init__
_OrderedMerge.__init__(
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 1619, in __init__
_MergeOperation.__init__(
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 682, in __init__
self._validate_specification()
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 1782, in _validate_specification
if left_on in self.left.columns
File "/home/yoh/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 4572, in __contains__
hash(key)
TypeError: unhashable type: 'DatetimeIndex'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-63-generic
Version : #71~20.04.1-Ubuntu SMP Thu Jul 15 17:46:08 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : 0.7.0
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : 0.19.0
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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] | open | false | null | [] | null | 9 | "2021-08-01T23:25:11Z" | "2021-10-21T02:01:54Z" | null | NONE | null | _Note_: This is my first attempt to contribute code to pandas, so any correction is more than welcome.
This proposal adds a new parameter to the bar and barh plots, `positioning`, with the values:
- sequential: The index values are added sequentially (`x=0, 1, 2, 3...`) and only the tick label shows the value.
- numerical: The X axis positions will be related to the real index values.
- categorical: The X axis ticks includes all the possible categories, even if are not all used in the index.
With these modes it is possible to create richer bar plots, useful to compare results from different samples.
## Details
With the current bar and barh plots is difficult to create a graph where the categorical axis (x for bar) follows the real values
of the input data.
For example, if we have 10 machines that are not always active and we are collecting the RAM and CPU usage we can have
a data set like:
```python
data = pd.DataFrame({
'machine': [1, 6, 4, 2, 10],
'cpu': [0.9, 0.8, 0.8, 0.9, 0.7],
'ram': [0.6, 0.7, 0.2, 0.9, 1.0]
})
data.plot.bar(x='machine', rot=0)
```
This can be misleading and difficult to compare with data from other sample:
```python
other = pd.DataFrame({
'machine': [2, 5, 4, 8, 10],
'cpu': [0.7, 0.3, 0.8, 0.7, 0.8],
'ram': [0.5, 0.9, 0.6, 0.6, 0.8]
})
fig, axes = plt.subplots(2, 1)
data.plot.bar(x='machine', ax=axes[0], ylabel='day 1', rot=0)
other.plot.bar(x='machine', ax=axes[1], ylabel='day 2', rot=0)
fig.suptitle('Difficult to correlate plots')
```
This proposal introduces the possibility to interpret the x values according to the numerical value, solving this issue:
```python
fig, axes = plt.subplots(2, 1, sharex='col')
data.plot.bar(x='machine', ax=axes[0], ylabel='day 1', rot=0, positioning="numerical")
other.plot.bar(x='machine', ax=axes[1], ylabel='day 2', rot=0, positioning="numerical")
axes[1].set_xlim(0.5, 10.5)
fig.suptitle('Aligned plot')
```
When the X axis is a categorical data it is possible to also include all the missing labels in the X-axis,
so it is easier to compare graphs generated in different moments.
```python
machines = ['OP1', 'OP2', 'OP3', 'OP4', 'OP5', 'SP1', 'SP2', 'SP3', 'SP4', 'SP5']
day_1 = pd.DataFrame({'machine': ['OP1', 'SP1', 'OP4', 'OP2', 'SP5'], 'cpu': [0.9, 0.8, 0.8, 0.9, 0.7], 'ram': [0.6, 0.7, 0.2, 0.9, 1.0]})
day_1['machine'] = pd.Categorical(day_1['machine'], machines)
day_2 = pd.DataFrame({
'machine': ['OP2', 'OP5', 'OP4', 'SP3', 'SP4'],
'cpu': [0.7, 0.3, 0.8, 0.7, 0.8],
'ram': [0.5, 0.9, 0.6, 0.6, 0.8]
})
day_2['machine'] = pd.Categorical(day_2['machine'], machines)
fig, axes = plt.subplots(2, 1, sharex='col')
day_1.plot.bar(x='machine', ax=axes[0], ylabel='day 1', rot=0, positioning="categorical")
day_2.plot.bar(x='machine', ax=axes[1], ylabel='day 2', rot=0, positioning="categorical")
fig.suptitle('Plot with Categories')
```
## Other changes
I also factorized the documentation of bar and barh so they share the same examples. In the current version of the docs there are subtle differences between the bar and barh pages, I tried to unify them.
## Open Points
- Not convinced at all with the name of the flag and the values, for sure someone can find better alternative
- Current code works well with "absolute" index like float, integer categorical or timestamp. It does not work with relative index like period, timedelta or date offsets. Sometimes is fair enough (like using MonthEnd) but in other cases like TimeDelta the problem arises in the different way matplotlib converts the width parameter of bar.
- I added some tests, but I need to add additional ones to check proper behaviour with error bars and stacked plots.
- The code can be better organized, but requires to modify the content of some of the BarPlot class properties between `__init__` and the call to `_make_plot`. I preferred to keep backwards compatibility, even if this class looks internal to pandas.
_NOTE_: I renamed the parameter from `x_mode` to `positioning`. Still not the best, but at least do not produce confusion for the barh plots (where it really position the Y axis). Still not perfect, as there is also the parameter `position` that might be confusing.
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/42847 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42847/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42847/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42847/events | https://github.com/pandas-dev/pandas/issues/42847 | 957,733,299 | MDU6SXNzdWU5NTc3MzMyOTk= | 42,847 | BUG: Pylance type annotation issue with loc | {
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#### Code Sample, a copy-pastable example
```python
import pandas as pd
index = ["Firefox", "Chrome", "Safari", "IE10", "Konqueror"]
index2 = [1, 2, 3, 4, 5]
df = pd.DataFrame(
{
"http_status": [200, 200, 404, 404, 301],
"response_time": [0.04, 0.02, 0.07, 0.08, 1.0],
},
index=[index, index2],
)
print(df)
print(df.loc[("Firefox", 1)])
```
#### Problem description
This code works, and is pretty much exactly following what the documentation says I should do by Pylance is still not happy with the types.
#### Expected Output
No type error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 56.0.0
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 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
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/42848 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42848/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42848/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42848/events | https://github.com/pandas-dev/pandas/pull/42848 | 957,826,421 | MDExOlB1bGxSZXF1ZXN0NzAxMTkwMjk2 | 42,848 | ⬆️ UPGRADE: Autoupdate pre-commit config | {
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} | [] | closed | false | null | [] | null | 1 | "2021-08-02T07:11:17Z" | "2021-08-03T07:40:28Z" | "2021-08-03T07:40:28Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.11-py2.py3-none-any.whl (98 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.7.0-py2.py3-none-any.whl (5.3 MB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Collecting backports.entry-points-selectable>=1.0.4
Downloading backports.entry_points_selectable-1.1.0-py2.py3-none-any.whl (6.2 kB)
Collecting platformdirs<3,>=2
Downloading platformdirs-2.2.0-py3-none-any.whl (13 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Installing collected packages: six, platformdirs, filelock, distlib, backports.entry-points-selectable, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed backports.entry-points-selectable-1.1.0 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.11 nodeenv-1.6.0 platformdirs-2.2.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.7.0
```
### stderr:
```Shell
WARNING: You are using pip version 21.2.1; however, version 21.2.2 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.6/x64/bin/python -m pip install --upgrade pip' command.
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
updating 5.9.2 -> 5.9.3.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.23.0 -> v2.23.1.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Passed
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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} | 1 | "2021-08-02T08:13:53Z" | "2021-09-04T23:29:29Z" | "2021-09-04T23:29:29Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Firstly, here's a working example using the default dtypes:
```python
df = pd.DataFrame({
"x": [1, 1],
"y": [0.2, np.nan]
})
print(f"dtypes:\n{df.dtypes}")
display(df.groupby("x")["y"].quantile(0.5))
```
<details>
```text
dtypes:
x int64
y float64
dtype: object
x
1 0.2
Name: y, dtype: float64
```
</details>
To contrast, if I use the Pandas dtypes, the codef ails
```python
df = pd.DataFrame({
"x": [1, 1],
"y": [0.2, np.nan]
}).astype({"x": pd.Int64Dtype(), "y": pd.Float64Dtype()})
print(f"dtypes:\n{df.dtypes}")
display(df.groupby("x")["y"].quantile(0.5))
```
<details>
```text
dtypes:
x Int64
y Float64
dtype: object
TypeError: float() argument must be a string or a number, not 'NAType'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/josh/ipykernel_67869/1525766791.py in <module>
4 }).astype({"x": pd.Int64Dtype(), "y": pd.Float64Dtype()})
5 print(f"dtypes:\n{df.dtypes}")
----> 6 display(df.groupby("x")["y"].quantile(0.5))
.../python3.9/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
2450
2451 if is_scalar(q):
-> 2452 return self._get_cythonized_result(
2453 "group_quantile",
2454 aggregate=True,
.../python3.9/site-packages/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, cython_dtype, aggregate, numeric_only, needs_counts, needs_values, needs_2d, needs_nullable, min_count, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
2899 )
2900 continue
-> 2901 vals = vals.astype(cython_dtype, copy=False)
2902 if needs_2d:
2903 vals = vals.reshape((-1, 1))
TypeError: float() argument must be a string or a number, not 'NAType'
```
</details>
What is odd is that the quantile function works fine with if we avoid using groupby:
```python
df["y"].quantile(0.5)
```
<details>
```text
0.2
```
</details>
#### Problem description
I would have expected that change from the default `float64` type to `pd.Float64Dtype` should produce the same results with the `NAN` values being treated in the same way.
#### Output of ``pd.show_versions()``
<details>
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.13.6-zen1-1-zen
Version : #1 ZEN SMP PREEMPT Thu, 29 Jul 2021 00:21:08 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.1.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
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 : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 2 | "2021-08-02T10:52:58Z" | "2021-08-14T18:55:06Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.Series(["a", "a", "b", pd.NA]).value_counts(dropna=False).index
Index(['a', 'b', nan], dtype='object')
```
#### Problem description
`.value_counts` converts `pd.NA` to `np.nan`. Instead `pd.NA` should be preserved. In my use case, I use `value_counts` to find small groups, based on which I want to filter the data. This is hard, if the value in the counts differs from the actual value.
#### Expected Output
```python
Index(['a', 'b', <NA>], dtype='object')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-80-generic
Version : #90-Ubuntu SMP Fri Jul 9 22:49:44 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.20.2
pytz : 2020.5
dateutil : 2.8.1
pip : 19.3.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Trying to implement a demo from the tutorial "Art of Pandas"
Here is my code that should replace an element in the array
```
import pandas as pd
arr = pd.array([2, 5, 3], dtype="int")
arr[0] = [1]
```
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```
In [1]: s = np.random.rand(1000, 2).astype([("a", float), ("b", float)]); r = s.view(np.recarray)
In [2]: %timeit s["a"]
69.4 ns ± 0.0733 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
In [3]: %timeit r["a"]
1.2 µs ± 10 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [4]: %timeit r.a
1.86 µs ± 7.42 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
Therefore it can be desirable to convert DataFrames to
structured arrays rather than record arrays (the recipe
for the conversion directly comes from the numpy docs at
https://numpy.org/doc/stable/user/basics.rec.html#record-arrays,
ignoring the handling of non-record inputs).
Currently the implementation simply reuses the to_record implementation.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 0 | "2021-08-02T14:40:39Z" | "2021-08-05T02:42:36Z" | "2021-08-05T00:15:25Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Tracked all the places where _maybe_cast_slice_bound is called and concluded that stdlib date objects are [update: almost] always handled by caller, so don't need to be handled in _maybe_cast_slice bound. Removing that special-casing (this PR) will allow us to share the method between DTI/TDI/PI (next PR) | {
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} | 2 | "2021-08-02T17:24:37Z" | "2021-08-20T13:50:44Z" | "2021-08-20T13:50:30Z" | CONTRIBUTOR | null | - [x] closes #40989
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Added an if-condition for when axis = 1 so as to transpose the result in pandas/tests/frame/methods/test_fillna.py, and created a test for this change. The test also tests for `inplace =True`. This solves issue #40989 | {
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- [x] tests added / passed
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- [ ] closes #40498
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] tests added / passed
- [x] whatsnew entry
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- [ ] I have confirmed this bug exists on the latest version of pandas.
- [x] I have confirmed this bug exists on the master branch of pandas.
I don't really know how to describe this or if it is as expected but it seems strange behaviour to me:
```python
>>> df = pd.DataFrame({"a": [pd.NA, -1, None], "b": [np.nan, -1, 1]})
a b
0 <NA> NaN
1 -1 -1.0
2 None 1.0
>>> print(df.dtypes)
a object
b float64
>>>print(df * -1)
a b
0 NaN NaN
1 1 1.0
2 NaN -1.0
>>> print((df * -1).dtypes
a object
b float64
>>> print(-df)
TypeError: bad operand type for unary -: 'NoneType'
```
I guess there are two things here:
1) `(-df)` is not the same as `(df * -1)`
2) the operation keeps `dtype` as `object` but the `<NA>` and `None` are converted to `NaN` display | {
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https://github.com/pandas-dev/pandas/blob/3fe8e24a17170ad2296ecd81ba60e23cee763173/pandas/core/arrays/string_arrow.py#L63-L65
However, in theory, it is possible to have a pyarrow installation without `pyarrow.compute` support (it's an optional component when building). In that case the `import pyarrow.compute as pc` import will fail giving a slightly confusing error message + it will fail on a basic import of pandas (even without using any pyarrow functionality). | {
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} | 7 | "2021-08-03T12:47:30Z" | "2021-10-15T13:28:43Z" | "2021-10-15T13:28:43Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
Pandas provides DataFrameGroupBy.head() and tail(), which efficiently slice the beginning and end of each group while preserving the order and index. I would like to be able to do a general row slice with the same properties. DataFrame has head(), tail() and iloc that behave in a compatible way. There is no corresponding DataFrameGroupBy.iloc.
#### Describe the solution you'd like
Provide a new DataFrameGroupBy method to slice rows per group
#### API breaking implications
None
#### Describe alternatives you've considered
The following are existing ways to extract, say, the second and third entry of each group, assuming that there are a large number of rows in each group (~10000):
1. grouped.apply(lambda x: x.iloc[1:3, :]) - Extremely slow. Does not preserve the order or indexing.
2. grouped.take([1, 2]) - Extremely slow. Does not preserve the order or indexing.
3. grouped.nth([1, 2]) - Quite fast for a small list. Does not preserve the order or indexing.
4. grouped.head(3).groupby('...').tail(2) - Quite fast. Does preserve index and ordering.
5. grouped._selected_obj[mask] where mask is built from grouped.cumcount() - Very fast. Does preserve index and ordering. But uses private attribute of DataFrameGroupBy and takes several lines of code.
#### Additional context
There are three options:
1. Add an option to an existing method to force it to preserve index and order. But take() is very slow and nth() is quite slow. Neither accept a slice argument, so a range list has to be provided.
2. Easiest: Add a new method taking a slice as an argument and implementing it as in 5 above.
3. Most logical and complete: Add a new iloc attribute analogous to DataFrame.iloc
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} | 2 | "2021-08-03T13:05:01Z" | "2021-08-19T12:54:34Z" | "2021-08-19T12:54:30Z" | CONTRIBUTOR | null | This is in reference to #32306.
While the discussion on how to fix this is still ongoing, a warning will help users to at least know about this and take the appropriate precautions.
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} | 2 | "2021-08-03T16:57:17Z" | "2021-08-05T11:48:24Z" | "2021-08-05T11:48:24Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Run python as PYTHONOPTIMIZE, option -O:
```bash
python -O
```
```python
import pickle
import pandas as pd
idx = pd.date_range('2021-01-01', '2021-01-02', freq='D')
idx_dump = pickle.dumps(idx)
pickle.loads(idx_dump)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../lib/python3.7/site-packages/pandas/core/indexes/datetimes.py", line 61, in _new_DatetimeIndex
result = cls._simple_new(dta, **d)
TypeError: _simple_new() got an unexpected keyword argument 'tz'
```
#### Problem description
These dict operations go under the 'assert' statement:
```python
# pandas/core/indexes/datetimes.py
def _new_DatetimeIndex(cls, d):
...
if key in d:
assert d.pop(key) == getattr(dta, key)
...
```
which are ignored in PYTHONOPTIMIZED mode.
As a fix, bring dict pop operation out of the assert statement.
#### Expected Output
```python
DatetimeIndex(['2021-01-01', '2021-01-02'], dtype='datetime64[ns]', freq='D')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.7.9.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.5
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 57.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.to_latex.html
#### Documentation problem
I think the set of packages needed to get this to work should be more clearly defined, given how different peoples' Latex setups are. For instance, the current package list in the documentation doesn't seem to provide any explaination for where the \thsolid command comes from, which seems to be used in all of the tables generated by this table.
#### Suggested fix for documentation
A suggested set of imports to add to the preamble of a LaTeX document should be clearly laid out, and complete, with a minimum working example present including both Python and accompanying LaTeX code.
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- [X ] I have confirmed this bug exists on the latest version of pandas.
- [ X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
.to_latex() for Styler seems to have no way to handle when latex-invalid characters are used in the dataset. For instance, if the data values are in percentages (e.g. one cell is 68%, after the usage of .format('{0:,.0%}') on a python dataframe), the latex conversion doesn't handle escaping these characters appropriately for LaTeX.
https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.to_latex.html
#### Expected Output
Current output to LaTeX: -59%
Correct output: -59\%
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.11.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.122-66.218.amzn2int.x86_64
Version : #1 SMP Fri May 28 00:16:13 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 57.0.0
Cython : 0.29.23
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.11
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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Currently `searchsorted` is supported for `Series` and `Index` objects, however I wish to be able to use it with `DataFrame` objects as well.
#### Describe the solution you'd like
I'd like to be able to call `DataFrame.searchsorted(value, side, sorter)` like in the case of `Series.searchsorted`, but in this case the `value` parameter would be a `DataFrame` with the same number of columns as `self`.
#### Additional context
For example:
```python
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 5, 7], 'b': [10, 12, 14, 16]})
value_df = pd.DataFrame({'a': [0, 2], 'b': [10, 11]})
df.searchsorted(value_df)
```
would return
```python
array([0, 1])
```
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Ref: [ARROW-13074](https://issues.apache.org/jira/browse/ARROW-13074?page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel&focusedCommentId=17391457#comment-17391457)
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Just a tiny addition to the cookbook section of the User Guide.
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} | 17 | "2021-08-04T07:47:37Z" | "2021-10-09T20:52:39Z" | null | MEMBER | null | ```
In [4]: df = pd.DataFrame({'a': [1, 2, 3]})
In [5]: df.take(1)
...
~/scipy/pandas/pandas/core/indexes/range.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
433 ) -> Int64Index:
434 with rewrite_exception("Int64Index", type(self).__name__):
--> 435 return self._int64index.take(
436 indices,
437 axis=axis,
~/scipy/pandas/pandas/core/indexes/base.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
962 self._values, indices, allow_fill=allow_fill, fill_value=self._na_value
963 )
--> 964 return type(self)._simple_new(taken, name=self.name)
965
966 @final
~/scipy/pandas/pandas/core/indexes/base.py in _simple_new(cls, values, name)
611 Must be careful not to recurse.
612 """
--> 613 assert isinstance(values, np.ndarray), type(values)
614
615 result = object.__new__(cls)
AssertionError: <class 'numpy.int64'>
```
`take` requires an array-like for the indexer, but when passing a scalar you get a not very useful error message as above. We should raise a ValueError (or rather TypeError) instead with an informative message.
It actually comes from the Index take implementation:
```
In [9]: pd.Index([1, 2, 3]).take(1)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-9-ce9f87ba8bd2> in <module>
----> 1 pd.Index([1, 2, 3]).take(1)
~/scipy/pandas/pandas/core/indexes/base.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
962 self._values, indices, allow_fill=allow_fill, fill_value=self._na_value
963 )
--> 964 return type(self)._simple_new(taken, name=self.name)
965
966 @final
~/scipy/pandas/pandas/core/indexes/base.py in _simple_new(cls, values, name)
611 Must be careful not to recurse.
612 """
--> 613 assert isinstance(values, np.ndarray), type(values)
614
615 result = object.__new__(cls)
AssertionError: <class 'numpy.int64'>
``` | {
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} | 1 | "2021-08-04T09:03:02Z" | "2021-08-04T11:14:07Z" | "2021-08-04T11:14:02Z" | NONE | null | First of all, thank you for the great Pandas!
The docs of `pandas.Series.unique()` clearly state that the function does NOT sort the elements. Differently, `numpy.unique()` sorts the elements. What is the reason behind this counterintuitive difference? It is quite unexpected and deemed to create nasty bugs in code - which just happen to me.
Just to be clear:
```
In [3]: a=[3,2,1,0]
In [4]: np.unique(a)
Out[4]: array([0, 1, 2, 3])
In [5]: pd.Series(a).unique()
Out[5]: array([3, 2, 1, 0])
```
In the context of code that uses both `numpy` and `pandas`, it would be greatly beneficial to have the same behavior. Would that be possible? | {
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} | 1 | "2021-08-04T09:34:16Z" | "2021-08-04T15:29:31Z" | "2021-08-04T15:26:07Z" | MEMBER | null | Tiny PR, but I noticed this confusing message while running some tests (this skip is used generally, and JSON C code now actually no longer relies on the Block)
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] | open | false | null | [] | null | 12 | "2021-08-04T09:37:14Z" | "2021-08-21T20:32:57Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
a = pd.Series(np.random.normal(scale=0.1, size=(1_000_000,)).astype(np.float32)).pow(2)
assert isinstance(np.mean(a), float)
assert isinstance(np.mean(a.values), np.float32)
assert abs(1 - np.mean(a)/np.mean(a.values)) > 4e-4
```
#### Problem description
1. `pd.DataFrame.mean`/`pd.Series.mean`/`np.mean(pd.Series)` outputs a Python float instead of a numpy float. Since `np.mean(pd.Series.values)` does return an np float, I'm assuming for now that this should be fixed in pandas
2. if `dtype==np.float32`, then calling `mean` on a pandas object gives a significantly different result vs calling `mean` on the underlying numpy ndarray.
#### Expected Output
The output of `np.mean(a)` should be the same as `np.mean(a.values)`.
additional tests
```python
# both b and c ~1e-2
b = a.mean() # the pandas impl of mean
assert isinstance(b, float) # PYTHON float, not numpy float? Ergo implicit f64
h = np.mean(a)
assert isinstance(h, float)
assert h == b
c = a.values.mean() # the numpy impl of mean
assert isinstance(c, np.float32) # as exprected
print('\nerrors between pandas mean and numpy mean')
print(f'relative error: {abs(1-b/c):.3e}') # ~ 5e-4
print(f'absolute error: {abs(b -c):.3e}') # ~ 5e-6
print(f'relative error after casting: {abs(1-np.float32(b)/c):.3e}') # ~ 5e-4
print(f'absolute error after casting: {abs(np.float32(b) -c):.3e}') # ~ 5e-6
d = a.sum() / len(a)
assert isinstance(d, np.float64) # expected, because division. Note `sum` returns an np.float32
e = a.values.sum() / len(a)
assert isinstance(e, np.float64) # expected, because division
# these methods are equivalent
assert d==e
# and up to f32 precision equal to the numpy impl
assert d.astype(np.float32) == c
# the cherry on the cake
f = a.astype(np.float64).mean()
assert isinstance(f, float) # still not ideal, should be np.float64
g = a.astype(np.float64).values.mean()
print('\nrelative error between pandas f64 mean and numpy f64 mean')
print(f'relative error numpy f64/pandas f64: {abs(1-g/f):.3e}') # ~ 1e-14 -- 1e-16, not bad but I would have expected equality
print('\nerrors between pandas f64 mean and numpy/pandas f32 mean')
print(f'relative error pandas f32/pandas f64: {abs(1-b/f):.3e}') # ~ 5e-4
print(f'absolute error numpy f32/pandas f64: {abs(1-c/f):.3e}') # ~ 1e-7 -- 1e-9
# finally...
h = np.mean(a)
assert isinstance(h, float)
assert h == b
```
output
```python
errors between pandas mean and numpy mean
relative error: 5.210e-04
absolute error: 5.204e-06
relative error after casting: 5.210e-04
absolute error after casting: 5.204e-06
relative error between pandas f64 mean and numpy f64 mean
relative error numpy f64/pandas f64: 1.066e-14
errors between pandas f64 mean and numpy/pandas f32 mean
relative error pandas f32/pandas f64: 5.214e-04
absolute error numpy f32/pandas f64: 2.399e-07
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-80-generic
Version : #90-Ubuntu SMP Fri Jul 9 22:49:44 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 1 | "2021-08-04T10:26:08Z" | "2021-08-05T07:41:06Z" | "2021-08-04T12:28:37Z" | CONTRIBUTOR | null | waiting on green tests
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} | 2 | "2021-08-04T10:35:36Z" | "2021-09-05T01:45:01Z" | "2021-09-05T01:45:01Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
Converting a sparse Series to a `scipy.sparse.coo_matrix` could be much faster. I think the `get_indexer` function defined in [`_to_ijv`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/sparse/scipy_sparse.py#L22) adds unnecessary complexity.
#### Describe the solution you'd like
It can be much faster by accessing the `codes` attribute of the multiindex, as follows:
```
i_coord, j_coord = ss.index.codes
i_labels, j_labels = ss.index.levels
```
for a two-level multiindex. It should be straightforward to extend to more levels I think.
#### API breaking implications
None
#### Describe alternatives you've considered
None
#### Additional context
To give an example, I started digging into this problem because I had a 2-level-MultiIndexed Series with 61M rows, that is to be converted to a 1M x 1500 sparse matrix. Making the conversion using `to_coo()` took 10min, making it as described above took half a second. | {
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} | 1 | "2021-08-04T10:59:49Z" | "2021-08-10T20:06:45Z" | "2021-08-10T20:06:45Z" | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({
'Col1': ['one', 'two'],
'Col2': ['three', 'four']
})
df.drop(columns=['Col3', 'Col4'])
```
####
Actual Output:
```
KeyError: "['Col3' 'Col4'] not found in axis"
```
(comma missing between list items)
#### Problem description
When trying to drop a list of columns that don't exist in the dataframe the key error shows a list without the comma separating the items in the list. This leads to the confusion that the list I passed into the drop() function was wrong whereas it was correct but the KeyError message was wrong.
#### Expected Output
```python
KeyError: "['Col3', 'Col4'] not found in axis"
```
(with comma separating items in list)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.0.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 1 | "2021-08-04T14:31:23Z" | "2021-08-04T15:52:20Z" | "2021-08-04T15:45:00Z" | MEMBER | null | Small piece broken off from https://github.com/pandas-dev/pandas/pull/41878
This updates a bunch of tests that are unnecessarily using chained assignment. Outside tests specifically about indexing, I think it's better/cleaner to use the more idiomatic single .loc call (and also makes it easier to change the behaviour as I am doing in https://github.com/pandas-dev/pandas/pull/41878). | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> index = pd.MultiIndex.from_tuples([(('a', None), 1), (('b', None), 2)])
>>> index2 = pd.MultiIndex.from_tuples([(('b', None), 2), (('a', None), 1)])
>>> df1_dtype = pd.DataFrame([1,2], index=index)
>>> df2_dtype = pd.DataFrame([2,1], index=index2)
>>> df1_dtype.reindex_like(df2_dtype)
0
(b, None) 2 NaN
(a, None) 1 NaN
```
```python
>>> import pandas as pd
>>> import numpy as np
>>> index = pd.MultiIndex.from_tuples([(('a', np.nan), 1), (('b', np.nan), 2)])
>>> index2 = pd.MultiIndex.from_tuples([(('b', np.nan), 2), (('a', np.nan), 1)])
>>> df1_dtype = pd.DataFrame([1,2], index=index)
>>> df2_dtype = pd.DataFrame([2,1], index=index2)
>>> df1_dtype.reindex_like(df2_dtype)
0
(b, nan) 2 2
(a, nan) 1 1
```
#### Problem description
I would expect the two indices to align here.
#### Expected Output
```python
>>> import pandas as pd
>>> index = pd.MultiIndex.from_tuples([(('a', None), 1), (('b', None), 2)])
>>> index2 = pd.MultiIndex.from_tuples([(('b', None), 2), (('a', None), 1)])
>>> df1_dtype = pd.DataFrame([1,2], index=index)
>>> df2_dtype = pd.DataFrame([2,1], index=index2)
>>> df1_dtype.reindex_like(df2_dtype)
0
(b, None) 2 2
(a, None) 1 1
```
#### Output of ``pd.show_versions()``
<details>
```python3
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 226876a55b82324b92449256bab47d431b22310f
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+357.g226876a55b
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.14.5
sphinx : 4.1.2
blosc : 1.10.4
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : 2021.07.0
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
```
</details>
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} | 1 | "2021-08-04T15:16:36Z" | "2021-08-05T12:02:57Z" | "2021-08-05T12:01:47Z" | CONTRIBUTOR | null | - [x] closes #41779
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 11 | "2021-08-04T15:54:15Z" | "2021-10-31T00:49:27Z" | "2021-10-31T00:49:26Z" | NONE | null | - [x ] closes https://github.com/pandas-dev/pandas/issues/42875#issuecomment-892736707
- [ x] tests added / passed
- [ x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x ] whatsnew entry
Fixes: https://github.com/pandas-dev/pandas/issues/42875#issuecomment-892736707
TypeError is being raised on passing scalar value in DataFrame.take | {
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] | open | false | null | [] | null | 1 | "2021-08-04T16:02:40Z" | "2021-08-21T20:34:51Z" | null | MEMBER | null | Currently, when indexing 2 dimensions at once, we typically handle each dimension independently in the `indexing.py` code.
For example, consider the following `getitem` operation where we have both a row and column indexer:
```python
df = pd.DataFrame(np.random.randn(10, 4), columns=["a", "b", "c", "d"])
subset = df.loc[1:4, ["b", "c"]]
```
This specific case is finally handled in `_getitem_tuple_same_dim` with
https://github.com/pandas-dev/pandas/blob/226876a55b82324b92449256bab47d431b22310f/pandas/core/indexing.py#L786-L791
where you can see that we first select the slice for each column (all blocks), and then in a second loop iteration select the columns we need. For this specific case, that means that we are also unnecessarily indexing the columns (blocks) that we don't need + we are creating an intermediate DataFrame.
(for this specific case with a single block in the example above, it won't matter much, but in general you can have many blocks of course. Also, for a slice it won't be that important, but eg boolean filtering or integer indexing all columns is more expensive)
Similarly, consider a second example with a `setitem` operation:
```python
df = pd.DataFrame(np.random.randn(10, 4), columns=["a", "b", "c", "d"])
df["d"] = 1 # making it a multi-block df to not take the _setitem_single_block path
df.loc[1:4, ["b", "c"]] = 0.0
```
This specific case is handled in `_setitem_with_indexer_split_path` with
https://github.com/pandas-dev/pandas/blob/226876a55b82324b92449256bab47d431b22310f/pandas/core/indexing.py#L1722-L1723
So also here the operation is done column by column independently. The `_setitem_single_column` then accesses the column as a Series, updates that series and sets it again in the DataFrame (`self.obj._iset_item(loc, ser)`).
Also here this can be less efficient in some cases (eg if you assign into multiple columns of the same block), although I think this will be less prevalent as the getitem case above. But I also ran into this in my Copy-on-Write POC (https://github.com/pandas-dev/pandas/pull/41878) where the implementation updating each column by updating a Series is problematic (I want to have the final update logic inside the manager, so I can manage the reference tracking / copy on write there, for this specific PR. For that purpose, I introduced a `SingleArrayManager.setitem_column()` method).
---
In general, I think it would be a cleaner interface to leave it up to the Manager to see how it handles the multiple dimensions of the indexer (after all validation, pre-processing of the indexers (up to purely positional indexers) and the value in indexing.py, of course). And that way, try to move some of the "split path" or not complexity into the internals.
We could for example have a `DataManager.getitem/setitem` method with a signature like
```
def getitem(self: DataManager, row_indexer, column_indexer) -> DataManager:
...
def setitem(self: DataManager, row_indexer, column_indexer, value):
...
```
for the case where indexing a DataFrame results in a DataFrame (so not reducing the dimension). It's of course a bit a question how eg `value` would be passed if it was eg a DataFrame initially (and not a scalar like in my simple example).
So this issue is meant for an initial discussion of the topic. I ran into some of those issues while working on the ArrayManager/Copy-on-Write indexing related code, and so tried to put some thoughts here, to see what other are thinking about it.
Indexing is a complex topic, so in practice there are much more cases to consider than the simple examples above (and most will probably only really become apparent while trying to refactor something). But the general idea is to move more of the logic from indexing.py (after pre-processing/validation/conversion to positional indexers) into the internals.
cc @jbrockmendel @jreback
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} | 9 | "2021-08-04T19:31:35Z" | "2021-08-05T05:43:44Z" | "2021-08-05T05:43:44Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Previously, we were able to replace NaN values with `None` for easier insertion into databases like mysql, as listed here:
https://stackoverflow.com/questions/14162723/replacing-pandas-or-numpy-nan-with-a-none-to-use-with-mysqldb
python
```
df = pd.DataFrame([[1, None, np.NaN], [2, np.NaN, np.NaN]])
df = df.where(pd.notnull(df), None)
df
```
Output of df:
0 | 1 | 2
-- | -- | --
1 | NaN | NaN
2 | NaN | NaN
#### Problem description
Previously, all np.NaN were replaced with `None`. Now, they still remain as `np.NaN`, and breaks database insertions
#### Expected Output
0 | 1 | 2
-- | -- | --
1 | None | None
2 | None | None
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1049-aws
Version : #51~18.04.1-Ubuntu SMP Fri May 14 18:38:46 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df1 = pd.DataFrame.from_dict({"A": [1, 2, 3]})
df2 = pd.DataFrame.from_dict({"A": [4, 5]})
print(df1["A"])
df1.update(df2)
print(df1["A"])
```
Output:
```
# df1 before the update
0 1
1 2
2 3
Name: A, dtype: int64
# df1 after the update
0 4.0
1 5.0
2 3.0
Name: A, dtype: float64
```
#### Problem description
Update of int columns changes their type to float, that is unexpected behavior. And this happens only if the other dataframe does not have all indices of this dataframe. I would expect the type to be conserved...
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.el7.x86_64
Version : #1 SMP Thu Oct 4 20:48:51 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.15
pytest : 5.3.5
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fsspec : 2021.06.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.2
xlrd : None
xlwt : None
numba : None
1
</details>
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This will probably need to be done with a deprecation, this is just to to clarify which tested behaviors would change. | {
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} | 1 | "2021-08-04T22:48:35Z" | "2021-08-05T12:02:39Z" | "2021-08-05T12:02:33Z" | CONTRIBUTOR | null | - [x ] closes #33787
- [x ] tests added / passed
- [x ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
First attempt at contributing to pandas, or any open source projects! I think all the linting tests passed, since I installed and used the pre-commit package described in the documentation and everything was either passed or skipped.
This change added a short test for GH33787, which was to make sure that read_json was able to convert lists with date values.
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} | 9 | "2021-08-04T23:38:27Z" | "2021-11-07T15:33:42Z" | null | MEMBER | null | This breaks the last corner case I want to test for #33036.
```
arr = np.random.randn(4, 3).astype("int64")
df = pd.DataFrame(arr).astype("Int64")
df.iloc[:, 1] = pd.NA # <-- incorrectly casts to object, lets cast back and ignore that for now
df = df.astype("Int64")
res = df.mean()
>>> res
0 0.0
1 <NA>
2 -0.25
dtype: object
```
Trivial to fix with 2D EAs, not sure how/if to fix it without. | {
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} | 1 | "2021-08-05T01:51:16Z" | "2021-08-05T15:16:55Z" | "2021-08-05T12:04:11Z" | CONTRIBUTOR | null | - [ ] xref #27085 (chips away at, yes)
- [x] tests added / passed
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Continuation of https://github.com/pandas-dev/pandas/pull/42284
Moving CustomBusinessDay / CDay tests into own file. | {
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} | 0 | "2021-08-05T02:43:51Z" | "2021-08-05T23:52:52Z" | "2021-08-05T22:00:36Z" | MEMBER | null | - [ ] closes #xxxx
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] | closed | false | null | [] | null | 2 | "2021-08-05T02:50:10Z" | "2021-08-05T04:08:31Z" | "2021-08-05T04:08:31Z" | NONE | null | i have a .py document work ok in before edition, but fail in 1.3.1
these two sentence fail:
a = abc.loc[:, ['name', 'gl', 'time']]
output = a.groupby(['name']).sum().reset_index()
it run ok before, but under pandas edition 1.3.1, it report fail.
i found output is not what i want , it only have one series with 'name'. the output dataframe drop 'gl' and 'time' list.
it seems groupby function not work well.
so strange is , i found if i use a.groupby(['name']).sum(0).reset_index(drop=False), i add a 0 into sum(), it work well and output is ok.
so strange...
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] | closed | false | null | [] | null | 1 | "2021-08-05T07:56:05Z" | "2021-08-10T19:29:41Z" | "2021-08-10T19:29:41Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
rand = np.random.RandomState(1)
df = pd.DataFrame(
{
'A': ['foo', 'bar'] * 3,
'B': rand.randn(6),
'C': rand.randint(0, 20, 6)
}
)
gb = df.groupby(['A', 'C'])
x = gb.apply(pd.DataFrame.to_numpy).tolist() # This works
y = gb[['B', 'C']].apply(pd.DataFrame.to_numpy).tolist() # This does not work
```
#### Problem description
In the snippet above `x` works and contains list of arrays as expected. In the next line, selecting subset of columns in the `DataFrameGroupBy` object then performing the same operations should also work as expected. However, `y` raises following error:
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
1252 try:
-> 1253 result = self._python_apply_general(f, self._selected_obj)
1254 except TypeError:
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f, data)
1288
-> 1289 return self._wrap_applied_output(
1290 data, keys, values, not_indexed_same=mutated or self.mutated
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/groupby/generic.py in _wrap_applied_output(self, data, keys, values, not_indexed_same)
1166 # after raising AttributeError above
-> 1167 return self.obj._constructor_sliced(
1168 values, index=key_index, name=self._selection
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
352
--> 353 name = ibase.maybe_extract_name(name, data, type(self))
354
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/indexes/base.py in maybe_extract_name(name, obj, cls)
6392 if not is_hashable(name):
-> 6393 raise TypeError(f"{cls.__name__}.name must be a hashable type")
6394
TypeError: Series.name must be a hashable type
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-1-d8f27e6031fe> in <module>
11 gb = df.groupby(['A', 'C'])
12 x = gb.apply(pd.DataFrame.to_numpy).tolist()
---> 13 y = gb[['B', 'C']].apply(pd.DataFrame.to_numpy).tolist()
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
1262
1263 with group_selection_context(self):
-> 1264 return self._python_apply_general(f, self._selected_obj)
1265
1266 return result
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f, data)
1287 keys, values, mutated = self.grouper.apply(f, data, self.axis)
1288
-> 1289 return self._wrap_applied_output(
1290 data, keys, values, not_indexed_same=mutated or self.mutated
1291 )
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/groupby/generic.py in _wrap_applied_output(self, data, keys, values, not_indexed_same)
1165 # TODO: sure this is right? we used to do this
1166 # after raising AttributeError above
-> 1167 return self.obj._constructor_sliced(
1168 values, index=key_index, name=self._selection
1169 )
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
351 else:
352
--> 353 name = ibase.maybe_extract_name(name, data, type(self))
354
355 if is_empty_data(data) and dtype is None:
~/.conda/envs/PyDev/lib/python3.9/site-packages/pandas/core/indexes/base.py in maybe_extract_name(name, obj, cls)
6391 # GH#29069
6392 if not is_hashable(name):
-> 6393 raise TypeError(f"{cls.__name__}.name must be a hashable type")
6394
6395 return name
TypeError: Series.name must be a hashable type
```
#### Expected Output
`y` would be a list of array containing only selected columns (as specified).
#### Related issues
[#18930](https://github.com/pandas-dev/pandas/issues/18930)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.13.5-1-MANJARO
Version : #1 SMP PREEMPT Mon Jul 26 07:43:29 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.19.0
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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] | open | false | null | [] | null | 0 | "2021-08-05T08:13:12Z" | "2021-08-21T20:37:33Z" | null | NONE | null | [Question I asked on stackoverflow with example-code](https://stackoverflow.com/questions/68662268/pandas-series-replace-not-generating-default-value-from-defaultdict/)
#### Acceptable alternative
One could explicitly call a non-existing key with .map() instead of .replace(). And this would serve as a good alternative to the functionality Im after.
df['code'].map(recode)
#### Feature request
I wish to provide a "default value" in a dict-like datastructure to the replace() function. I'm open to other suggestions instead of defaultdict, that incorporates the "default value" within the recode-lookup-datastructure.
#### Describe the solution you'd like
Something smarter in the replace-function than checking if the "key is in the mapping", like "could the generator create a value for this key". I think this is the line in question:
https://github.com/pandas-dev/pandas/blob/e045034e5c89b932a526d6c9e691d3031784c377/pandas/core/frame.py#L5248
#### API breaking implications
Would maybe be more processing-intensive, should maybe branch off from "ordinary dict" logic at some point.
#### Describe alternatives you've considered
.fillna() afterwards would fill empty fields, but replace does not replace values not in the mapping. So you are stuck with the old values, not nan or similar. Its always possible to write logic around this. But this will be a very common need. Maybe a "default value" could be passed to replace() instead, but it would then be harder to incorporate in the incoming dict-like structure.
#### Additional context
```python
import pandas as pd
from collections import defaultdict
recode = defaultdict(lambda:"Unknown", {
1 : "Yes",
2 : "No"
})
print("key 0:", recode[0]) # Will generate a key-value for the key "0", but none will exist for recode[5]
df = pd.DataFrame(pd.Series([0,1,2,5]), columns = ["code"])
df['answer'] = df['code'].replace(recode)
print(df)
print(0 is in recode)
print(5 is in recode)
```
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} | 2 | "2021-08-05T09:22:13Z" | "2021-08-12T09:05:44Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas. (tested 1.3.1)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# This returns a pd.Series with 0 items
pd.DataFrame.from_dict({
'timestamp': pd.DatetimeIndex([]),
'symbol': np.array([], dtype=object),
'price': np.array([], dtype='f4'),
}).set_index('timestamp').groupby('symbol').resample('D', closed='right')['price'].ohlc()
# This returns a pd.DataFrame with columns open, high, low, close and a single row (index=[(ABC, 2021-01-01)])
pd.DataFrame.from_dict({
'timestamp': pd.DatetimeIndex(['2021-01-01']),
'symbol': np.array(['ABC'], dtype=object),
'price': np.array([100], dtype='f4'),
}).set_index('timestamp').groupby('symbol').resample('D', closed='right')['price'].ohlc()
```
#### Problem description
It is surprising that `groupby.ohlc` returns different data types depending on whether or not there are zero rows.
#### Expected Output
I think it would be more sensible for the zero row case to return a `DataFrame` with columns open/high/low/close, but with zero rows. Returning a `Series` as a special case doesn't make sense.
#### Output of ``pd.show_versions()``
(This failed with an error about llvmlite.dll in the environment with pandas 1.3.1 installed, so this output is actually from a slightly earlier install of Pandas which still exhibits the bug.)
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18363
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.18.0
xlrd : 2.0.1
xlwt : None
numba : 0.53.0
</details>
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} | 6 | "2021-08-05T12:31:35Z" | "2021-09-06T20:37:19Z" | "2021-09-06T20:37:19Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
When you mix `resample` with `groupby` and try to use the `agg` method to supply multiple functions to either a DataFrameGroupBy or SeriesGroupBy, Python suddently exits without even raising an error.
I first thought I was running into this because I was supplying a single column expecting a DataFrame with multiple columns, but I can confirm this happens to me whether I provide a column (variable `b`) or apply the method to the entire GroupBy (variable `c`):
#### Code Sample
```python
import pandas as pd
a = pd.DataFrame({
'class': {
0: 'beta', 1: 'alpha', 2: 'alpha', 3: 'gaga', 4: 'beta', 5: 'gaga',
6: 'beta', 7: 'gaga', 8: 'beta', 9: 'gaga', 10: 'alpha', 11: 'beta',
12: 'alpha', 13: 'gaga', 14: 'alpha'},
'value': {
0: 69, 1: 33, 2: 40, 3: 2, 4: 36, 5: 40, 6: 48, 7: 84, 8: 77, 9: 22,
10: 55, 11: 82, 12: 37, 13: 88, 14: 41},
'date': {
0: pd.Timestamp('2021-02-28 00:00:00'),
1: pd.Timestamp('2021-11-30 00:00:00'),
2: pd.Timestamp('2021-02-28 00:00:00'),
3: pd.Timestamp('2021-04-30 00:00:00'),
4: pd.Timestamp('2021-02-28 00:00:00'),
5: pd.Timestamp('2021-04-30 00:00:00'),
6: pd.Timestamp('2021-07-31 00:00:00'),
7: pd.Timestamp('2021-01-31 00:00:00'),
8: pd.Timestamp('2021-01-31 00:00:00'),
9: pd.Timestamp('2021-01-31 00:00:00'),
10: pd.Timestamp('2021-04-30 00:00:00'),
11: pd.Timestamp('2021-10-31 00:00:00'),
12: pd.Timestamp('2021-09-30 00:00:00'),
13: pd.Timestamp('2021-04-30 00:00:00'),
14: pd.Timestamp('2021-05-31 00:00:00')}})
# This will exit Python
b = a\
.set_index('date')\
.groupby('class')\
.resample('M')['value']\
.agg(['sum', 'size'])
# Not informing a column will ALSO make Python exit
c = a\
.set_index('date')\
.groupby('class')\
.resample('M')\
.agg(['sum', 'size'])
```
#### Problem description
I'm not sure if this method is supported for instances of `DatetimeIndexResamplerGroupby` objects, but calling it without arguments is valid, giving:
```
<bound method Resampler.aggregate of <pandas.core.resample.DatetimeIndexResamplerGroupby object at 0x00000163B22B0100>>
```
Also, while the problem arises with either a Series or a DataFrame, given that using `agg` with multiple functions on a `SeriesGroupBy` will correctly create a DataFrame, I would expect the same to happen when resampling with timestamps:
```python
In [1]: a.groupby('class')['value'].agg(['sum', 'size'])
Out[1]:
sum size
class
alpha 206 5
beta 312 5
gaga 236 5
```
#### Expected Output
```
sum size
class date
alpha 2021-02-28 40 1
2021-03-31 0 0
2021-04-30 55 1
2021-05-31 41 1
2021-06-30 0 0
2021-07-31 0 0
2021-08-31 0 0
2021-09-30 37 1
2021-10-31 0 0
2021-11-30 33 1
beta 2021-01-31 77 1
2021-02-28 105 2
2021-03-31 0 0
2021-04-30 0 0
2021-05-31 0 0
2021-06-30 0 0
2021-07-31 48 1
2021-08-31 0 0
2021-09-30 0 0
2021-10-31 82 1
gaga 2021-01-31 106 2
2021-02-28 0 0
2021-03-31 0 0
2021-04-30 130 3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : pt_BR.cp1252
pandas : 1.3.1
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.24.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : 1.0.8
s3fs : None
scipy : 1.7.0
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/42906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42906/events | https://github.com/pandas-dev/pandas/issues/42906 | 962,002,166 | MDU6SXNzdWU5NjIwMDIxNjY= | 42,906 | BUG:Getting KeyError when saving style multiindex pandas to excel | {
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When saving a `df` that has been style into excel, compiler return
> KeyError: "['A'] not in index"
import pandas as pd
import numpy as np
def color_negative_red(value):
if value < 0:
color = 'red'
elif value > 0:
color = 'green'
else:
color = 'black'
return 'color: %s' % color
np.random.seed(0)
arrays = [np.hstack([['One']*2, ['Two']*2]) , ['A', 'B', 'A', 'B']]
columns = pd.MultiIndex.from_arrays(arrays)
df= pd.DataFrame(np.random.randn(5, 4), columns=list('ABCD'))
df.columns = columns
data=df.style.applymap(color_negative_red, subset=['A'])
data.to_excel('test.xlsx')
Expected output
[![enter image description here][1]][1]
p.s. similar post has been post at SO: https://stackoverflow.com/questions/68670375/getting-keyerror-when-saving-style-multiindex-pandas-to-excel
[1]: https://i.stack.imgur.com/WafAy.png | {
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} | 3 | "2021-08-05T17:25:37Z" | "2021-08-21T20:38:29Z" | null | NONE | null | #### Location of the documentation
https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.transform.html
#### Documentation problem
In my eyes, the documentation makes it very unclear how `func` (which is also mislabeled `f` in the parameter descriptions) is actually applied to each group.
General description:
> Call function producing a like-indexed DataFrame on each group and return a DataFrame having the same indexes as the original object filled with the transformed values
Description of parameter `func`:
> Function to apply to each group. [...]
I'm not sure if I'm just confused by this or if the documentation is actually misleading, because for me this is implying that `transform` takes a function which is called for each group and thus accepts a `DataFrame` as an argument.
This however is not the case, as `transform` actually applies `func` to _each column within each group_.
Only this phrase in the 'Notes' section (and the examples) somewhat hint at this functionality, in my opinion:
> if this is a DataFrame, f must support application column-by-column in the subframe
(which I find kind of confusing as well, to be honest, as I expected that the "this" in this case would always be a `DataFrame`, because it is a method of 'groupby. _DataFrame_ GroupBy')
On the other hand, the shorthand explanation in the 'See also' about `transform` section on other pages like [GroupBy.apply](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.apply.html) is much more concise in my opinion:
> transform: Apply function column-by-column to the GroupBy object.
Which to me, as an unexperienced pandas user, makes it crystal-clear what the function is supposed to do, as opposed to "Call function producing a like-indexed DataFrame on each group", which is rather ominous to me. It took me a while to figure out that [GroupBy.apply](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.apply.html) is what I actually needed.
#### Suggested fix for documentation
Change the general description and `func` parameter description to include something along the lines of "Apply function column-by-column to the GroupBy object", which is already used as a short description as mentioned before, and fix the mislabeled parameter. And maybe the "if this is a DataFrame" phrase in the notes should be changed as well. | {
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carries on from https://github.com/pandas-dev/pandas/pull/35428 | {
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} | 1 | "2021-08-05T22:01:29Z" | "2021-08-06T00:00:38Z" | "2021-08-05T23:39:11Z" | CONTRIBUTOR | null | - [ ] xref #27085 (chips away at, yes)
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Continuation of:
* https://github.com/pandas-dev/pandas/pull/42284
* https://github.com/pandas-dev/pandas/pull/42896
This change moves business month tests to own file and moves some displaced month tests into `test_month.py`. | {
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} | 1 | "2021-08-05T22:55:54Z" | "2021-08-06T06:02:24Z" | "2021-08-05T23:34:50Z" | CONTRIBUTOR | null | removes `print` from tests.
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} | 6 | "2021-08-06T00:53:57Z" | "2021-09-18T17:45:41Z" | "2021-09-04T14:09:45Z" | MEMBER | null | - [ ] closes #41157
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Triggers with command ``@github-actions benchmark -b ^regexgoeshere``(doesn't work rn, needs to be merged to work, this is an example result though, https://github.com/lithomas1/pandas/pull/6#issuecomment-893920379, which is me testing it on the io.csv benchmarks). Our benchmarks are super slow right now, they take six hours to do, so having this bot might help for people with weaker computers. Unfortunately, this also can clog up our CI pipelines, so I added a concurrency limit, which restricts runs to 1 per person.
cc @jbrockmendel @simonjayhawkins
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} | 1 | "2021-08-06T07:41:22Z" | "2021-08-06T07:50:31Z" | "2021-08-06T07:50:11Z" | NONE | null | I want to generate sentece_id from the word column. Between two fullstop puncuation ( | ,?, !) all the words will be counted as a sentence_id .
I have two columns named as Words and Labels . I wanna generate a new column which specify the words of the same sentences as an unique id . I have the words and labels column .
Can anyone help me ?
In this format I'm trying to convert - (https://simpletransformers.ai/docs/ner-data-formats/)
my dataframe is kinda look like this. -
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} | 1 | "2021-08-06T10:31:54Z" | "2021-08-06T10:42:28Z" | "2021-08-06T10:42:12Z" | NONE | null | #### Is your feature request related to a problem?
Indexed data on series types does not support inverse indexing (like lists in Python).
#### Describe the solution you'd like
In the Series type, I wish negative index to count data from the back.
#### Additional context
```python
r = [1, 2]
ser = pd.Series(r, copy=False)
ser.iloc[0] = 999
print(ser)
# 0 999
# 1 2
# dtype: int64
print(ser[0])
# 999
print(ser[-1])
# KeyError: -1
```
expected data ( it is not good looking )
```python
print(ser[len(ser) - 1])
# 2
```
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} | 1 | "2021-08-06T12:00:09Z" | "2021-08-09T10:49:14Z" | "2021-08-09T10:49:08Z" | CONTRIBUTOR | null | follows #42636, which added `levels` to `Styler.hide_index`. This adds same functionality.
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} | 2 | "2021-08-06T12:25:57Z" | "2021-10-16T18:28:29Z" | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
import pandas as pd
print(pd.__version__)
df_a = pd.DataFrame({"value": range(10), "idx1": [1] * 5 + [2] * 5, "idx2": [1, 2, 3, 4, 5] * 2}).set_index(["idx1", "idx2"])
df_b = pd.DataFrame({"value": range(5), "idx2": [1, 2, 3, 4, 5]}).set_index("idx2")
test = df_a.groupby(level=0).rolling(2).cov(df_b)
print(test)
```
On pandas 1.3.1:
```python
1.3.1
value
idx1 idx1 idx2
1 1 1 NaN
2 0.5
3 0.5
4 0.5
5 0.5
2 1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
2 1 1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
2 1 NaN
2 0.5
3 0.5
4 0.5
5 0.5
```
On pandas 1.0.5:
```python
1.0.5
value
idx1 idx1 idx2
1 1 1 NaN
2 0.5
3 0.5
4 0.5
5 0.5
2 2 1 NaN
2 0.5
3 0.5
4 0.5
5 0.5
```
1.0.5 has the correct expected output. In 1.3.1, everything in index 1/2/x and 2/1/x is useless and will always be NaN.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.4.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 : English_United States.1252
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.2
setuptools : 54.1.2
Cython : None
pytest : 6.2.4
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 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.22
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 5 | "2021-08-06T14:02:50Z" | "2021-08-15T17:45:25Z" | null | NONE | null | #### Is your feature request related to a problem?
Pandas does not allow anti-joins. It would be helpful to have these added
Pyspark has implemented this on its 'join' command.
https://spark.apache.org/docs/latest/sql-ref-syntax-qry-select-join.html
#### Describe the solution you'd like
Add anti-left and anti-right joins to 'how'
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html
#### API breaking implications
None
#### Describe alternatives you've considered
You can replicate an anti-join by doing an outer join, and filtering.
My code does work but sometimes gives too many columns - not sure why. Also my code relies on the column name not already containing '_drop' - which obviously could be an issue.
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
import pandas as pd
def left_anti_join(df1,df2,left_on,right_on):
""" Perfoms left anti join of two data frames"""
df=pd.merge(df1,df2,left_on=left_on,right_on=right_on,suffixes=('','_drop'), how='outer', indicator=True)
df=df[df['_merge']=='left_only']
df.drop([col for col in df.columns if '_drop' in col],axis=1, inplace=True)
df.drop(columns="_merge", inplace=True)
return df
```
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} | 2 | "2021-08-06T15:22:38Z" | "2021-08-10T17:57:58Z" | "2021-08-10T16:29:23Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Code
import pandas as pd
import numpy as np
# import xlwings as xw
import openpyxl
# from pyxlsb import open_workbook as open_xlsb
from urllib.request import urlopen
import json
import itertools
filename = 'excel\inputFile.xlsb'
datasheet = 'Sheet1'
df = pd.read_excel(filename, sheet_name=datasheet, skiprows = 0, engine='pyxlsb', header=None)
print('List of columns')
print(df[1])
indx_BU = df[1].loc[lambda x: x=='Busienss unit (MSK/SCL)'].index
print(indx_BU)
Problem description
I have read the excel sheet and store it in the data frame (df). if I print the column[1] values I had the value 'Business unit (MSK/SCL)'.
But I won't get any details if I tried to get the index location of 'Business unit (MSK/SCL)'. Please do the needful.
FYR, I have attached the screenshot.
Note: Sometimes it is working even without change any code in the file.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
python : 3.9.0.final.0
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : 1.0.8
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
None
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
Given a csv file `test.csv`(sorry, didn't manage to reproduce without reading a `csv`) with the following content:
```
x,y
00000114136524405793555657657729584885,
```
```python
df = pd.read_csv('test.csv', dtype={'x': 'string', 'y': 'string'})
df['y'].fillna({}, inplace=True)
```
#### Problem description
Above code fails with an error `ValueError: Must provide strings.`. Full traceback below in the details.
<details>
```
Traceback (most recent call last):
File "test.py", line 3, in <module>
df['y'].fillna({}, inplace=True)
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/series.py", line 4529, in fillna
return super().fillna(
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/generic.py", line 6057, in fillna
new_data = self._mgr.fillna(
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 585, in fillna
return self.apply(
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 406, in apply
applied = getattr(b, f)(**kwargs)
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/internals/blocks.py", line 1779, in fillna
values = values.fillna(value=value, limit=limit)
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/arrays/string_.py", line 262, in fillna
return super().fillna(value, method, limit)
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/arrays/numpy_.py", line 307, in fillna
new_values[mask] = value
File "/home/user/.local/lib/python3.8/site-packages/pandas/core/arrays/string_.py", line 256, in __setitem__
raise ValueError("Must provide strings.")
ValueError: Must provide strings.
```
</details>
On the other hand, when the `dtype` object is `{'x': str, 'y': str}`, the error is not raised - from [the docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/basics.html#dtypes) I understood both versions should be equivalent?
#### Expected Output
Both versions have the same behavior.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-25-generic
Version : #27~20.04.1-Ubuntu SMP Tue Jul 13 17:41:23 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 44.0.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : 2021.07.0
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
None
</details>
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} | 7 | "2021-08-06T16:40:31Z" | "2021-08-09T17:05:55Z" | "2021-08-08T23:09:19Z" | MEMBER | null | - [ ] closes #42588
- [ ] tests added / passed
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Also adds support for ``use_nullable_dtypes`` keyword. Technically, its not our enhancement its fastparquet's, so I think can backport. IMO, users shouldn't have to wait 6 months for 1.4 to get this new feature.
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} | 3 | "2021-08-06T19:40:42Z" | "2021-09-01T05:46:11Z" | "2021-08-31T23:43:10Z" | CONTRIBUTOR | null | - [x] closes #40830
- [x] tests added / passed
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