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- [X ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
import numpy as np
test_df1 = pd.DataFrame({'a':[1,2,3,4],'b':['a','b','c','d'],'c':[np.nan,np.nan,np.nan,np.nan]})
test_df2 = pd.DataFrame({'a':[1,2,3,4],'b':[np.nan,np.nan,np.nan,np.nan],'c':['a','b','c','d']})
def drop_na_test1(df):
df = df.dropna(axis=1,how='all')
return df
def drop_na_test2(df):
df.dropna(axis=1,how='all',inplace=True)
return df
dfs_to_process = [test_df1,test_df2]
for df in dfs_to_process:
df = drop_na_test1(df)
display(test_df1)
display(test_df2)
for df in dfs_to_process:
df = drop_na_test2(df)
display(test_df1)
display(test_df2)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
Assigning back dataframe with dropna() should yield same result as dropna(inplace=True).
#### Expected Output
Expect same result with df=df.dropna() as df.dropna(inplace=True)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
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} | 3 | "2021-04-29T19:12:36Z" | "2021-04-30T22:29:25Z" | "2021-04-30T17:17:29Z" | 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.
---
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': ['a', np.nan, 'a', 'a'], 'B': ['b', 'b', np.nan, 'b'], 'X': [5, 10, 15, 20]})
A B X
0 a b 5
1 NaN b 10
2 a NaN 15
3 a b 20
pd.pivot_table(df, index=['A', 'B'], values=['X'], aggfunc=np.sum)
A B X
0 a b 25
```
#### Problem description
Certain rows are being ignored during aggregation by pivot_table method when the data contains any missing values in index columns.
#### Expected Output
```python
pd.pivot_table(df, index=['A', 'B'], values=['X'], aggfunc=np.sum)
A B X
0 a b 25
1 NaN b 10
2 a NaN 15
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-29T19:36:34Z" | "2021-05-01T08:38:44Z" | "2021-04-29T22:08:09Z" | MEMBER | null | ```
[ 50.00%] ··· strings.Methods.time_startswith ok
[ 50.00%] ··· ============== ==========
dtype
-------------- ----------
str 19.2±0ms
string 16.0±0ms
arrow_string 2.42±0ms
============== ==========
[ 50.00%] ··· strings.Methods.time_endswith ok
[ 50.00%] ··· ============== ==========
dtype
-------------- ----------
str 19.3±0ms
string 15.0±0ms
arrow_string 6.68±0ms
============== ==========
``` | {
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} | 5 | "2021-04-29T20:09:15Z" | "2021-05-21T15:57:25Z" | "2021-05-21T15:57:21Z" | CONTRIBUTOR | null | - [x] closes #40904
- [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
- Already have one for the same bug, not sure if I need one for this.
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} | 1 | "2021-04-29T20:33:57Z" | "2021-04-30T19:04:23Z" | "2021-04-30T17:18:46Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Followup to #41067. cc @jbrockmendel | {
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} | 4 | "2021-04-29T20:35:43Z" | "2021-05-21T01:44:13Z" | "2021-05-21T01:44:13Z" | CONTRIBUTOR | null | Pandas 1.2.4 fails to open XLS files generated by Lotus 1-2-3 because they have a different header than the one expected in `XLS_SIGNATURE` (`io.excel._base` line 1001 and then 1050).
I know I'm likely to be the only person in the world with this issue, but my boss still uses Lotus 1-2-3 🙄
I'm willing to submit a pull request with my fix if people are okay with it. The fix basically involves changing `XLS_SIGNATURE` to a list:
```
XLS_SIGNATURES = [
b"\xD0\xCF\x11\xE0\xA1\xB1\xA1\xE1",
b"\t\x04\x06",
]
ZIP_SIGNATURE = b"PK\x03\x04"
PEEK_SIZE = max(map(len, XLS_SIGNATURES + [ZIP_SIGNATURE]))
```
and then later:
```
if any(peek.startswith(signature) for signature in XLS_SIGNATURES):
return "xls"
```
I don't know if the second `XLS_SIGNATURES` value is "good," but it works. I can attach an XLS that was exported by 1-2-3 if it would be beneficial to others, or if they'd be able to help me find a better second value for `XLS_SIGNATURES`. I tried looking through the byte-code for anything similar to the "DOCFILE" XLS signature, but didn't see anything.
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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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} | 4 | "2021-04-29T23:13:08Z" | "2021-05-06T04:00:43Z" | "2021-05-06T01:41:26Z" | CONTRIBUTOR | null | - [x] closes #41167
- [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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- [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.
---
[previous commit #36710](https://github.com/pandas-dev/pandas/pull/36710)
```python
def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame:
if not isinstance(periods, int):
if not (is_float(periods) and periods.is_integer()):
raise ValueError("periods must be an integer")
periods = int(periods)
bm_axis = self._get_block_manager_axis(axis)
if bm_axis == 0 and periods != 0:
return self - self.shift(periods, axis=axis)
new_data = self._mgr.diff(n=periods, axis=bm_axis)
return self._constructor(new_data).__finalize__(self, "diff")
```
If periods is float, then it can't pass `(is_float(periods) and periods.is_integer())`, and ValueError will always be raised
So I assume it should be `or` here?
```python
# suggested:
# here str can't pass through, I'm not sure if str should be supported as well
if not (is_float(periods) or is_integer(periods)):
raise ValueError("periods must be an integer or float")
```
Besides, I think `.diff()` should support float for backward compatibility
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-140-generic
Version : #144-Ubuntu SMP Fri Mar 19 14:12:35 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : 0.9.3
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.53.1563e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-140-generic
Version : #144-Ubuntu SMP Fri Mar 19 14:12:35 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : 0.9.3
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-04-30T02:58:55Z" | "2021-06-18T02:29:52Z" | "2021-04-30T14:54:54Z" | MEMBER | null | `float()` raises TypeError in py310
```
________________ TestEnsureNumeric.test_non_convertable_values _________________
self = <pandas.tests.test_nanops.TestEnsureNumeric object at 0x7f7da1e029e0>
def test_non_convertable_values(self):
msg = "Could not convert foo to numeric"
with pytest.raises(TypeError, match=msg):
nanops._ensure_numeric("foo")
# with the wrong type, python raises TypeError for us
msg = "argument must be a string or a number"
with pytest.raises(TypeError, match=msg):
> nanops._ensure_numeric({})
pandas/tests/test_nanops.py:771:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
x = {}
def _ensure_numeric(x):
if isinstance(x, np.ndarray):
if is_integer_dtype(x) or is_bool_dtype(x):
x = x.astype(np.float64)
elif is_object_dtype(x):
try:
x = x.astype(np.complex128)
except (TypeError, ValueError):
try:
x = x.astype(np.float64)
except (TypeError, ValueError) as err:
# GH#29941 we get here with object arrays containing strs
raise TypeError(f"Could not convert {x} to numeric") from err
else:
if not np.any(np.imag(x)):
x = x.real
elif not (is_float(x) or is_integer(x) or is_complex(x)):
try:
> x = float(x)
E TypeError: float() argument must be a string or a real number, not 'dict'
pandas/core/nanops.py:1600: TypeError
``` | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/41232 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41232/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41232/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41232/events | https://github.com/pandas-dev/pandas/pull/41232 | 872,070,290 | MDExOlB1bGxSZXF1ZXN0NjI3MDM2NDUx | 41,232 | ENH: Add formatters parameter for the xlsxwriter engine | {
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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
Currently pandas doesn't support different formats for columns of the type `date`/`datetime`. Giving the provision to do so using the `formatters` parameter as shown below:
**Master**
```
from datetime import datetime
df = pd.DataFrame(
{
"Col1": [1010.34, 2020.67, 3030.65,3210.65],
"Col2": [
datetime(2014, 1, 31),
datetime(2014, 1, 31),
datetime(2014, 1, 31),
datetime(2014, 1, 31)
],
"Col3": [
datetime(2014, 1, 31),
datetime(2014, 1, 31),
datetime(2014, 1, 31),
datetime(2014, 1, 31)
],
"Col4": [
datetime(2014, 1, 31),
datetime(2014, 1, 31),
datetime(2014, 1, 31),
datetime(2014, 1, 31)]
},
)
writer = pd.ExcelWriter("pd_datetime.xlsx",
engine='xlsxwriter',
datetime_format='MM/DD/YYYY HH:MM:SS',
date_format='MM/DD/YYYY')
df.to_excel(writer, sheet_name='Sheet1')
writer.save()
```
**Output of the file**

**Proposed change**
```
writer = pd.ExcelWriter(
"pd_datetime.xlsx",
engine="xlsxwriter",
datetime_format="MM/DD/YYYY HH:MM:SS",
date_format="MM/DD/YYYY",
formatters={"Col2": "mmmm", "Col3": "YYYY"},
)
df.to_excel(writer, sheet_name="Sheet1")
writer.save()
```
**Output of the file**

The `date`/`datetime` columns that do not have a format specified in the `formatters` dict will take the format from the `date_format`/`datetime_format` parameters.
PS- The proposed change is currently applicable **only** for the `date`/`datetime` types but can easily be extended for `int` and `float` as well. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41233 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41233/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41233/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41233/events | https://github.com/pandas-dev/pandas/issues/41233 | 872,188,118 | MDU6SXNzdWU4NzIxODgxMTg= | 41,233 | BUG: histogram plot ignores `xlabel` and `ylabel` arguments | {
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] | open | false | null | [] | null | 2 | "2021-04-30T09:01:24Z" | "2021-06-06T17:18:44Z" | 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 numpy as np
import pandas as pd
data = pd.Series(np.random.random(10))
##problem
data.plot.hist(xlabel="My xlabel", ylabel="My ylabel")
```
Output graph:

#### Problem description
The method accepts the keyword arguments but ignores them and overrides them with defaults (respectively <nothing> and "Frequency". While those defaults are sensible, specifying those keyword arguments should either be met with the intended output (custom x and y labels) or an error (of the type `NotImplementedError`)
The documentation (for [hist](https://github.com/pandas-dev/pandas/blob/2cb96529396d93b46abab7bbc73a208e708c642e/pandas/plotting/_core.py#L1266) and [plot](https://github.com/pandas-dev/pandas/blob/2cb96529396d93b46abab7bbc73a208e708c642e/pandas/plotting/_core.py#L680)) seem to indicate this is supported (although a bit unclear)
#### Expected Output
The outputted graph should be the same as when produced with the following code:
```python
import numpy as np
import pandas as pd
data = pd.Series(np.random.random(10))
ax = data.plot.hist()
ax.set_xlabel("My xlabel")
ax.set_ylabel("My ylabel")
```
Which is the following:

#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-7614-generic
Version : #15~1618626693~20.10~ecb25cd-Ubuntu SMP Thu Apr 22 16:00:45 UTC
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : 1.0.2
psycopg2 : None
jinja2 : None
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.7
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41234 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41234/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41234/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41234/events | https://github.com/pandas-dev/pandas/issues/41234 | 872,422,430 | MDU6SXNzdWU4NzI0MjI0MzA= | 41,234 | Regression: concatenating `MultiIndex` with empty `RangeIndex` raises | {
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} | 1 | "2021-04-30T12:21:36Z" | "2021-05-04T12:53:48Z" | "2021-05-04T12:53:48Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
As also reported here: https://github.com/dask/dask/issues/7610, PR https://github.com/pandas-dev/pandas/pull/38671/ introduced the following behaviour:
> A reproducer with just pandas:
>
> ```
> df1 = pd.DataFrame([[1, 2]], columns=pd.MultiIndex.from_tuples([('B', 1), ('C', 1)]))
> df2 = pd.DataFrame(index=[0], columns=pd.RangeIndex(0))
>
> pd.concat([df1, df2])
> ```
>
> What triggers the error here is the `columns=pd.RangeIndex(0)` for the empty `df2` (by default pandas creates a zero-length object dtype Index, which works fine, but if it's an empty RangeIndex, we now get this error).
>
> This is a regression in itself in pandas. But I am also wondering a bit where the empty RangeIndex is coming from (it seems that the groupby operation in dask results in some empty partitions)
_Originally posted by @jorisvandenbossche in https://github.com/dask/dask/issues/7610#issuecomment-827708470_
IIUC the fix for this could possibly be as simple as replacing
https://github.com/pandas-dev/pandas/blob/0acbff881c9273c2cda962fb263f2862a5e7be28/pandas/core/indexes/base.py#L2926-L2928
with
```python
if isinstance(self, ABCMultiIndex) and not is_object_dtype(
unpack_nested_dtype(other)
) and len(other) > 0:
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} | 1 | "2021-04-30T13:13:04Z" | "2021-04-30T14:55:42Z" | "2021-04-30T14:55:38Z" | 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
Simplified kwargs unpacking in resample, by utilizing keyword-only arguments. | {
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} | 2 | "2021-04-30T14:22:38Z" | "2021-08-20T02:44:31Z" | null | NONE | null | Trying to use read_sql_table on an instance without SQLAlchemy installed gives a confusing error message:
> NotImplementedError: read_sql_table only supported for SQLAlchemy connectable.
The issue is that `_is_sqlalchemy_connectable` returns false if SQLAlchemy is not installed. My suggestion is to raise an error stating that SQLAlchemy is required by trying to import it directly in read_sql_table or extending the error message to say that SQLAlchemy is required. I don't know the internals and what might go wrong, but one way would be to not catch the ImportError...
https://github.com/pandas-dev/pandas/blob/49c779118c2d473c60565d9010e119bb62d06f74/pandas/io/sql.py#L308-L311 | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = DataFrame.from_dict({'key': [frozenset([1])], 'value': [2]}).set_index('key')
k = frozenset([1])
k in df.index # True
df.loc[[k]] # OK
df.loc[k] # raises: KeyError: "None of [Int64Index([1], dtype='int64', name='key')] are in the [index]"
df.value.loc[[k]] # OK
df.value.loc[k] # raises: KeyError: "None of [Int64Index([1], dtype='int64', name='key')] are in the [index]"
```
#### Problem description
Frozensets are hashable and could serve as indices. They are only partially supported in the current version:
- set a frozenset as index: OK,
- is frozenset in index: OK,
- index with a collection of frozensets: OK,
- index with a frozenset: KeyError.
This applies to both Series and DataFrame.
Full support would be nice.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.utf8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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} | 0 | "2021-04-30T15:57:45Z" | "2021-04-30T18:12:47Z" | "2021-04-30T16:52:15Z" | MEMBER | null | ATM `isinstance(resampler, ABCDataFrame)` can be True, which I wasn't aware of until recently. This strikes me as a grade-A footgun. | {
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} | 0 | "2021-04-30T18:23:54Z" | "2021-04-30T22:32:23Z" | "2021-04-30T22:12:53Z" | 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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} | 7 | "2021-04-30T18:40:56Z" | "2021-09-29T12:58:50Z" | "2021-09-29T12:58:50Z" | 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_pq = pd.read_parquet(x, use_nullable_dtypes = True)
```
#### Problem description
Get error when add the new parameter **use_nullable_dtypes** to pd.read_parquet().
If remove it , everything go back to normal.
OS: Ubuntu 16
Python: 3.8
A empty parquet file from spark causes the problem. Its schema is:
Authors,AuthorId,int64
Authors,Rank,int32
Authors,NormalizedName,string
Authors,DisplayName,string
Authors,LastKnownAffiliationId,int64
Authors,PaperCount,int64
Authors,PaperFamilyCount,int64
Authors,CitationCount,int64
Authors,CreatedDate,date32[day]
error msg:
df_pq = pd.read_parquet(x,use_nullable_dtypes = True)
File "/vjan/lib/python3.8/site-packages/pandas/io/parquet.py", line 459, in read_parquet
return impl.read(
File "/vjan/lib/python3.8/site-packages/pandas/io/parquet.py", line 221, in read
return self.api.parquet.read_table(
File "pyarrow/array.pxi", line 751, in pyarrow.lib._PandasConvertible.to_pandas
File "pyarrow/table.pxi", line 1668, in pyarrow.lib.Table._to_pandas
File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 792, in table_to_blockmanager
blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)
File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 1133, in _table_to_blocks
return [_reconstruct_block(item, columns, extension_columns)
File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 1133, in <listcomp>
return [_reconstruct_block(item, columns, extension_columns)
File "/vjan/lib/python3.8/site-packages/pyarrow/pandas_compat.py", line 751, in _reconstruct_block
pd_ext_arr = pandas_dtype.__from_arrow__(arr)
File "/vjan/lib/python3.8/site-packages/pandas/core/arrays/integer.py", line 121, in __from_arrow__
return IntegerArray._concat_same_type(results)
File "/vjan/lib/python3.8/site-packages/pandas/core/arrays/masked.py", line 271, in _concat_same_type
data = np.concatenate([x._data for x in to_concat])
File "<__array_function__ internals>", line 5, in concatenate
ValueError: need at least one array to concatenate
#### Expected Output
read the empty parquet file and generate an empty df
#### Output of ``pd.show_versions()``
<details>
1.2.4
</details>
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- [ ] tests added / passed
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- [ ] whatsnew entry
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} | 0 | "2021-04-30T22:01:48Z" | "2021-05-03T01:18:39Z" | "2021-05-02T23:30:45Z" | MEMBER | null | <b>update</b> reverted most of this in favor of #41271 | {
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} | 1 | "2021-04-30T22:09:32Z" | "2021-05-12T09:19:02Z" | "2021-05-12T01:26:19Z" | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Regression on master so no whatsnew. Restored behaviro of 1.2.4
cc @gfyoung | {
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} | 0 | "2021-04-30T22:38:50Z" | "2021-05-03T01:29:35Z" | "2021-05-02T23:29:02Z" | MEMBER | null | AFAICT the only reason `Resampler.__init__` has a lazy component is bc we call `super().__init__(None)` in `GroupByMixin.__init__`. By avoiding that (and directly setting the one attribute we need that super call to set), we can de-lazify `Resampler.__init__`, which in turn allows simplifying a bunch of other methods | {
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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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[ 25.00%] ··· strings.Methods.time_len ok
[ 25.00%] ··· ============== ==========
dtype
-------------- ----------
str 19.1±0ms
string 6.82±0ms
arrow_string 1.93±0ms
============== ==========
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} | 4 | "2021-05-01T21:19:03Z" | "2021-05-05T16:33:16Z" | "2021-05-04T12:59:25Z" | CONTRIBUTOR | null | This does four things to better organize the numeric index's tests:
1. Moves test method `test_index_groupby` from `tests.indexes.test_numeric.py::TestNumericIndex` to `tests.indexes.common.py::Base` and makes it use a fixture. This means this tests will be used for all index types. Also deletes `TestNumericIndex`, as its empty now.
2. Moves the `Numeric` class from `test_numeric.py` to `common.py` and renames it `NumericBase`.
3. Moves the `tests.indexes.numeric.py::TestArithmetic::test_arithmetic_explicit_conversions` tests to the new `common.py::NumericBase` and delete `test_numeric.py::TestArithmetic` class, as it's now empty.
4. Moves the file `tests.indexes.test_numeric.py` to `tests.indexes.numeric.test_numeric.py`.
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Required changes to `DateTimeLikeArrayMixin` for correct compatibility.
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- [ ] add actual example using `where` (beyond my skillset)
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} | 4 | "2021-05-02T01:33:00Z" | "2021-05-03T13:00:03Z" | "2021-05-03T12:46:14Z" | CONTRIBUTOR | null | Typing fixes for `ExtensionArray.unique()` and `ExtensionArray.repeat()`
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} | 1 | "2021-05-02T02:51:41Z" | "2021-05-02T23:11:56Z" | "2021-05-02T23:11:52Z" | MEMBER | null | The _bootlocale module has been removed from Python 3.10.
https://github.com/python/cpython/commit/b62bdf71ea0cd52041d49691d8ae3dc645bd48e1 | {
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} | 2 | "2021-05-02T04:19:33Z" | "2021-05-03T01:19:36Z" | "2021-05-02T23:10:17Z" | 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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} | 3 | "2021-05-02T10:46:15Z" | "2021-05-05T13:41:07Z" | "2021-05-05T12:39:13Z" | CONTRIBUTOR | null | There's currently inconsistent behaviour when converting `RangeIndex` to `CategoricalIndex` using different methods. This fixes that.
```python
>>> ridx = pd.RangeIndex(5)
>>> pd.CategoricalIndex(ridx).categories
RangeIndex(start=0, stop=5, step=1) # both master and this PR
>>> ridx.astype("category").categories
Int64Index([0, 1, 2, 3, 4], dtype='int64') # master
RangeIndex(start=0, stop=5, step=1) # this PR
```
In general, when supplying a Index subclass to `Categorical`/`CategoricalIndex`, the new categories should be of that type (unless the supplied index is a `CategoricalIndex` itself).
Discovered working on tests for #41153. | {
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} | 17 | "2021-05-02T15:39:41Z" | "2021-10-31T14:58:27Z" | "2021-10-31T14:58:27Z" | NONE | null | - [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version 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
# create empty df, and set a multi index
empty_df = pd.DataFrame(columns=["a", "b", "c"])
multi_index_empty_df = empty_df.set_index(keys=["a", "b"])
# index is different when applying shift on grouping when specifying a fill value
assert multi_index_empty_df.groupby(["a","b"]).shift(1).index.names == multi_index_empty_df.groupby(["a","b"]).shift(1, fill_value=0).index.names
```
#### Problem description
I discovered this problem when a unit test failed on a function that performed a groupby and shift on an empty dataframe.
Specifying `fill_value` in pandas.DataFrame.shift() should not alter the index that was set on a dataframe. This is not the case for empty dataframes with a multi-index, as the example above shows.
#### Expected Output
When executing:
```python
multi_index_empty_df.groupby(["a","b"]).shift(1).index.names
```
I get the output:
```
FrozenList(['a', 'b'])
```
But when executing
```python
multi_index_empty_df.groupby(["a","b"]).shift(1, fill_value=0).index.names
```
I should get the same output, but instead I get
```
FrozenList([None])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2018.7
dateutil : 2.7.5
pip : 18.1
setuptools : 40.6.3
Cython : 0.29.2
pytest : 4.0.2
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.2.2
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.0.2
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : 1.2.15
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
numba : 0.41.0
</details>
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} | 0 | "2021-05-02T15:40:41Z" | "2021-05-03T16:51:38Z" | "2021-05-03T15:14:58Z" | MEMBER | null | Untangling these layered try/excepts is turning out to be an exceptional PITA, so splitting it into extra-small pieces. | {
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} | 8 | "2021-05-02T16:02:20Z" | "2021-07-27T18:03:50Z" | "2021-06-16T00:22:53Z" | CONTRIBUTOR | null | This closes #41158 (which is an alternative PR for similar functionality)
Currently `hide_index()` and `hide_columns(subset)` have similar signatures but **different** operations. One hides the index whilst showing the data-rows, and the other hides data-columns whilst showing the column-headers row.
### This PR
- adds the `subset` keyword to give: `hide_index(subset=None)`.
- sets the default to `hide_columns(subset=None)`.
When `subset` is None the function now operates to hide the entire index, or entire column headers row respectively.
When `subset` is not None it operates to selectively hide the given rows or columns respectively, keeping the index or column headers row.
<s>We also add the `show` keyword to allow the method to operate inversely.</s>
@jreback I think you will prefer this over the `hide_values` and `hide_headers` methods suggested in #41158 since this re-uses the existing methods with minimal changes but tries to align their consistency. It is also fully backwards compatible.
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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 | "2021-05-02T17:55:43Z" | "2021-05-03T18:30:08Z" | "2021-05-02T23:47:54Z" | MEMBER | null | - [x] closes #40440
- [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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I'll adress the IntegerArray case from #40933 after this is merged | {
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} | 1 | "2021-05-02T18:38:41Z" | "2021-05-07T05:18:04Z" | "2021-05-06T23:32:02Z" | CONTRIBUTOR | null | - [x] closes #41143
The PR aims to make Styler compatible with non-unique indexes/columns, for the purpose of rendering all DataFrame types (even if no styling is applied)
- [x] `Styler.format`: made FULLY compatible with some modifications to the loops, inc TESTS.
- [x] <s>`Styler.apply` and `Styler.applymap`: made PARTIALLY compatible:
- if `subset`s are unique then compatible, inc TESTS.
- if `subset`s are non-unique slices will raise a not compatible `KeyError`, inc. TESTS</s>
- [x] `Styler.apply` and `applymap` are NOT compatible. Raises KeyError.
- [x] `Styler.set_table_styles`: made FULLY compatible and will style multiple rows/columns from a non-unique key, inc TESTS.
- [x] `Styler.set_td_classes` uses `reindex` so is PARTIALLY compatible where `classes` has unique index/columns: now returns a `KeyError` in non-unique case, inc TESTS.
- [x] `Styler.set_tooltips` uses `reindex` so is PARTIALLY compatible where `ttips` has unique index/columns: now returns a `KeyError` in non-unique case, inc TESTS.
- [x] `Styler.hide_index` and `.hide_columns` are already FULLY compatible through existing code (inc TESTS)
- [x] all the `built-in` styling functions use some version of `apply` or `applymap` so are captured by the above cases.
I believe this is all relevant functionality reviewed.
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} | 3 | "2021-05-02T19:37:45Z" | "2021-05-02T23:52:30Z" | "2021-05-02T23:08:51Z" | CONTRIBUTOR | null | follow up to #41083 to change test | {
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} | 1 | "2021-05-02T19:58:37Z" | "2021-05-03T01:24:52Z" | "2021-05-02T23:24:23Z" | MEMBER | null | - make the exception-handling in SeriesGroupBy._cython_agg_general match that in DataFrameGroupBy._cython_agg_general
- remove an unnecessary for loop
- define array_func to match the pattern in DataFrameGroupBy._cython_agg_general in the hopes that we can share these methods before long | {
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} | 19 | "2021-05-02T21:15:20Z" | "2021-11-11T11:24:17Z" | null | CONTRIBUTOR | null | I've made a (draft) PR in #41153 that implements an index class for all the normal numpy numeric dtypes (int64/32/16/8, uint64/32/16/8 an dfloat65/32). There was some discussion in that PR how the public API for this should be, so I'm opening this issue for discussing that.
My plan is to get #41153 merged when its ready, possibly without any public-facing API changes. The public-facing changes would then come afterwards, and after there is an conclusion on how the API should be.
## Summary of the options
As I see it the options for the API is:
|Nr.|Option | Is backwards compat. | complexity |
--- |--- | --- | ---
|1.| Have a single, public `NumericIndex` class for all numeric index types | Yes | Easy |
|2.|Have dtype-specific subclasses (`Int8Index`, `Int16Index`, `Int32Index`, etc), and those can still be backed by a single internal `NumIndex` |Yes|Easy|
|3.| Use the existing, general-purpose `Index` class for these numeric numpy dtypes | No | Harder |
|4.| Use the existing, numeric index classes for these numeric numpy dtypes, so e.g. `Int64Index` can take int32 etc. | No | Easy |
Options 1. and 3. would mean that the existing `Int64Index`, `UInt64Index` and `Float64Index` would be deprecated and removed in pandas 2.0, because their functionality would bw covered by the other index classes.
Option 2. would increase the number of numeric index classes.
Options 4. would extend the functionality of the existing numeric index classes.
So there are quite a few possibilities. Hopefully we can come to a common conclusion.
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} | 1 | "2021-05-02T21:49:16Z" | "2021-05-04T14:04:01Z" | "2021-05-04T12:52:35Z" | MEMBER | null | - [ ] closes #xxxx
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} | 1 | "2021-05-03T07:03:01Z" | "2021-06-02T14:38:08Z" | "2021-05-04T12:53:48Z" | CONTRIBUTOR | null | - [x] closes #41234
- [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
See also https://github.com/dask/dask/issues/7610 and https://github.com/JDASoftwareGroup/kartothek/issues/464. The regression seems to have been introduced in pandas-dev/pandas#38671. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41276 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41276/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41276/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41276/events | https://github.com/pandas-dev/pandas/issues/41276 | 874,321,492 | MDU6SXNzdWU4NzQzMjE0OTI= | 41,276 | QST: Efficiently stylizing and appending new dataframes to existing excel | {
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- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com). See my question [here](https://stackoverflow.com/questions/67336786/efficiently-stylizing-and-appending-new-dataframes-to-existing-csv)
---
I believe that my stackoverflow question is explained pretty well. As such, I will mostly copy paste the relevant info here.
I need to log important information in a human readable format for an app.
The three biggest requirements are as follows:
1. The info should be logged in a single CSV/excel file.
2. A human should be able to read the CSV/excel file efficiently, which implies that important rows should be colored.
3. It should be fast and memory efficient
I have found many ways of satisfying requirement 1, 2 and 3. But never all at the same time.
For point 1, I can use [Append existing excel sheet with new dataframe using python pandas](https://stackoverflow.com/questions/38074678/append-existing-excel-sheet-with-new-dataframe-using-python-pandas/38075046#38075046) or [How to add pandas data to an existing csv file?](https://stackoverflow.com/questions/17530542/how-to-add-pandas-data-to-an-existing-csv-file)
For point 2, I can use Color formatting excel file row in python
If I want to accomplish point 1 and 2 at the same time, I can use a slightly modified version of [Append existing excel sheet with new dataframe using python pandas](https://stackoverflow.com/questions/38074678/append-existing-excel-sheet-with-new-dataframe-using-python-pandas/38075046#38075046) (by using` df.style.apply` right before the `to_excel()` call). But since the excel file is read at each iteration, the result is slow.
Similarly, [How to add pandas data to an existing csv file?](https://stackoverflow.com/questions/17530542/how-to-add-pandas-data-to-an-existing-csv-file) satisfies point 1 and 3, but not point 2, as it would seem like colors do not work in the same way for `to_csv()` (at least not when saving to a file). `to_excel()`, on the other hand, colors the rows correctly but doesn't take the `mode='a'` argument which gives to `to_csv()` its efficiency.
Here is a minimum reproducible example corresponding to point 1 and 2 at the same time. As you will see if you run the code, the time taken per iteration quickly increases.
```python
from os.path import isfile
import pandas as pd
from openpyxl import load_workbook
from time import time
def highlight_idx(x, idx):
if x.name == idx:
return ['background-color: red'] * len(x)
return [''] * len(x)
def append_df_to_excel(filename, df, sheet_name='Sheet1', selected_idx=None, startrow=None,
truncate_sheet=False,
**to_excel_kwargs):
# Excel file doesn't exist - saving and exiting
if not isfile(filename):
df.style.apply(lambda x: highlight_idx(x, selected_idx), axis=1).to_excel(
filename,
sheet_name=sheet_name,
startrow=startrow if startrow is not None else 0,
**to_excel_kwargs)
return
# ignore [engine] parameter if it was passed
if 'engine' in to_excel_kwargs:
to_excel_kwargs.pop('engine')
writer = pd.ExcelWriter(filename, engine='openpyxl', mode='a')
# try to open an existing workbook
writer.book = load_workbook(filename)
# get the last row in the existing Excel sheet
# if it was not specified explicitly
if startrow is None and sheet_name in writer.book.sheetnames:
startrow = writer.book[sheet_name].max_row
# truncate sheet
if truncate_sheet and sheet_name in writer.book.sheetnames:
# index of [sheet_name] sheet
idx = writer.book.sheetnames.index(sheet_name)
# remove [sheet_name]
writer.book.remove(writer.book.worksheets[idx])
# create an empty sheet [sheet_name] using old index
writer.book.create_sheet(sheet_name, idx)
# copy existing sheets
writer.sheets = {ws.title: ws for ws in writer.book.worksheets}
if startrow is None:
startrow = 0
# write out the new sheet
df.style.apply(lambda x: highlight_idx(x, selected_idx), axis=1).to_excel(writer, sheet_name,
startrow=startrow,
**to_excel_kwargs)
# save the workbook
writer.save()
for i in range(100000):
df = pd.DataFrame({'A': ['2', '3', '4'],
'B': ['5', '6', '7']})
start = time()
append_df_to_excel("out.xlsx", df, selected_idx=1)
print("time taken for iteration:", time() - start)
```
The above is just one of the many things I have attempted. But, as I said earlier, none of my attempts have managed to satisfy all 3 criteria so far. So much so that I'm starting to wonder if doing such a thing is even possible with pandas at the time?
I have tried it with the latest pandas version (1.2.4). Other than that, the relevant libraries I'm using are jinja2==2.11.3 and openpyxl=3.0.7
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https://api.github.com/repos/pandas-dev/pandas/issues/41277 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41277/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41277/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41277/events | https://github.com/pandas-dev/pandas/issues/41277 | 874,416,294 | MDU6SXNzdWU4NzQ0MTYyOTQ= | 41,277 | QST: Pandas describe() methods(with float16 and float32) are different | {
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] | open | false | null | [] | null | 7 | "2021-05-03T09:47:58Z" | "2021-08-20T02:47:33Z" | null | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
I'm trying to optimise my df dtypes to consume less memory.
I noticed differences in the describe() method output on that df.
Why those outputs are different?
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
df = pd.DataFrame({'a':[11111,22222,3333]})
df['a'].describe()
df['a'].astype('float16').describe()
```
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} | 1 | "2021-05-03T09:48:43Z" | "2021-05-03T14:40:34Z" | "2021-05-03T14:28:06Z" | MEMBER | null | cc @simonjayhawkins fixtures defined by pandas are not known when inheriting the tests as external package. There are a bunch of fixtures that external packages are expected to define in `pandas/tests/extension/conftest.py`, but I think for the string dtype here this is not worth it to expect downstream users to define it themselves (certainly given that it is still going to change) | {
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} | 1 | "2021-05-03T12:19:53Z" | "2021-05-03T13:53:58Z" | "2021-05-03T13:49:46Z" | CONTRIBUTOR | null | Turns out that the issue was the comparison of the type. Loop has `WarningMessage`, but the type of warning is in `actual_warning.category` | {
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} | 2 | "2021-05-03T12:23:55Z" | "2021-05-03T13:53:19Z" | "2021-05-03T13:50:14Z" | MEMBER | null | Some of those do have docstrings with example to be validated, and some do not have.
This only make sure that future files or changes to files in those directories will be checked.
---
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-05-03T12:41:35Z" | "2021-05-05T12:55:43Z" | "2021-05-05T12:50:49Z" | MEMBER | null | Unlike MaskedArray, this returns a numpy bool array to be consistent with the EA interface and StringArray and also due to the fact that the returned boolean array has no null values to be consistent with the latest version of pyarrow.
```
[ 0.00%] ·· Benchmarking existing-py_home_simon_miniconda3_envs_pandas-dev_bin_python
[ 4.17%] ··· algos.isin.IsIn.time_isin ok
[ 4.17%] ··· ================== ==========
dtype
------------------ ----------
int64 295±0μs
uint64 348±0μs
object 337±0μs
Int64 785±0μs
boolean 868±0μs
bool 420±0μs
datetime64[ns] 4.67±0ms
category[object] 9.46±0ms
category[int] 7.30±0ms
str 535±0μs
string 556±0μs
arrow_string 330±0μs
================== ==========
[ 8.33%] ··· algos.isin.IsIn.time_isin_categorical ok
[ 8.33%] ··· ================== ==========
dtype
------------------ ----------
int64 374±0μs
uint64 507±0μs
object 467±0μs
Int64 633±0μs
boolean 702±0μs
bool 458±0μs
datetime64[ns] 3.10±0ms
category[object] 10.2±0ms
category[int] 9.12±0ms
str 598±0μs
string 628±0μs
arrow_string 404±0μs
================== ==========
[ 12.50%] ··· algos.isin.IsIn.time_isin_empty ok
[ 12.50%] ··· ================== ==========
dtype
------------------ ----------
int64 275±0μs
uint64 285±0μs
object 390±0μs
Int64 1.06±0ms
boolean 1.14±0ms
bool 280±0μs
datetime64[ns] 295±0μs
category[object] 4.17±0ms
category[int] 3.49±0ms
str 424±0μs
string 718±0μs
arrow_string 140±0μs
================== ==========
[ 16.67%] ··· algos.isin.IsIn.time_isin_mismatched_dtype ok
[ 16.67%] ··· ================== ==========
dtype
------------------ ----------
int64 221±0μs
uint64 216±0μs
object 337±0μs
Int64 337±0μs
boolean 356±0μs
bool 323±0μs
datetime64[ns] 348±0μs
category[object] 4.86±0ms
category[int] 3.47±0ms
str 586±0μs
string 525±0μs
arrow_string 224±0μs
================== ==========
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From https://github.com/python/typeshed/issues/2148#issuecomment-520783318 it seems this can be resolved with
```
__hash__: None
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} | 17 | "2021-05-03T14:15:01Z" | "2021-06-29T12:31:30Z" | "2021-06-29T12:27:43Z" | MEMBER | null | - [ ] closes #40013
- [ ] 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] 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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@WillAyd can i get your help with _wrap_applied_output here? As commented, the added case is pretty much a kludge and I think we can do better. Am I remembering correctly that you refactored a bunch of the wrapping code ~18 months ago? | {
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} | 2 | "2021-05-03T19:08:19Z" | "2021-05-05T19:51:11Z" | "2021-05-05T12:41:14Z" | MEMBER | null | - [x] closes #40933
- [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
This is more or less a follow up of #39040, so don't think we need a whatsnew? | {
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} | 0 | "2021-05-03T19:24:11Z" | "2021-05-04T16:40:44Z" | "2021-05-04T16:27:25Z" | 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
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] | closed | false | null | [] | null | 4 | "2021-05-03T20:06:05Z" | "2021-08-20T02:50:14Z" | "2021-08-20T02:50:13Z" | NONE | null | It would be great if `pandas.Series.apply` had in-built handling of missing values. This could be achieved by simply adding a `na_action` parameter, exactly as `pandas.Series.map` to work in a similar way. In this respect NaN values could simply be ignored or an error raised if one was encountered. `pandas.Series.map` has basic missing value handling, but does NOT handle *args or **kwargs. The ideal world would be a modification to `apply` to provide both the missing value AND additional argument functionality. Alternatives could be a new function name that handles both cases, but I wouldn't recommend this due to redundancy.
I am aware of easy solutions to this practically, including NaN handling within the custom function, dropping NaN values prior to calling `apply`, but this is an ease-of-use feature that I believe can significantly improve usability.
See this use case standalone:
```python
import pandas as pd
from numpy import nan
string2 = "love"
x = pd.Series(['we','love',nan,'pandas','very','much'])
def some_string_processing_function(s, another_string):
return s[1]==another_string[1]
# pd.Series.map doesn't work here because of keyword argument
x.apply(some_string_processing_function, another_string=string2)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-116-95783a17b05f> in <module>
----> 1 x.apply(some_string_processing_function, another_string="love")
~\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds)
4136 else:
4137 values = self.astype(object)._values
-> 4138 mapped = lib.map_infer(values, f, convert=convert_dtype)
4139
4140 if len(mapped) and isinstance(mapped[0], Series):
pandas\_libs\lib.pyx in pandas._libs.lib.map_infer()
~\lib\site-packages\pandas\core\series.py in f(x)
4121
4122 def f(x):
-> 4123 return func(x, *args, **kwds)
4124
4125 else:
<ipython-input-115-4f94a2ec1809> in some_string_processing_function(s, another_string)
1 def some_string_processing_function(s, another_string):
----> 2 return s[1]==another_string[1]
TypeError: 'float' object is not subscriptable
```
Thank you for your time. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41291 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41291/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41291/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41291/events | https://github.com/pandas-dev/pandas/issues/41291 | 874,888,119 | MDU6SXNzdWU4NzQ4ODgxMTk= | 41,291 | API: SeriesGroupBy.product with numeric_only and empty non-numeric data | {
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} | 2 | "2021-05-03T20:33:56Z" | "2021-06-02T15:14:09Z" | "2021-06-02T15:14:09Z" | MEMBER | null | By default numeric_only=True. But when there are no numeric columns and we raise DataError, the fallback ignores numeric_only. So we end up getting wonky results.
Below I think that gb1 raising is correct, gb3 should raise, gb2 and gb4 should return empty DataFrames. cc @jreback @jorisvandenbossche @TomAugspurger @WillAyd
```
dti = pd.date_range("2016-01-01", periods=3)
ser = pd.Series(dti)
df = ser.to_frame()
gb1 = ser.groupby([0, 0, 0])
gb2 = df.groupby(gb1.grouper)
gb3 = ser[:0].groupby([])
gb4 = df[:0].groupby([])
>>> gb1.prod()
TypeError: 'DatetimeArray' does not implement reduction 'prod'
>>> gb2.prod() # <-float64
0
0 NaN
>>> gb3.prod()
Series([], dtype: datetime64[ns])
>>> gb4.prod() # <- dt64
Empty DataFrame
Columns: [0]
Index: []
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41292 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41292/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41292/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41292/events | https://github.com/pandas-dev/pandas/pull/41292 | 874,926,460 | MDExOlB1bGxSZXF1ZXN0NjI5Mzc5NzUx | 41,292 | BUG: fix suppressed TypeErrors in Groupby.mean, median, var | {
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} | 3 | "2021-05-03T21:31:20Z" | "2021-05-04T16:26:07Z" | "2021-05-04T16:23:01Z" | MEMBER | null | These are raising TypeError because we aren't passing enough args, but then we're catching those TypeErrors and falling back. So this doesn't change behavior, just avoids an unnecessary fallback in some cases.
No tests. Surfacing the exceptions would require ugly/invasive changes. | {
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} | 0 | "2021-05-03T21:40:25Z" | "2021-05-04T18:10:17Z" | "2021-05-04T16:26:35Z" | MEMBER | null | pre-cursor cleanup to avoid duplicating validation | {
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} | 4 | "2021-05-03T23:02:03Z" | "2021-05-04T16:25:45Z" | "2021-05-04T16:22:29Z" | 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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} | 13 | "2021-05-04T00:10:24Z" | "2021-06-19T22:37:10Z" | "2021-06-12T10:21:23Z" | CONTRIBUTOR | null | - [ ] closes #40560 (Pending feedback on decorator-generated docstrings)
- [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 try at any open source stuff! This one looked pretty doable, hopefully it's there's nothing wrong with it/let me know!
I'm not sure whether *I* should be checking these boxes, or a reviewer should. I *believe* this fixes the issue since it adds the validator, though it's not a particularly clever validator.
It technically is an added test, and it technically works - I re-added one of the entries and got a successful (correct) result for its failure... (amongst the infinite other validation failures 😢 )
I assume linting will run after the PR is submitted, but I'll look into running it locally, too.
```
...
None:None:ES01:pandas.IndexSlice:No extended summary foundNone:None:EX03:pandas.IndexSlice:flake8 error: E231 missing whitespace after ',' (4 times)
.../pandas/pandas/core/indexes/multi.py:434:ES01:pandas.MultiIndex.from_arrays:No extended summary found
.../pandas/pandas/core/indexes/multi.py:434:GL05:pandas.MultiIndex.from_arrays:Use 'array-like' rather than 'array_like' in docstrings. <<<--- Message for added issue.
.../pandas/pandas/core/indexes/multi.py:500:ES01:pandas.MultiIndex.from_tuples:No extended summary found
.../pandas/pandas/core/indexes/multi.py:567:ES01:pandas.MultiIndex.from_product:No extended summary found
None:None:ES01:pandas.MultiIndex.names:No extended summary found
...
```
More on the validator:
* I chose `GL` as the code after looking through [the numpy docstring validation guide](https://numpydoc.readthedocs.io/en/latest/validation.html) and the [pandas docstring validation guide](https://pandas.pydata.org/docs/dev/development/contributing_docstring.html) - I think it stands for "General" and that seems like the best categorization for this message.
* I used `raw_doc` for this but there was another property like `clean_doc` as well. I could just iterate through each section, too. 🤷
* I didn't add any extra detail/context to the error message. The line and file are already given, so I think there's nothing extra that's needed.
Using the validator to pick up any missed docstring changes:
```
$ time ./validate_docstrings.py 2>&1 | tee validate_docstrings.log ### (It takes ~5m to run)
$ grep GL05 validate_docstrings.log
.../pandas/pandas/core/frame.py:10220:GL05:pandas.DataFrame.resample:Use 'array-like' rather than 'array_like' in docstrings.
.../pandas/pandas/core/series.py:5171:GL05:pandas.Series.resample:Use 'array-like' rather than 'array_like' in docstrings.
```
-----
Actually, looking into those two remaining results - those are docstrings generated by lines like:
```10220 @doc(NDFrame.resample, **_shared_doc_kwargs)```
Looking in more detail, I'm not exactly sure how this happens, but it seems like this decorator must be generating text with "array_like" in it. I kind of want to declare it out of scope for this PR, but I can dig into it more if needed.
I suspect maybe the `Substitution` decorator tool can help, possibly. It seems kind of cheesy to add something that will substitute array_like for array-like in a docstring - eg, it might be the result of a function whose param is actually called `array_like`, which, I'm pretty sure they *do* exist. I'm open to feedback on that.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-05-04T02:47:57Z" | "2021-05-06T23:22:47Z" | "2021-05-06T23:22:43Z" | MEMBER | null | Not sure if this is the desired way to address this.
```
________________________ TestDataFrameRound.test_round _________________________
self = <pandas.tests.frame.methods.test_round.TestDataFrameRound object at 0x7fcdd089eb90>
def test_round(self):
# GH#2665
# Test that rounding an empty DataFrame does nothing
df = DataFrame()
tm.assert_frame_equal(df, df.round())
# Here's the test frame we'll be working with
df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]})
# Default round to integer (i.e. decimals=0)
expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]})
tm.assert_frame_equal(df.round(), expected_rounded)
# Round with an integer
decimals = 2
expected_rounded = DataFrame(
{"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]}
)
tm.assert_frame_equal(df.round(decimals), expected_rounded)
# This should also work with np.round (since np.round dispatches to
# df.round)
tm.assert_frame_equal(np.round(df, decimals), expected_rounded)
# Round with a list
round_list = [1, 2]
msg = "decimals must be an integer, a dict-like or a Series"
with pytest.raises(TypeError, match=msg):
df.round(round_list)
# Round with a dictionary
expected_rounded = DataFrame(
{"col1": [1.1, 2.1, 3.1], "col2": [1.23, 2.23, 3.23]}
)
round_dict = {"col1": 1, "col2": 2}
tm.assert_frame_equal(df.round(round_dict), expected_rounded)
# Incomplete dict
expected_partially_rounded = DataFrame(
{"col1": [1.123, 2.123, 3.123], "col2": [1.2, 2.2, 3.2]}
)
partial_round_dict = {"col2": 1}
tm.assert_frame_equal(df.round(partial_round_dict), expected_partially_rounded)
# Dict with unknown elements
wrong_round_dict = {"col3": 2, "col2": 1}
tm.assert_frame_equal(df.round(wrong_round_dict), expected_partially_rounded)
# float input to `decimals`
non_int_round_dict = {"col1": 1, "col2": 0.5}
msg = "integer argument expected, got float"
with pytest.raises(TypeError, match=msg):
> df.round(non_int_round_dict)
pandas/tests/frame/methods/test_round.py:67:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = col1 col2
0 1.123 1.234
1 2.123 2.234
2 3.123 3.234
decimals = {'col1': 1, 'col2': 0.5}, args = (), kwargs = {}
concat = <function concat at 0x7fce2c668e50>
_dict_round = <function DataFrame.round.<locals>._dict_round at 0x7fcdd13728c0>
def round(
self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs
) -> DataFrame:
"""
Round a DataFrame to a variable number of decimal places.
Parameters
----------
decimals : int, dict, Series
Number of decimal places to round each column to. If an int is
given, round each column to the same number of places.
Otherwise dict and Series round to variable numbers of places.
Column names should be in the keys if `decimals` is a
dict-like, or in the index if `decimals` is a Series. Any
columns not included in `decimals` will be left as is. Elements
of `decimals` which are not columns of the input will be
ignored.
*args
Additional keywords have no effect but might be accepted for
compatibility with numpy.
**kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
A DataFrame with the affected columns rounded to the specified
number of decimal places.
See Also
--------
numpy.around : Round a numpy array to the given number of decimals.
Series.round : Round a Series to the given number of decimals.
Examples
--------
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
... columns=['dogs', 'cats'])
>>> df
dogs cats
0 0.21 0.32
1 0.01 0.67
2 0.66 0.03
3 0.21 0.18
By providing an integer each column is rounded to the same number
of decimal places
>>> df.round(1)
dogs cats
0 0.2 0.3
1 0.0 0.7
2 0.7 0.0
3 0.2 0.2
With a dict, the number of places for specific columns can be
specified with the column names as key and the number of decimal
places as value
>>> df.round({'dogs': 1, 'cats': 0})
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
Using a Series, the number of places for specific columns can be
specified with the column names as index and the number of
decimal places as value
>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
"""
from pandas.core.reshape.concat import concat
def _dict_round(df, decimals):
for col, vals in df.items():
try:
yield _series_round(vals, decimals[col])
except KeyError:
yield vals
def _series_round(s, decimals):
if is_integer_dtype(s) or is_float_dtype(s):
return s.round(decimals)
return s
nv.validate_round(args, kwargs)
if isinstance(decimals, (dict, Series)):
if isinstance(decimals, Series):
if not decimals.index.is_unique:
raise ValueError("Index of decimals must be unique")
> new_cols = list(_dict_round(self, decimals))
pandas/core/frame.py:9205:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
df = col1 col2
0 1.123 1.234
1 2.123 2.234
2 3.123 3.234
decimals = {'col1': 1, 'col2': 0.5}
def _dict_round(df, decimals):
for col, vals in df.items():
try:
> yield _series_round(vals, decimals[col])
pandas/core/frame.py:9190:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
s = 0 1.234
1 2.234
2 3.234
Name: col2, dtype: float64, decimals = 0.5
def _series_round(s, decimals):
if is_integer_dtype(s) or is_float_dtype(s):
> return s.round(decimals)
pandas/core/frame.py:9196:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = 0 1.234
1 2.234
2 3.234
Name: col2, dtype: float64
decimals = 0.5, args = (), kwargs = {}
def round(self, decimals=0, *args, **kwargs) -> Series:
"""
Round each value in a Series to the given number of decimals.
Parameters
----------
decimals : int, default 0
Number of decimal places to round to. If decimals is negative,
it specifies the number of positions to the left of the decimal point.
*args, **kwargs
Additional arguments and keywords have no effect but might be
accepted for compatibility with NumPy.
Returns
-------
Series
Rounded values of the Series.
See Also
--------
numpy.around : Round values of an np.array.
DataFrame.round : Round values of a DataFrame.
Examples
--------
>>> s = pd.Series([0.1, 1.3, 2.7])
>>> s.round()
0 0.0
1 1.0
2 3.0
dtype: float64
"""
nv.validate_round(args, kwargs)
> result = self._values.round(decimals)
E TypeError: 'float' object cannot be interpreted as an integer
pandas/core/series.py:2361: TypeError
During handling of the above exception, another exception occurred:
self = <pandas.tests.frame.methods.test_round.TestDataFrameRound object at 0x7fcdd089eb90>
> ???
E AssertionError: Regex pattern 'integer argument expected, got float' does not match "'float' object cannot be interpreted as an integer".
pandas/tests/frame/methods/test_round.py:-1: AssertionError
```
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} | 1 | "2021-05-04T10:43:44Z" | "2021-05-06T07:35:51Z" | "2021-05-04T12:44:09Z" | CONTRIBUTOR | null | The main time in rendering Styler is consumed by:
- formatting values for display
- adding cell styles via apply / applymap
- adding cell styles via set_td_classes
We have benchmarks for the latter 2 but not the first. Any future changes to the formatting function might be worth having benchmarks (I would use them :) )
Especially if we try to merge Styler formatting with the floatformatter for example.
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} | 3 | "2021-05-04T10:49:51Z" | "2021-05-04T11:52:47Z" | "2021-05-04T11:52:32Z" | 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
df = pd.DataFrame({"dates": ["2020-07-07", np.datetime64("Nat")]})
df.mask(df.isna(), None)
```
#### Problem description
`df.mask` does not replace `NaT` by `None`.

#### Expected Output
`df.mask` should properly mask `NaT`.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.214-160.339.amzn2.x86_64
Version : #1 SMP Sun Jan 10 05:53:05 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : 0.17.0
xlrd : 1.2.0
xlwt : None
numba : 0.52.0
```
</details>
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} | 14 | "2021-05-04T10:56:22Z" | "2021-05-14T16:13:58Z" | "2021-05-14T16:13:53Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
import pandas as pd
from io import StringIO
data = """
"date_time";"T";"Po"
"04.05.2021 09:00";"9.8";"759.0";
"""
df = pd.read_csv(StringIO(data), sep=';',index_col=0)
df['T']
df.head(1)
```
#### Problem description
When there is no separator at the end of the header line, pandas seems like skip first column (T) and shows second column instead.
```python
df['T']
df.loc[:,'T']
```
**Output:**
```
04.05.2021 09:00 759.0
Name: T, dtype: float64
```
**Expected:**
(as df.iloc[0,0] give 9.8):
```
Местное время в Омске / им. Д. М. Карбышева (аэропорт)
04.05.2021 09:00 9.8
Name: T, dtype: float64
```
After I add ";" separator after "sss" bug disappeared, df.iloc[0,0] output is same in both case.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.21354
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.5
numpy : 1.17.4
pytz : 2018.9
dateutil : 2.8.1
pip : 19.0.3
setuptools : 42.0.1
Cython : 0.29.21
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.3.1
fsspec : 0.6.1
fastparquet : None
gcsfs : 0.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.18
tables : None
tabulate : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41305 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41305/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41305/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41305/events | https://github.com/pandas-dev/pandas/issues/41305 | 875,371,042 | MDU6SXNzdWU4NzUzNzEwNDI= | 41,305 | BUG: shared doc string decorator doesn't handle args defined with sets | {
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] | open | false | null | [] | null | 0 | "2021-05-04T11:33:01Z" | "2021-08-20T02:51:16Z" | null | CONTRIBUTOR | null | I was exploring using a shared docstring decorator today and came up with the issue that the pandas notation for parameter sets is the same as needed for string replacement. So for example:
```
@doc(param1="some_value")
def test_doc_replacement(self, set_ars):
"""
Description of {param1}
Parameters
----------
set_args : {'str1', 'str2_or', 'str3'}
string values from a set.
Returns
-------
A KeyError!!
"""
pass
```
Yielded a KeyError, since its tries to find a key `"'str1', 'str2_or', 'str3'", in my docstring parameters.
The way I'm attempting to get around it is:
```
@doc(param1="some_value", set_args="{'str1', 'str2_or', 'str3'}")
def test_doc_replacement(self, set_ars):
"""
Description of {param1}
Parameters
----------
set_args : {set_args}
string values from a set.
Returns
-------
Hopefully the right display.
"""
pass
```
Incidentally anywhere in the docs that a `{}` appears seems to be a problem. So in my examples section I also had to try changing:
`>>> df = DataFrame({'A': [1,2], 'B': [3,4]})`
to
`>>> df = DataFrame([[1,3],[2,4]], columns=["A", "B"])` | {
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} | 0 | "2021-05-04T12:27:42Z" | "2021-05-04T17:58:37Z" | "2021-05-04T16:37:46Z" | MEMBER | null | seperate commits for cleanups
remove hasattr for str methods available in pyarrow 1.0.0
add comments to indicate pyarrow version a method is addded.
de-duplicate fixture
de-duplicate warning from str.contains with pattern with capture groups
remove special casing StringDtype | {
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} | 1 | "2021-05-04T14:33:56Z" | "2021-05-04T19:40:41Z" | "2021-05-04T19:40:36Z" | CONTRIBUTOR | null | - [x] xref #37643
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | "2021-05-04T14:55:12Z" | "2021-05-04T21:55:23Z" | "2021-05-04T21:36:53Z" | 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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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
res = pd.DataFrame({"Method": ["Alpha1", "Beta1", "Beta2", "Alpha2", "Alpha3"], "Value": [5, 6, 7, 8, 10]})
df = res[res["Value"] <= 8]
df.loc[df["Method"].str.contains("Alpha"), "Method"] = "Gamma"
display(res)
display(df)
```
#### Problem description
In the code above, we are:
1. Creating a new dataframe `res`
2. Getting a copy of the dataframe where values are less than 8 and assigning that to `df`
3. Setting values in `df` using the `.loc` indexing
Step 3 raises the `SettingWithCopyWarning`. I don't believe we are setting the value of a copy there as we are using the `.loc` indexing method, which should set values in `df`. The values are being set, but the warning is being raised. If I'm understand the warning correctly, it should not be raised.
#### Expected Output
No warning should be raised
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-50-generic
Version : #56~20.04.1-Ubuntu SMP Mon Apr 12 21:46:35 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.51.1
</details>
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} | 10 | "2021-05-04T16:04:48Z" | "2021-08-26T13:44:50Z" | "2021-06-12T09:43:18Z" | MEMBER | null | - [x] closes #41101
- [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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} | 3 | "2021-05-04T16:17:56Z" | "2021-05-04T16:43:40Z" | "2021-05-04T16:33:41Z" | NONE | null | #### Is your feature request related to a problem?
I expect when loading and saving CSV data that whatever defaults used for loading and saving can be used mindlessly if my data is simple enough. For example, in the below example, I would expect that ``new_df`` is equal to ``df``:
```
df = pd.DataFrame({'time (ms)': [1, 2, 3, 4]})
df.to_csv('test.csv')
new_df = pd.read_csv('test.csv')
```
However, this is not so. The index is saved as the first column by default, but is not *read* as the first column by default when loading data with ``read_csv()``.
#### Describe the solution you'd like
I should not have to pass in additional arguments to get the simplest use case to work for loading and saving data. I suggest either not saving the index by default when converting to CSV (this seems less desirable), or setting the ``index_col=0`` by default when loading a CSV file.
#### API breaking implications
``index_col`` would be set by default
#### Describe alternatives you've considered
Manually avoiding saving the index, and manually adding the ``index_col=0`` keyword argument when loading CSV data. Both have caused me endless frustration.
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https://api.github.com/repos/pandas-dev/pandas/issues/41312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41312/events | https://github.com/pandas-dev/pandas/pull/41312 | 875,636,830 | MDExOlB1bGxSZXF1ZXN0NjI5OTM5OTk0 | 41,312 | [ArrowStringDtype] Make it already a StringDtype subclass | {
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} | 7 | "2021-05-04T16:36:23Z" | "2021-05-05T06:55:53Z" | "2021-05-05T06:55:49Z" | MEMBER | null | @simonjayhawkins this will be redundant after https://github.com/pandas-dev/pandas/pull/39908, but in the meantime, ArrowStringDtype being a subclass would help for writing robust code related to `StringDtype` in downstream packages (for those testing against pandas master) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41313/events | https://github.com/pandas-dev/pandas/issues/41313 | 875,638,833 | MDU6SXNzdWU4NzU2Mzg4MzM= | 41,313 | CI: numpy doesn't build on python 3.10beta | {
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} | 6 | "2021-05-04T16:38:54Z" | "2021-05-05T12:58:42Z" | "2021-05-05T12:58:42Z" | MEMBER | null | The CI is failing is only failing for the "Install dependencies" phase for ```python 3.10```
Saw in:
- https://github.com/pandas-dev/pandas/pull/41308/checks?check_run_id=2501536827
- https://github.com/pandas-dev/pandas/pull/41307/checks?check_run_id=2501358707
- https://github.com/pandas-dev/pandas/pull/41269/checks?check_run_id=2498810271 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41314/events | https://github.com/pandas-dev/pandas/issues/41314 | 875,651,539 | MDU6SXNzdWU4NzU2NTE1Mzk= | 41,314 | ENH: Incorporate inplace and subset parameters into pandas.DataFrame.apply | {
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My lame examples of how the function would look if implemented.
```
import numpy as np
import pandas as pd
# Set the seed for a reproducible sample
np.random.seed(0)
df = pd.DataFrame(np.random.randn(10, 5), columns=list('ABCDE'))
df.apply(lambda x: x + 2*x, inplace=True) # would apply x + 2*x to each column
df.apply(lambda x: x + 2*x, inplace=True, subset=['A','B']) # would apply x + 2*x to each column, 'A' and 'B'
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41315/events | https://github.com/pandas-dev/pandas/pull/41315 | 875,663,895 | MDExOlB1bGxSZXF1ZXN0NjI5OTYwODg4 | 41,315 | CI: pin py310-dev version to alpha 7 | {
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} | 1 | "2021-05-04T17:10:11Z" | "2021-05-04T19:27:49Z" | "2021-05-04T19:27:24Z" | MEMBER | null | xref #41313
Not sure if the syntax is correct. Let's see. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41316 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41316/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41316/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41316/events | https://github.com/pandas-dev/pandas/issues/41316 | 875,741,061 | MDU6SXNzdWU4NzU3NDEwNjE= | 41,316 | BUG: Using df.groupby().rolling() when grouping by a MultiIndex level causes the MultiIndex to lose levels | {
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] | closed | false | null | [] | null | 1 | "2021-05-04T18:53:42Z" | "2021-05-04T19:37:19Z" | "2021-05-04T19:37:19Z" | 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.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
When grouping a DataFrame which has a MultiIndex using the `level` parameter for `groupby`, performing any `rolling` operation will cause all MultiIndex levels except the one it was grouped by to be lost. This bug has been present since at least 1.1.5.
```python
import pandas as pd
print(pd.__version__)
df = pd.DataFrame({"idx1": [1] * 5 + [2] * 5, "idx2": range(10), "value": range(10)})
df = df.set_index(["idx1", "idx2"])
test = df.groupby(level=0).rolling(2).mean()
print(test.index)
```
On version 1.2.4:
```python
>>>1.2.4
>>>MultiIndex([(1,),
(1,),
(1,),
(1,),
(1,),
(2,),
(2,),
(2,),
(2,),
(2,)],
names=['idx1'])
```
On version 1.0.5:
```python
>>>1.0.5
>>>MultiIndex([(1, 1, 0),
(1, 1, 1),
(1, 1, 2),
(1, 1, 3),
(1, 1, 4),
(2, 2, 5),
(2, 2, 6),
(2, 2, 7),
(2, 2, 8),
(2, 2, 9)],
names=['idx1', 'idx1', 'idx2'])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
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 : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
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 : 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 : 1.4.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-05-04T19:10:25Z" | "2021-05-05T14:50:22Z" | "2021-05-05T12:44:33Z" | MEMBER | null | In _python_agg_general:
```
if self.grouper._filter_empty_groups:
mask = counts.ravel() > 0
```
It isn't entirely trivial, but we can show that we will always have `mask.all()`, as a result of which the remainder of this code chunk of code is a no-op. This PR removes it.
As a consequence, we can remove `_filter_empty_groups` and `compressed`
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] | open | false | null | [] | null | 4 | "2021-05-04T19:35:22Z" | "2021-08-20T02:51:46Z" | 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.
---
**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
def do_nothing(value):
return value
df = pd.DataFrame(
{
"col1": [2, 51, 17],
"col2": [5.5, 3.3, 4.4],
"col3": ["a", "b", "c"],
},
)
print(df)
df1 = df.sort_values(by=["col1"], ascending=None, key=do_nothing)
print(df1)
```
#### Problem description
Output is
```
col1 col2 col3
0 2 5.5 a
1 51 3.3 b
2 17 4.4 c
Traceback (most recent call last):
File "sort_values_test9.py", line 14, in <module>
df1 = df.sort_values(by=["col1"], ascending=None, key=do_nothing)
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/frame.py", line 5464, in sort_values
indexer = nargsort(
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/sorting.py", line 354, in nargsort
return nargsort(
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/sorting.py", line 368, in nargsort
return items.argsort(ascending=ascending, kind=kind, na_position=na_position)
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/core/arrays/base.py", line 583, in argsort
ascending = nv.validate_argsort_with_ascending(ascending, args, kwargs)
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/compat/numpy/function.py", line 150, in validate_argsort_with_ascending
validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/compat/numpy/function.py", line 67, in __call__
validate_args_and_kwargs(
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/util/_validators.py", line 205, in validate_args_and_kwargs
validate_kwargs(fname, kwargs, compat_args)
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/util/_validators.py", line 149, in validate_kwargs
_check_for_default_values(fname, kwds, compat_args)
File "/home/gregory/work/miniconda3/envs/modin/lib/python3.8/site-packages/pandas/util/_validators.py", line 65, in _check_for_default_values
raise ValueError(
ValueError: the 'axis' parameter is not supported in the pandas implementation of argsort()
```
Function `validate_argsort_with_ascending` inserts a value for `ascending` argument in case it is `None`, but later this value is considered as a value of `axis` parameter for some reason. This leads to an exception instead of a normal operation.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-72-generic
Version : #80-Ubuntu SMP Mon Apr 12 17:35:00 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.7
pandas_gbq : 0.15.0
pyarrow : 1.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.8
tables : 3.6.1
tabulate : None
xarray : 0.17.0
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41319/events | https://github.com/pandas-dev/pandas/issues/41319 | 875,817,625 | MDU6SXNzdWU4NzU4MTc2MjU= | 41,319 | BUG: pandas utctimetuple() fails on python3.9 (but not 37) with timezone aware timestamps | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```
Python 3.9.2 (default, Feb 24 2021, 13:26:09)
[Clang 12.0.0 (clang-1200.0.32.29)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> ts = pd.Timestamp("2020-11-27 16:00:00-0500", tz="US/Eastern")
>>> ts.utctimetuple()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/timestamps.pyx", line 1095, in pandas._libs.tslibs.timestamps.Timestamp.__new__
TypeError: an integer is required
>>> ts = pd.Timestamp("2020-11-27 16:00:00", tz="US/Eastern")
>>> ts.utctimetuple()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/_libs/tslibs/timestamps.pyx", line 1095, in pandas._libs.tslibs.timestamps.Timestamp.__new__
TypeError: an integer is required
>>> ts = pd.Timestamp("2020-11-27 16:00:00")
>>> ts.utctimetuple()
time.struct_time(tm_year=2020, tm_mon=11, tm_mday=27, tm_hour=16, tm_min=0, tm_sec=0, tm_wday=4, tm_yday=332, tm_isdst=0)
>>>
```
#### Problem description
This does not happen in python3.7 with whatever the latest pandas is for 37; it happens on 1.2.4 in python3.9.
Pandas timestamps that are timezone aware fail `.utctimetuple()` however the python JSON encoder calls this function; for example I originally thought this was a flask issue: https://github.com/pallets/flask/issues/3993:
```
return jsonify(aod)
File "/Users/tommycarpenter/Development/python-indexapi/.tox/py39/lib/python3.9/site-packages/flask/json/__init__.py", line 370, in jsonify
dumps(data, indent=indent, separators=separators) + "\n",
File "/Users/tommycarpenter/Development/python-indexapi/.tox/py39/lib/python3.9/site-packages/flask/json/__init__.py", line 211, in dumps
rv = _json.dumps(obj, **kwargs)
File "/usr/local/Cellar/[email protected]/3.9.2_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/__init__.py", line 234, in dumps
return cls(
File "/usr/local/Cellar/[email protected]/3.9.2_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/local/Cellar/[email protected]/3.9.2_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/Users/tommycarpenter/Development/python-indexapi/.tox/py39/lib/python3.9/site-packages/flask/json/__init__.py", line 91, in default
return http_date(o.utctimetuple())
File "pandas/_libs/tslibs/timestamps.pyx", line 1095, in pandas._libs.tslibs.timestamps.Timestamp.__new__
TypeError: an integer is required
```
#### Expected Output
All three variants above work, and timezoning does not affect jsonifying pandas timestamps
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 53.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
>>>
</details>
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