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} | 3 | 2021-01-26T21:01:52Z | 2021-01-26T22:50:22Z | 2021-01-26T22:47:01Z | MEMBER | null | - [x] whatsnew entry
Forgot this in #39407 | {
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} | 3 | 2021-01-26T21:01:58Z | 2021-01-31T06:26:55Z | 2021-01-30T20:46:15Z | CONTRIBUTOR | null | - [V] closes #39189
- [V] tests added / passed
- [V] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [V] whatsnew entry
This bug happens when filtering on categorical string columns and choose a value which doesn't exist in the column.
Instead of returning an empty dataframe, we get some records.
It happens because of the usage in numpy `searchsorted(v, side="left")` that find indices where elements should be inserted to maintain order (and not 0 in case that the value doesn't exist), like was assumed in the code.
I changed it to first the for the value, and use `searchsorted` only if value exists, I also added a test for this specific use case.
I think in the long run, maybe we should refactor this area in the code since one function covers multiple use cases which makes it more complex to test.
In addition, I moved the logic to a new method to keep the single-responsibility principle and to make it easier to test. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39421 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39421/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39421/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39421/events | https://github.com/pandas-dev/pandas/pull/39421 | 794,559,400 | MDExOlB1bGxSZXF1ZXN0NTYyMDU1MjY2 | 39,421 | BUG: Subset slicer on Styler failed on MultiIndex with slice(None) | {
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} | 5 | 2021-01-26T21:03:49Z | 2021-02-04T06:42:32Z | 2021-02-04T00:51:34Z | CONTRIBUTOR | null | - [x] closes #33562
- [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
The problem with above issue was with the function `non_reducing_slice`. It didn't correctly re-format slicing tuples on multiindices when rows or cols also contained a `slice(None)`. An additional check is now done. This method is only used for `Styler` currently.
Tests Added / reformmatted. | {
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} | 2 | 2021-01-26T21:18:25Z | 2021-01-30T08:37:13Z | 2021-01-30T08:37:12Z | 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
import numpy as np
a = np.array(
[
["2015-01-01", "2015-01-02", "2015-01-03"],
["2017-01-01", "2017-01-02", "2017-02-03"],
],
dtype="datetime64[ms]",
)
print(pd.DataFrame(a))
0 1 2
0 2015-01-01 2015-01-03 2017-01-02
1 2015-01-02 2017-01-01 2017-02-03
b = np.array(
[
["2015-01-01", "2015-01-02", "2015-01-03"],
["2017-01-01", "2017-01-02", "2017-02-03"],
],
dtype="datetime64[M]",
)
print(pd.DataFrame(b))
0 1 2
0 2015-01-01 2015-01-03 2017-01-02
1 2015-01-02 2017-01-01 2017-02-03
c = np.array(
[
["2015-01-01", "2015-01-02", "2015-01-03"],
["2017-01-01", "2017-01-02", "2017-02-03"],
],
dtype="datetime64[ns]",
)
print(pd.DataFrame(c))
0 1 2
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03
```
#### Problem description
The exists an inconsistency how pd.DataFrame() is created from 2D numpy.ndarray for not pandas native type. The dataframe should be transposed for datetime64[M], datetime64[ms] etc.
#### Expected Output
```python
print(pd.DataFrame(a))
0 1 2
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03
print(pd.DataFrame(b))
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03
```
#### Possible Hint
```python
d = np.array(
[
["2015-01-01", "2015-01-02", "2015-01-03"],
["2017-01-01", "2017-01-02", "2017-02-03"],
],
dtype="datetime64[ms]",
order="f",
)
print(pd.DataFrame(d))
0 2015-01-01 2015-01-02 2015-01-03
1 2017-01-01 2017-01-02 2017-02-03
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0
setuptools : 49.6.0.post20210108
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 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 6 | 2021-01-26T21:23:03Z | 2021-01-28T08:48:38Z | 2021-01-28T02:34:13Z | MEMBER | null | - [x] closes #39410
- [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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] | open | false | null | [] | null | 24 | 2021-01-26T21:33:54Z | 2021-11-15T19:02:25Z | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({("z","a"): [1, 2, 3], ("z","b"):[4, 5, 6]})
df.loc[pd.IndexSlice[:, pd.IndexSlice["z", ["a", "b", "c"]]]] # works: DataFrame with columns 'a' and 'b'.
df = pd.DataFrame({("a"): [1, 2, 3], ("b"):[4, 5, 6]})
df.loc[pd.IndexSlice[:, pd.IndexSlice[["a", "b", "c"]]]] # KeyError: missing label 'c'
```
#### Problem description
Should this behaviour be consistent?
A recent decision to move to KeyError: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike
#### Expected Output
Both return KeyError?
This can be linked to #32125 also. | {
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} | 7 | 2021-01-26T22:05:21Z | 2021-02-04T00:50:51Z | 2021-02-04T00:50:51Z | 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
# Your code here
import pandas as pd
import numpy as np
RAND_LOW = 0
RAND_HIGH = 100
NCOLS = 2 ** 5
NROWS = 2 ** 2
random_state = np.random.RandomState(seed=42)
data = {
"col{}".format(int((i - NCOLS / 2) % NCOLS + 1)): random_state.randint(
RAND_LOW, RAND_HIGH, size=(NROWS)
)
for i in range(NCOLS)
}
df1 = pd.DataFrame(data)
print(df1)
df2 = df1.sort_values(df1.index[0], axis=1, ignore_index=True)
print(df2)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
Printing object returned by `sort_values` produces an exception because some internal metadata is wrong:
```
Traceback (most recent call last):
File "sort_values_test6.py", line 19, in <module>
print(df2)
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 803, in __repr__
self.to_string(
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/core/frame.py", line 939, in to_string
return fmt.DataFrameRenderer(formatter).to_string(
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/io/formats/format.py", line 1031, in to_string
string = string_formatter.to_string()
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/io/formats/string.py", line 23, in to_string
text = self._get_string_representation()
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/io/formats/string.py", line 47, in _get_string_representation
return self._fit_strcols_to_terminal_width(strcols)
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/io/formats/string.py", line 179, in _fit_strcols_to_terminal_width
self.fmt.truncate()
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/io/formats/format.py", line 700, in truncate
self._truncate_horizontally()
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/io/formats/format.py", line 718, in _truncate_horizontally
self.tr_frame = concat((left, right), axis=1)
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 298, in concat
return op.get_result()
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/core/reshape/concat.py", line 520, in get_result
new_data = concatenate_block_managers(
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/core/internals/concat.py", line 89, in concatenate_block_managers
return BlockManager(blocks, axes)
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 143, in __init__
self._verify_integrity()
File "/localdisk/gashiman/miniconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py", line 323, in _verify_integrity
raise construction_error(tot_items, block.shape[1:], self.axes)
ValueError: Shape of passed values is (4, 16), indices imply (32, 16)
```
#### 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 : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-56-generic
Version : #62-Ubuntu SMP Mon Nov 23 19:20:19 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : 0.14.1
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.5.4
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : None
xarray : 0.16.2
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 0 | 2021-01-26T23:05:12Z | 2021-01-28T02:35:29Z | 2021-01-28T02:06:42Z | 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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} | 0 | 2021-01-26T23:33:09Z | 2021-01-27T08:39:00Z | 2021-01-27T08:39:00Z | NONE | null | Backport PR #39406: DOC: link to correct PR | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39430 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39430/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39430/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39430/events | https://github.com/pandas-dev/pandas/issues/39430 | 794,799,103 | MDU6SXNzdWU3OTQ3OTkxMDM= | 39,430 | BUG: read_json(orient='table') converts 'float64' to 'datetime64' datatype if the column name ends with '_time' | {
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] | closed | false | null | [] | null | 2 | 2021-01-27T06:00:39Z | 2021-08-15T04:07:00Z | 2021-08-15T04:07:00Z | CONTRIBUTOR | null | - [ X] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
df1=pd.DataFrame({'test_time':[np.nan,np.nan]})
df1.info()
```
This returns the data type as `float64`.
```python
RangeIndex: 2 entries, 0 to 1
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 test_time 0 non-null float64
dtypes: float64(1)
memory usage: 144.0 bytes
```
Now convert `df1` to `json` and convert it back to `DataFrame`.
```python
df2=pd.read_json(df1.to_json(orient='table'),orient='table')
df2.info()
```
This convert the data type from `float64` to `datetime64` as shown below.
```python
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 test_time 0 non-null datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 32.0 bytes
```
The following works as expected if the column named is changed to avoid to end with `_time` .
```python
DF1=pd.DataFrame({'test_t':[np.nan,np.nan]})
DF1.info()
DF2=pd.read_json(DF1.to_json(orient='table'),orient='table')
DF2.info()
```
#### Problem description
When the data is read from `json` , the column names ending with `_time` change the data type from `float64` to `datetime64`.
`DataFrame => to_json (orient='table') => read_json(orient='table') => DataFrame`
should return the same data type since `orient='table' ` encodes all the column data type in the `json` string.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.1
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.50.1
</details>
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] | closed | false | null | [] | null | 5 | 2021-01-27T07:11:56Z | 2021-02-07T15:15:44Z | 2021-02-07T15:15:44Z | NONE | null | I am working on network traffic classification using tf.keras. i want to drop a column name Label and set Label as Y and all other columns in X. I am using pandas. here is the code line:
**df =df.drop(['Label'], axis=1, inplace=True)**
I am getting the following error:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-41-6e2d6c1eb80d> in <module>()
----> 1 df =df.drop(['Label'], axis=1, inplace=True)
3 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in drop(self, labels, errors)
5285 if mask.any():
5286 if errors != "ignore":
-> 5287 raise KeyError(f"{labels[mask]} not found in axis")
5288 indexer = indexer[~mask]
5289 return self.delete(indexer)
**KeyError: "['Label'] not found in axis**"
I am trying different methods but failed. anyone of you kindly help to get rid of this situation.
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} | 4 | 2021-01-27T10:37:38Z | 2021-02-02T13:19:25Z | 2021-02-02T13:19:21Z | CONTRIBUTOR | null | Add support for reading compressed dta files directly
xref #26599
- [X] closes #26599
- [X] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [X] whatsnew entry
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} | 3 | 2021-01-27T10:56:41Z | 2021-04-09T19:42:45Z | 2021-04-09T19:42:45Z | NONE | null | - [ ] 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
data = [
["A", "2018-01-01", 100],
["A", "2018-01-02", 200],
["B", "2018-01-01", 150],
["B", "2018-01-02", 250],
]
df = pd.DataFrame(data, columns=["id", "date", "num"])
df["date"] = pd.to_datetime(df["date"])
df.set_index(["date"], inplace=True)
df_res1 = df.groupby([df.id, df.index.weekday]).rolling(window=2, min_periods=1).mean()
df_res2 = df.groupby([df.id, df.index.weekday], as_index=False).rolling(window=2, min_periods=1).mean()
df_res3 = df.groupby([df.id]).rolling(window=2, min_periods=1).mean()
df_res4 = df.groupby([df.id], as_index=False).rolling(window=2, min_periods=1).mean()
pd.show_versions()
print(df_res1.head())
print(df_res1.index.names)
print(df_res2.index.names)
print(df_res3.index.names)
print(df_res4.index.names)
if __name__ == '__main__':
...
```
#### Problem description
Comparing pandas 1.0.1 and 1.2.1 yields:
1. When using a date part like df.index.weekday to group by and using some rolling window function like .mean() the original datetime index is not part of the resulting frames index anymore.
2. The group by keyword argumen as_index seems to have no effect in that situation anymore
#### Expected Output
['id', 'date', 'date']
[None, 'date']
['id', 'date']
[None, 'date']
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-45-generic
Version : #48-Ubuntu SMP Tue Jan 29 16:28:13 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.1
</details>
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} | 8 | 2021-01-27T11:07:50Z | 2021-02-26T14:21:24Z | 2021-02-26T14:21:24Z | 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
# Your code here
import pandas
import numpy as np
df = pandas.DataFrame(np.arange(64))
length = len(df.index)
df.index = [(i - length / 2) % length for i in range(length)]
df.sort_index(axis=0, ascending=None, na_position="first")
ValueError: the 'axis' parameter is not supported in the pandas implementation of argsort()
```
#### Problem description
There was no such issue in pandas version 1.1.5
#### Output of ``pd.show_versions()``
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : None
xarray : 0.16.2
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 0 | 2021-01-27T11:50:26Z | 2021-08-15T04:08:25Z | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
In general, the implementation of this idea should contribute to simplification of reading functions use and reduce the use of boilerplate code.
On the other hand, this shouldn't make it much more difficult to maintain that functions in Pandas.
Current reading approach (from Pandas docs):
```python
import glob
files = glob.glob('file_*.csv')
result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)
```
#### Describe the solution you'd like
We can make reading several files as "out-of-the-box" feature of Pandas (with using wildcard):
```python
result = pd.read_csv('file_*csv')
```
#### API breaking implications
In one of the two proposed solutions: `filepath_or_buffer` also can be of `list[str]` type.
Changes do not break backward compatibility.
#### Describe alternatives you've considered
Another possible option (using list of files in `read_*` call):
```python
import glob
result = pd.read_csv(glob.glob('file_*csv'))
```
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] | open | false | null | [] | null | 1 | 2021-01-27T16:21:10Z | 2021-07-05T16:29:31Z | null | NONE | null | This minimal example assigns the column B the first value of each group:
```
df = pd.DataFrame(data=np.asarray([[1,1,1,2],[4,4,4,10]]).transpose(),columns = ['A','B'])
print(df)
dfagg = df.groupby('A').agg({'B' : lambda x : x.iloc[0]})
print(dfagg)
```
Hence
```
A B
0 1 4
1 1 4
2 1 4
3 2 10
```
Becomes:
```
A
1 4
2 10
```
If I now modify the value of column B to an array of DataFrames
```
df1 = pd.DataFrame({'x' : [0,1], 'y' : [-1,-2]})
df2 = pd.DataFrame({'x' : [0,3], 'y' : [11,12]})
df = pd.DataFrame(data=np.asarray([[1,1,1,2],[df1,df1,df1,df2]],dtype=object).transpose(),columns = ['A','B'])
print(df)
dfagg = df.groupby('A').agg({'B' : lambda x : x.iloc[0]})
print(dfagg)
```
I get the error:
**ValueError: Must produce aggregated value**
It seems like this is a bug. There is no logic behind this behavior.
I also think that it did not raise an error in pandas like 2 years ago, when I used the code snippet the last time.
Currently using the newest version 1.2.1
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} | 7 | 2021-01-27T16:31:26Z | 2021-01-28T01:35:26Z | 2021-01-28T01:35:26Z | NONE | null |
#### Problem description
I am seeing some very strange behaviour. I have a python function which does simple manipulation of data in a DataFrame.
this code is very simple - unit tests work in the relevant package - however the behaviour is very different in server environment - where function is called via rest api.
It turns out that the difference is due to the fact that in the server environment the behaviour of the dataframe 'at' setter method, (ie: myframe.at[rowidx,colkey] = value) is different to that in the package test environment.
in the package test environment the effect of this method is to change the value of the appropriate element of the dataframe - but in the server environment it appears to insert a value after the referenced element. The dataframe changes size - even through the referenced element is within the defined bounds of the data.
as far as I can tell the versions of the relevant libraries packages are exactly the same in both cases - I might be wrong - if I dig into the pandas code - it seems to me to be unnecessarily convoluted, but as far as i can tell the problem happens with frame._set_value in the except clause.
I am a very experienced developer - but not with Python. I have written components similar to DataFrame in C++ in the distant past.
this kind of problem shakes my confidence in deploying anything which might exhibit this kind of behaviour - which causes serious and unpredictable errors downstream rather than an outright runtime error, or maybe I am just using it incorrectly.
I have dug into this quite a bit - and I am unclear how to further diagnose what is going on.
#### Output of ``pd.show_versions()``
<details>
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : English_United Kingdom.1252
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | 2021-01-27T16:40:30Z | 2021-01-28T15:04:29Z | 2021-01-28T15:04:20Z | CONTRIBUTOR | null | - [x] closes #39401
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 7 | 2021-01-27T17:22:23Z | 2021-02-23T21:59:23Z | 2021-01-28T19:50:04Z | CONTRIBUTOR | null | follow-up to #39253
- [x] closes #39247
- [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
Make sure that the user-provided mode is always preferred (if it contains 't' or 'b') and extend list of text-only classes. | {
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- Removed information for setting up an environment outside of Docker and created a new file for this purpose.
- Removed information for contributing to the codebase and created a new file for this purpose.
- Reformatted contributing.rst and renamed as contributing_documentation.rst (containing the remainder of information pertaining to documentation contributions).
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} | 5 | 2021-01-27T20:00:15Z | 2021-01-30T08:36:59Z | 2021-01-28T20:11:56Z | MEMBER | null | - [x] closes #39422
- [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
The ravel("K") was a problem when passing in a view on the underlying object, which was done here. | {
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} | 9 | 2021-01-27T21:37:24Z | 2021-08-31T11:12:03Z | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from datetime import datetime
pd.concat([
pd.DataFrame({'x': pd.Series(datetime(2021, 1, 1), index=[0], dtype='category')}),
pd.DataFrame({'x': pd.Series(datetime(2021, 1, 2), index=[1], dtype='category')}),
])
```
#### Problem description
Since v1.2, concatenating two or more dataframes with at least one categorical datetime column with different categories raises this error:
<details>
<summary>Traceback</summary>
```python-traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-36ad6bb4648d> in <module>
2 from datetime import datetime
3
----> 4 pd.concat([
5 pd.DataFrame({'x': pd.Series(datetime(2021, 1, 1), index=[0], dtype='category')}),
6 pd.DataFrame({'x': pd.Series(datetime(2021, 1, 2), index=[1], dtype='category')}),
venv/lib/python3.9/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
296 )
297
--> 298 return op.get_result()
299
300
venv/lib/python3.9/site-packages/pandas/core/reshape/concat.py in get_result(self)
518 mgrs_indexers.append((obj._mgr, indexers))
519
--> 520 new_data = concatenate_block_managers(
521 mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
522 )
venv/lib/python3.9/site-packages/pandas/core/internals/concat.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
78 values = values.reshape(1, len(values))
79
---> 80 b = make_block(values, placement=placement, ndim=blk.ndim)
81 else:
82 b = make_block(
venv/lib/python3.9/site-packages/pandas/core/internals/blocks.py in make_block(values, placement, klass, ndim, dtype)
2730 values = DatetimeArray._simple_new(values, dtype=dtype)
2731
-> 2732 return klass(values, ndim=ndim, placement=placement)
2733
2734
venv/lib/python3.9/site-packages/pandas/core/internals/blocks.py in __init__(self, values, placement, ndim)
135 """
136 # TODO(EA2D): ndim will be unnecessary with 2D EAs
--> 137 self.ndim = self._check_ndim(values, ndim)
138 self.mgr_locs = placement
139 self.values = self._maybe_coerce_values(values)
venv/lib/python3.9/site-packages/pandas/core/internals/blocks.py in _check_ndim(self, values, ndim)
184
185 if self._validate_ndim and values.ndim != ndim:
--> 186 raise ValueError(
187 "Wrong number of dimensions. "
188 f"values.ndim != ndim [{values.ndim} != {ndim}]"
ValueError: Wrong number of dimensions. values.ndim != ndim [1 != 2]
```
</details>
I have confirmed that:
- it happens on `v1.2.0`, `v1.2.1`, and `v1.3.0.dev0+553.gd0cfa0303` on both Windows 10 and Linux
- it doesn't happen before `v1.2` or if the categories are the same
#### Expected Output
A single, properly concatenated dataframe:
```
x
0 2021-01-01
1 2021-01-02
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.10-zen1-1-zen
Version : #1 ZEN SMP PREEMPT Sat, 23 Jan 2021 23:59:50 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.1
setuptools : 52.0.0
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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} | 1 | 2021-01-28T01:39:54Z | 2021-01-28T02:22:36Z | 2021-01-28T02:18:59Z | 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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} | 1 | 2021-01-28T01:58:37Z | 2021-02-01T23:12:40Z | 2021-02-01T22:32:37Z | MEMBER | null | The dtype-finding code in internals.concat is something else. Tried untangling it, but now I think we can make it unnecessary altogether. This is the first step in that process. | {
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] | open | false | null | [] | null | 4 | 2021-01-28T08:00:41Z | 2021-08-15T04:09:48Z | null | NONE | null | #### Pandas interpolate doesn't give monotonic results
I have the following data and I want to apply the interpolate method using a spline to the last 4 numbers (I'm aware this is extrapolation):
```python
import numpy as np
x = [
18.792571,
19.170139,
19.370556,
19.393820,
19.239932,
18.908891,
18.400699,
17.892507,
17.384314,
16.876122,
16.367930,
15.859737,
np.nan,
np.nan,
np.nan,
np.nan
]
```
I'm running pandas interpolate and a very weird thing is going on, as the code
```python
import pandas as pd
pd.Series(x).interpolate(
method="spline",
order=1
)
```
returns
```python
0 18.792571
1 19.170139
2 19.370556
3 19.393820
4 19.239932
5 18.908891
6 18.400699
7 17.892507
8 17.384314
9 16.876122
10 16.367930
11 15.859737
12 16.103099
13 15.790022
14 15.476945
15 15.163868
dtype: float64
```
So while the trend of the data is clearly negative since very early indices the interpolation produces a jump upwards. When running the same computation using scipy
```python
import scipy.interpolate as inp
train_x = [_ for _ in x if _ > 0]
s = inp.InterpolatedUnivariateSpline(range(len(train_x)), train_x, k=1)
ynew = s(range(len(x)))
ynew[12:]
```
I get
```python
array([15.351544, 14.843351, 14.335158, 13.826965])
```
in this case, the interpolation doesn't have an upward change, so the results make sense to me.
Using scipy interp1d I have the same issue:
```python
s = inp.interp1d(range(len(train_x)), train_x, kind=1, fill_value='extrapolate')
ynew = s(range(len(x)))
ynew[12:]
```
Gives
```python
array([15.351544, 14.843351, 14.335158, 13.826965])
```
My questions are, then:
* Why are the pandas and scipy results different?
* How to make pandas `interpolate` give the results that I obtain using scipy?
* Why does this upward change happen in pandas?
Thanks in advance!
----
Requirements file:
```
pandas==1.0.4
scipy==1.5.2
```
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] | open | false | null | [] | null | 0 | 2021-01-28T08:03:34Z | 2021-08-15T04:10:40Z | null | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
Under certain cases, doing an assignment on a DataFrame created using `.dropna()` emits the infamous . So I followed what [this SO answer](https://stackoverflow.com/a/61240274/554319) suggests, and inspected the `._is_view` and `._is_copy` attributes:
```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df1 = pd.DataFrame([[1.0, np.nan, 2, np.nan],
...: [2.0, 3.0, 5, np.nan],
...: [np.nan, 4.0, 6, np.nan]], columns=list("ABCD")); df1
Out[3]:
A B C D
0 1.0 NaN 2 NaN
1 2.0 3.0 5 NaN
2 NaN 4.0 6 NaN
In [4]: df2 = df1.dropna(axis="columns", how="all")
In [5]: df2._is_view, df2._is_copy
Out[5]: (False, <weakref at 0x7f56e8ecfd60; to 'DataFrame' at 0x7f56e916f9d0>)
In [6]: df2["A"] = df2["A"].fillna(df2["B"])
<ipython-input-6-9c99ac454f90>:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df2["A"] = df2["A"].fillna(df2["B"])
```
However, this `_is_copy` is set to None if you actually call `.copy()` (misleading variable name?), which on the other hand removes the warning:
```python
In [17]: df3 = df1.dropna(axis="columns", how="all").copy()
In [18]: df3._is_view, df3._is_copy
Out[18]: (False, None)
In [19]: df3["A"] = df3["A"].fillna(df3["B"])
In [20]:
```
But, finally, `.fillna` result does have `_is_copy` set to `None`:
```python
In [20]: df1 = pd.DataFrame([[1.0, np.nan], [2.0, 3.0]], columns=["A", "B"]); df1
Out[20]:
A B
0 1.0 NaN
1 2.0 3.0
In [21]: df2 = df1.dropna(axis="columns", how="all")
In [22]: df2._is_view, df2._is_copy
Out[22]: (False, None)
In [23]: df2["A"] = df2["A"].fillna(df2["B"])
In [24]:
```
Can anybody explain what's the meaning of the `SettingWithCopyWarning` in this context and why do I still get despite `_is_view` being `False` in all cases?
I am particularly worried about this, in light of what the pandas docs say:
> Sometimes a `SettingWithCopy` warning will arise at times when there’s no obvious chained indexing going on. **These** are the bugs that `SettingWithCopy` is designed to catch!
_Originally asked at https://stackoverflow.com/q/65926892/554319_
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} | 11 | 2021-01-28T15:49:39Z | 2021-03-09T22:03:54Z | 2021-03-09T22:03:54Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
Bug exists in 1.2.1, not in <=1.2.0
- [ ] (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
from pathlib import Path
import pandas as pd
file = Path("non-utf8.csv")
file.write_bytes(b"\xe4\na\n1") # non utf-8 character
df = pd.read_csv(file)
```
Output:
```
�
0 a
1 1
```
#### Problem description
In pandas version 1.2.1, reading a csv file containing non utf-8 characters does not raise a `UnicodeDecoreError`. In version 1.2.0 and earlier, a `UnicodeDecodeError` is raised, allowing proper exception handling in application code.
#### Expected Output
Expect a `UnicodeDecodeError` to be raised.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : French_Canada.1252
pandas : 1.2.1
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
```
</details>
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} | 5 | 2021-01-28T16:50:29Z | 2021-02-26T16:38:25Z | 2021-02-26T14:21:24Z | MEMBER | null | - [ ] closes #39434
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Added validation of ``ascending`` for ``sort_index`` methods.
Proposed a solution to raise ``ValueError`` when either of the elements of ``ascending`` turned out to be ``None``. | {
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} | 7 | 2021-01-28T20:00:32Z | 2021-02-02T02:46:09Z | 2021-02-02T02:42:26Z | 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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} | 0 | 2021-01-28T20:36:15Z | 2021-02-01T23:14:09Z | 2021-02-01T22:56:09Z | MEMBER | null | - [ ] closes #xxxx
- [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
I'm pretty sure we're going to end up with just using find_common_type instead of re-implementing similar stuff in internals.concat. | {
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} | 2 | 2021-01-28T21:13:12Z | 2021-01-29T02:03:03Z | 2021-01-29T02:02:57Z | CONTRIBUTOR | null | Should take care of the occasional `OSError: [Errno 98] Address already in use` in `test_server_and_default_headers`. | {
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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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} | 5 | 2021-01-28T21:39:09Z | 2021-02-02T02:00:00Z | 2021-02-02T01:59:49Z | CONTRIBUTOR | 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
- [ ] whatsnew entry
The issue is that, `kh_resize_##name` expects the number of buckets in the map as input: https://github.com/pandas-dev/pandas/blob/39e126199b1d2082d9d084f7a37c9745ab9a95ee/pandas/_libs/src/klib/khash.h#L324
it is however used with the number of elements throughout the pandas code, e.g. https://github.com/pandas-dev/pandas/blob/39e126199b1d2082d9d084f7a37c9745ab9a95ee/pandas/core/algorithms.py#L412
as can be seen in the `__cinit__` function: https://github.com/pandas-dev/pandas/blob/39e126199b1d2082d9d084f7a37c9745ab9a95ee/pandas/_libs/hashtable_class_helper.pxi.in#L400-L404
Thus, for some sizes (like 10^3) there still could be a rehashing even if size-hint was used with the idea to avoid it (because at most 75% of buckets can be occupied)
I think it make sense to do the less surprising thing with the size-hint (i.e. the number of elements and not buckets).
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} | 1 | 2021-01-28T21:59:25Z | 2021-01-29T02:04:31Z | 2021-01-29T02:04:26Z | CONTRIBUTOR | null | - [x] closes #38808
- [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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} | 1 | 2021-01-28T23:24:35Z | 2021-01-29T02:52:40Z | 2021-01-29T02:10:56Z | 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
Last move from aggregation.aggregate to apply. Will follow up with moving helper methods from aggregation into apply. | {
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} | 0 | 2021-01-28T23:41:55Z | 2021-01-29T03:03:44Z | 2021-01-29T02:07:57Z | MEMBER | null | Moves this chunk of code incrementally towards being BlockManager/ArrayManager agnostic | {
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} | 1 | 2021-01-29T01:12:28Z | 2021-02-02T15:12:31Z | 2021-02-02T13:34:23Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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This started off with editing na_value_for_dtype so that it could be re-used in #39446, ended up snowballing into assert_attr_equal. If requested, I'll split off the bugfix into its own PR. | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 6 | 2021-01-29T03:46:02Z | 2021-02-04T00:50:57Z | 2021-02-04T00:50:51Z | CONTRIBUTOR | null | - [x] closes #39426
- [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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The problem occurs when sorting on columns and the `ignore_index` parameter is set to `True`. `sort_values` tries to refit the index after the columns have been sorted resulting in a corrupted dataframe. | {
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} | 5 | 2021-01-29T04:58:39Z | 2021-11-17T02:09:41Z | 2021-11-17T02:09:41Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
a=pd.DataFrame({'a':[1,2,3,4,5],'b':[4,5,6,7,8]})
a.to_csv('myfile.csv.zip')
```
#### Problem description
when use gzip method, the function works fine, a.to_csv('myfile.csv.gz') will create a gzip file with an archive file named myfile.csv.
the "zip" method works bad,a.to_csv('myfile.csv.zip') will create a zipfile with an archive file named myfile.csv.zip
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} | 3 | 2021-01-29T08:38:24Z | 2021-02-11T14:29:48Z | 2021-02-11T13:40:46Z | 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
The [mypy daemon](https://mypy.readthedocs.io/en/latest/mypy_daemon.html) throws some extra `var-annotated` errors compared with `mypy pandas`, so I've tried fixing some. Some of the variables it complains about seem to be unused (ExcelWriter.curr_sheet and NDFrame._ix) - are these safe to remove? | {
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} | 5 | 2021-01-29T12:33:46Z | 2021-02-25T01:58:40Z | null | NONE | null | #### Is your feature request related to a problem?
As a part of a research project, me and two co-authors have been mining GitHub for Jupyter notebooks and studied a number of frequent function calls in the notebooks. It appears that users sometimes make calls to `read_csv`, specifying `dtype` as a dict containing keys that are not present in the data set. When this happens, these keys are silently ignored. To take a simple example, one may write
`df = pd.read_csv("my_data.csv", dtype={"a":float})`
although there is no column named "a" in "my_data.csv". The key-value pair "a"-float is silently ignored.
This means that a user who misspells a column header, or tries to change the data type of a column that is filtered out (e.g. using `usecols`), or of a non-existing column that the user thinks exists, he/she will not get aware of this, and the data will not be converted as expected. If, instead, `read_csv` issued a warning, the user could easily detect the mistake and be able to fix it immediately. As a bonus, such a warning could also help remedying other bugs. For example, if a user tries to change the data type of the values in a column that is filtered out, the user most likely thinks that he/she filters out other columns than he/she actually does. A warning from `read_csv` would indicate that there is something wrong, and a user could find a bug that may otherwise go undetected or take time to debug.
We have noticed the same behavior when dicts passed as `converters` contain keys that are missing in the data set. For the same reasons as above, I think issuing a warning in these cases would be helpful for the users.
#### Describe the solution you'd like
`read_csv` should issue a warning when dicts passed as values for `dtype` or `converters` contain keys that are not present in the data set.
#### API breaking implications
If only a warning is issued, I don't see how this could break anything.
#### Describe alternatives you've considered
An alternative could be to issue an error instead of a warning, but that would risk breaking existing code. | {
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https://github.com/pandas-dev/pandas/blob/master/doc/source/development/test_writing.rst
#### Documentation problem
The `test_writitng.rst` file doesn't have the standard header.
#### Suggested fix for documentation
This can be fixed by adding `{{ header }}` at the beginning of the file. | {
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} | 5 | 2021-01-30T00:52:12Z | 2021-02-20T20:32:26Z | 2021-02-20T20:32:14Z | CONTRIBUTOR | null | Progress towards #28283
1. refactor `pivot_table` function to avoid overhead of `_finalize__` calls when a list of aggfuncs passed
2. call `__finalize__` before returning `groupby.mean` and `groupby.median`
3. add tests for 1 and 2
4. add ignored test cases for `groupby.std/var/sem/size/ohlc/describe` as a reminder to fix(they are failing currently)
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} | 1 | 2021-01-30T01:52:17Z | 2021-02-02T22:16:55Z | 2021-02-02T22:14:57Z | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
s = pd.Series(['foo', 'bar', 'baz'])
assert s.astype(bytes).dtype.kind == 'S', "kind is not S!" # works in 1.1.x, 1.2.0, does not work in 1.2.1 (kind is 'O')
```
#### Problem description
Setting the type of a series to `bytes` changes str instances to `bytes`, but keeps the type as `object`. This seems to be a regression, in prior versions the type was changed to `bytesXX`. In pandas 1.2.1:
```py
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '1.2.1'
In [3]: s = pd.Series(['foo', 'bar', 'baz'])
In [4]: s.astype('|S')
Out[4]:
0 b'foo'
1 b'bar'
2 b'baz'
dtype: object
In [5]: s.astype('bytes')
Out[5]:
0 b'foo'
1 b'bar'
2 b'baz'
dtype: object
In [6]: s.astype(bytes)
Out[6]:
0 b'foo'
1 b'bar'
2 b'baz'
dtype: object
```
#### Expected Output
I expected this output, produced with 1.2.0:
```py
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '1.2.0'
In [3]: s = pd.Series(['foo', 'bar', 'baz'])
In [4]: s.astype('|S')
Out[4]:
0 b'foo'
1 b'bar'
2 b'baz'
dtype: bytes24
In [5]: s.astype('bytes')
Out[5]:
0 b'foo'
1 b'bar'
2 b'baz'
dtype: bytes24
In [6]: s.astype(bytes)
Out[6]:
0 b'foo'
1 b'bar'
2 b'baz'
dtype: bytes24
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.17-1rodete4-amd64
Version : #1 SMP Debian 5.7.17-1rodete4 (2020-10-01)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : 0.29.21
pytest : 4.6.11
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2021-01-30T01:54:56Z | 2021-01-30T08:35:33Z | 2021-01-30T08:35:26Z | MEMBER | null | - [x] closes #39422 | {
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} | 1 | 2021-01-30T01:57:17Z | 2021-01-30T08:36:36Z | 2021-01-30T08:36:36Z | MEMBER | null | - [x] whatsnew entry
Move the whatsnew for backport | {
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} | 0 | 2021-01-30T03:35:16Z | 2021-02-02T15:26:02Z | 2021-02-02T13:36:27Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | open | false | null | [] | null | 0 | 2021-01-30T14:07:00Z | 2021-02-03T15:08:25Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# import pandas as pd
# import numpy as np
# like https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.cumsum.html
# Ex1
>>> pd.Series([2, np.nan, 5, -1, 0], dtype="float").cumsum()
0 2.0
1 NaN
2 7.0
3 6.0
4 6.0
dtype: float64
# Ex2
>>> pd.Series([2, np.nan, 5, -1, 0], dtype="Float64").cumsum()
0 2.0
1 2.0
2 7.0
3 6.0
4 6.0
dtype: object
# Ex3
>>> pd.Series([2, np.nan, 5, -1, 0], dtype="Int64").cumsum()
0 2
1 <NA>
2 <NA>
3 <NA>
4 <NA>
dtype: object
```
#### Problem description
- Ex1 is expected behavior while Ex2 and Ex3 yield different values.
- Ex2 returns `dtype: object` but `Float64` is expected, the same applies to Ex3.
- Other cum-family methods (cumprod, cummax, cummin) have the same problem.
#### Expected Output
```python
# Ex2
>>> pd.Series([2, np.nan, 5, -1, 0], dtype="Float64").cumsum()
0 2.0
1 <NA>
2 7.0
3 6.0
4 6.0
dtype: Float64
# Ex3
>>> pd.Series([2, np.nan, 5, -1, 0], dtype="Int64").cumsum()
0 2
1 <NA>
2 7
3 6
4 6
dtype: Int64
```
#### Environment
```Dockerfile
FROM python:3.9.1-slim-buster
WORKDIR /home
RUN pip install pandas==1.2.1
CMD ["/usr/local/bin/python"]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.121-linuxkit
Version : #1 SMP Tue Dec 1 17:50:32 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0
setuptools : 52.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2021-01-30T15:22:13Z | 2021-02-02T13:06:56Z | 2021-02-02T13:06:41Z | NONE | null | - [x] I have checked that this issue has not already been reported. (not reported in pandas repo as I could see, but has been incidentally in pyarrow repo)
- [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
file = '/home/whatever_test_directory/test.parquet'
# Does not work: category based on int
df_test = pd.DataFrame({'a':[1,2,3]}).astype({'a':'category'})
print(df_test['a']) # check category has been correctly created
```
Name: a, dtype: category
Categories (3, int64): [1, 2, 3]
```python
df_test.to_parquet(file)
df_read = pd.read_parquet(file)
print(df_read['a']) # no more a catgory
```
Name: a, dtype: int64
```python
# Does work: category based on string
df_test = pd.DataFrame({'a':['1','2','3']}).astype({'a':'category'})
print(df_test['a']) # check category has been correctly created
```
Name: a, dtype: category
Categories (2, object): ['1', '2', '3']
```python
df_test.to_parquet(file)
df_read = pd.read_parquet(file)
print(df_read['a']) # still a catgory!
```
Name: a, dtype: category
Categories (2, object): ['1', '2', '3']
#### Problem description
Even when based on int, category should survive a round/trip in parquet.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-41-generic
Version : #46~20.04.1-Ubuntu SMP Mon Jan 18 17:52:23 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.1
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
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} | 4 | 2021-01-30T15:53:12Z | 2021-02-04T01:52:52Z | 2021-02-04T01:52:52Z | NONE | null | ```
import numpy as np
import pandas as pd
df = pd.DataFrame(
np.zeros([1, 5]),
columns=pd.MultiIndex.from_tuples(
[
(0, None, None),
(0, 0, 0),
(0, 0, 1),
(0, 1, 0),
(0, 1, 1),
],
)
)
```
```
df.stack(2)
```
```
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.6.final.0
pandas : 1.1.5
numpy : 1.17.4
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} | 5 | 2021-01-30T16:43:48Z | 2021-02-01T23:09:03Z | 2021-02-01T13:49:12Z | MEMBER | null | - [x] closes #39250
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IMO the core issue in #39250 is upstream in openpyxl, providing a better error message is really tangential to it. Since the error message that openpyxl raises is not a regression, it seemed best to me to (a) target this for 1.3 and (b) unify all error messages across engines.
If, on the other hand, we really do want to improve the error message in 1.2.2 because of the switch from xlrd to openpyxl, then I will open a new PR for 1.2.2 that only impacts openpyxl.
Will do a followup to remove the duplicate tests.
cc @simonjayhawkins @jorisvandenbossche | {
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Not sure if this is the right place | {
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} | 18 | 2021-01-30T18:55:51Z | 2021-02-07T12:05:14Z | 2021-02-05T03:15:59Z | MEMBER | null | - [x] closes #38956
- [x] closes #39001
- [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
Targeted for 1.2.2 because this resolves an issue that occurs from the change of default engine from xlrd to openpyxl in 1.2. | {
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} | 2 | 2021-01-30T19:07:06Z | 2021-01-30T20:29:02Z | 2021-01-30T20:26:24Z | MEMBER | null | Pyarrow 2.0.0 instead of 0.15.1 is fetched on these builds since today, which causes test failures | {
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} | 3 | 2021-01-30T19:12:19Z | 2021-02-03T02:11:38Z | 2021-02-03T01:31:04Z | MEMBER | null | - [ ] closes #xxxx
- [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
We fixed this a few days ago for some of the setting-methods, but there were a few we missed. Re-using the TestSetitemEquivalents pattern is much more thorough.
We'll be able to de-duplicate the test class following #39477 | {
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} | 3 | 2021-01-30T19:56:30Z | 2021-08-15T04:15:42Z | 2021-08-15T04:15:42Z | NONE | null | - I have asked my usage related question on [StackOverflow](https://stackoverflow.com/questions/65955108/pandas-groupby-aggregation-behavior).
---
#### Question About GroupBy Aggregation Behavior
I observed a behavior of the Pandas GroupBy method `agg()` which I cannot explain based on the [docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.aggregate.html).
I want to perform a custom aggregation on a grouped `DataFrame`. Let's take this code snippet:
```python
def aggregate_point(x):
print(x)
exit()
data = data.groupby(
pd.Grouper(level='time', freq='1H')).agg(aggregate_point)
```
This prints a `Series` for each group.
However, when I pass any additional parameter to `aggregate_point`, the full `DataFrame` is being printed:
```python
def aggregate_point(x, score):
print(x)
exit()
data = data.groupby(
pd.Grouper(level='time', freq='1H')).agg(aggregate_point, score=True)
```
Why does the `score` parameter influence the value of `x` (`DataFrame` vs. `Series`)? | {
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} | 3 | 2021-01-30T20:37:14Z | 2021-02-02T01:42:46Z | 2021-02-02T01:42:42Z | CONTRIBUTOR | null | - [x] closes #38865
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} | 4 | 2021-01-30T21:37:13Z | 2021-02-02T10:07:00Z | 2021-02-02T01:39:26Z | CONTRIBUTOR | null | xref #39450
This should probably go in 1.2.2.
<1.2.0: `engine=c` and `engine=python` handled encoding errors differently when `encoding=None` (python: "replace", c: "strict"). The `engine=c` further supported to ignore lines (`skiprows`) with encoding errors iff `encoding=None`.
1.2.0 (shared file opening code between `engine=c` and `engine=python`): both engine mistakenly used "strict" when `encoding=None`. `engine=c`+`encoding=None` can no longer skip encoding errors.
1.2.1: both engine use "replace" when `encoding=None`. Both engines can ignore encoding errors in skipped rows iff `encoding=None`.
Option for 1.3: default to "strict" but expose `errors` in `read_csv` (and other read calls that might need it)?
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} | 3 | 2021-01-31T00:11:42Z | 2021-02-02T00:27:43Z | 2021-02-02T00:27:39Z | CONTRIBUTOR | null | previous PR #39079
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] | open | false | null | [] | null | 2 | 2021-01-31T13:03:40Z | 2021-06-06T18:16:06Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import fastparquet as fp
from os import path as os_path
df = pd.DataFrame({'ab': [1, 2, 3], 'a':['a', 'b', 'c']})
file = os_path.expanduser('~/Documents/code/data/test.parquet')
# Case 1: write: pandas/pyarrow || read: pandas/pyarrow
# OK : 'ab' column is read back with type 'category'
# NOOK : does not naturally overwrite (file names are different)
# as a consequence, df that is read back contains twice more data.
# OK : with snappy not installed, naturally does not compress data.
df.to_parquet(file, partition_cols=['ab'])
df.to_parquet(file, partition_cols=['ab'])
df_rec_pd1 = pd.read_parquet(file)
print(df_rec_pd1['ab'])
# Case 2: write: pandas/fastparquet || read: pandas/fastparquet
# OK : 'ab' column is read back with type 'category'
# OK : naturally overwrites (same file names)
# NOOK : with snappy not installed, it does not naturally not compress the data,
# and use of 'compression' keyword is required to specify 'uncompressed'.
df.to_parquet(file, partition_cols=['ab'], engine='fastparquet', compression='uncompressed')
df.to_parquet(file, partition_cols=['ab'], engine='fastparquet', compression='uncompressed')
df_rec_pd2 = pd.read_parquet(file, engine='fastparquet')
print(df_rec_pd2['ab'])
# Case 3: write: pandas/fastparquet || read: pandas/pyarrow
# OK : 'ab' column is read back with type 'category'
# OK : naturally overwrites (same file names)
# NOOK : with snappy not installed, it does not naturally not compress the data,
# and use of 'compression' keyword is required to specify 'uncompressed'.
df.to_parquet(file, partition_cols=['ab'], engine='fastparquet', compression='uncompressed')
df.to_parquet(file, partition_cols=['ab'], engine='fastparquet', compression='uncompressed')
df_rec_pd3 = pd.read_parquet(file)
print(df_rec_pd3['ab'])
# Case 4: write: pandas/pyarrow || read: pandas/fastparquet
# NOOK : reading does not work. pyarrow does not generate common metada which
# fastparquet is looking for.
df.to_parquet(file, partition_cols=['ab'])
df.to_parquet(file, partition_cols=['ab'])
df_rec_pd4 = pd.read_parquet(file, engine='fastparquet')
print(df_rec_pd4['ab'])
# Case 5: write: fastparquet || read: pandas/pyarrow
# OK : 'ab' column is read back with type 'category'
# OK : naturally overwrites (same file names)
fp.write(file, df, file_scheme='hive', partition_on=['ab'], compression='BROTLI')
fp.write(file, df, file_scheme='hive', partition_on=['ab'], compression='BROTLI')
df_rec_fp_pd = pd.read_parquet(file)
# Case 6: write: fastparquet || read: fastparquet
# OK : nothing to say, perfect :)
fp.write(file, df, file_scheme='hive', partition_on=['ab'])
fp.write(file, df, file_scheme='hive', partition_on=['ab'])
df_rec_fp = fp.ParquetFile(file).to_pandas()
# Case 7: write: pandas/pyarrow || read: fastparquet
# NOOK : reading does not work. It seems that even if snappy is not available
# at writing, it is indicated in metadata as the engine that has been
# used. As fastparquet is not finding it, it raise an error.
df.to_parquet(file)
df_rec_pd_fp = fp.ParquetFile(file).to_pandas()
# Case 8: write: pandas/pyarrow || read: fastparquet
# NOOK : bug already reported: https://github.com/pandas-dev/pandas/issues/39480
# It seems pandas/pyarrow is not writing categories of int as categories.
df.to_parquet(file, compression='BROTLI')
df_rec_pd_fp = fp.ParquetFile(file).to_pandas()
```
#### Problem description
Hi. I report here 4 different problems as far as I can see (a 5th related one having been reported earlier in [#39480](https://github.com/pandas-dev/pandas/issues/39480)). Trying to synthetize:
- when using `partition_cols` in `to_parquet`, the ways it is managed by fastparquet and pyarrow are different. Most notably, fastparquet is writing common metdata in the root directory, while pyarrow ...? I don't know what it does, but at least, it does not write this 'common metadata' file. This raises a bug as in case 4: writing with pyarrow, reading with fastparquet. fastparquet does not find the common metadata file and refuses to read the data
- when not having 'snappy' installed, for writing step, no error is raised with pyarrow. But when read back with fastparquet, this time, reader believes it has been compressed with 'snappy' and complains not to find it. This raises the error as in case 7.
- when not having 'snappy' installed, for the writing and reading steps, could the writer and reader simply/naturally fall back to 'uncompressed'? With fastparquet, this forces the user to add the parameter `compression` set to `uncompressed` as in cases 2, & 3.
- when using `partition_cols` with pyarrow, names of parquet files are always different. Hence, writing twice the same dataset will result in duplicating the data (on the opposite, fastparquet uses always the same file names, which ensures natural overwriting), as in case 1.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-41-generic
Version : #46~20.04.1-Ubuntu SMP Mon Jan 18 17:52:23 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.1
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 2 | 2021-01-31T14:00:36Z | 2021-02-02T14:55:41Z | 2021-02-01T19:15:10Z | CONTRIBUTOR | null | - [x] closes #39313
- [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 - no
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] | closed | false | null | [] | null | 28 | 2021-01-31T14:54:12Z | 2021-09-08T22:21:55Z | 2021-05-24T19:48:47Z | CONTRIBUTOR | null | Modifications to reduce error reports from `pyright --verifytypes` related to `ExtensionArray` and `ExtensionDtype`.
Some notes:
1. Had to use `Any` in a number of places, because an `ExtensionArray` can contain any type
2. Because `numpy` doesn't have a released version with types, `pyright` complains whenever we work with `numpy` types, so for type checking purposes, I redefine `np.dtype` and `np.ndarray` to a dummy type. When `numpy` 1.20 is released, we can most likely remove that. Alternatively, I can find out if there is a way to see if `pyright` has something like `TYPE_CHECKING` to bracket those changes.
I anticipate these changes might be controversial, so constructive criticism is welcome!
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} | 1 | 2021-01-31T17:30:06Z | 2021-01-31T22:14:11Z | 2021-01-31T21:15:46Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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.
Readed! I will know for next time
#### Code Sample, a copy-pastable example
```python
There is no need for code, as the error is on the function having repeated keywords.
```
#### Problem description
There are two repeated keywords on the master/pandas/core/indexes/datetimes.py#L560 and #L561.
I closed the issue #38121, as it is working fine, but as said, there are repeated keywords.
valid_resos = {
"year",
"month",
"quarter",
"day",
"hour",
"minute",
"second",
"minute",
"second",
"millisecond",
"microsecond",
}
it should be:
valid_resos = {
"year",
"month",
"quarter",
"day",
"hour",
"minute",
"second",
"millisecond",
"microsecond",
}
Reference here:
https://github.com/pandas-dev/pandas/blob/0b16fb308b7808f193a5a8d86d71b6e57d99d4e7/pandas/core/indexes/datetimes.py#L560-L561
#### Expected Output
valid_resos = {
"year",
"month",
"quarter",
"day",
"hour",
"minute",
"second",
"millisecond",
"microsecond",
}
#### Output of ``pd.show_versions()``
There is no need for versions. Pandas code in this case is fine. Just two repeated lines.
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] | closed | false | null | [] | null | 5 | 2021-01-31T18:11:01Z | 2021-04-24T13:35:55Z | 2021-04-24T13:35:54Z | CONTRIBUTOR | null | Incremental typing PR for #26302
- [ ] 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 | 2021-01-31T18:18:56Z | 2021-02-01T13:36:32Z | 2021-01-31T19:36:22Z | CONTRIBUTOR | null |
- [x] closes #38862
- [ ] 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
-----
Since pre-commit in comment-bot runs on all files, it's a bit slow. Running it only on diff should speed it up significantly. | {
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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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- [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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https://api.github.com/repos/pandas-dev/pandas/issues/39509 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39509/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39509/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39509/events | https://github.com/pandas-dev/pandas/issues/39509 | 797,804,164 | MDU6SXNzdWU3OTc4MDQxNjQ= | 39,509 | trying to drop columns on a series silently fails | {
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```python
c = pd.DataFrame({ 'a':[1,2] ,'b':[3,4]})
d = c.iloc[0] # makes a series
e = d.drop(columns=['a'])
'''
a 1
b 3
Name: 0, dtype: int64
'''
```
#### Problem description
Drop silently fails on series. It does nothing when the user tries an impossible operation (dropping column from a series).
#### Expected Output
Error should be raised.
'Cannot drop columns from a series'
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_United States.1252
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.3.3.post20210118
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
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} | 19 | 2021-01-31T20:29:53Z | 2021-06-30T14:46:15Z | 2021-03-16T16:56:07Z | 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
xref https://github.com/pandas-dev/pandas/issues/38896#issuecomment-761620727 | {
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} | 2 | 2021-01-31T21:08:27Z | 2021-02-03T02:08:56Z | 2021-02-03T01:30:50Z | MEMBER | null | Also hopefully fixes some docbuild issues | {
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} | 4 | 2021-01-31T21:37:48Z | 2021-02-02T21:14:21Z | 2021-02-01T13:42:29Z | MEMBER | null | This should get the ipython directives passing | {
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} | 5 | 2021-01-31T22:03:40Z | 2021-03-10T20:46:44Z | 2021-03-10T20:45:15Z | MEMBER | null | Mypy raises a lot of errors since the new numpy is available on conda-forge. What is the best approach here? Adding ignores for everything and fixing them step by step afterwards?
cc @simonjayhawkins | {
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} | 5 | 2021-01-31T22:04:03Z | 2021-02-11T15:18:46Z | 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```
caleb ~ $ cat sample_read_wrong.tsv
CAS Chemical A B C D E
7440-63-3 Xenon -0.0000023692 0.000000098454 -0.000000000048314 0.00000000000001953 -3.42E-018
7439-90-9 Krypton -0.000000792 0.000000102624 -0.000000000055428 0.00000000000002187 -3.69E-018
7440-37-1 Argon 0.0000016196 0.000000081279 -0.000000000041263 0.00000000000001668 -2.76E-018
7440-01-9 Neon 0.0000023014 0.000000122527 -0.000000000097141 0.00000000000005386 -1.103E-017
```
```In [1]: import pandas as pd
In [2]: pd.read_csv('sample_read_wrong.tsv',delimiter='\t')
Out[2]:
CAS Chemical A ... C D E
0 7440-63-3 Xenon -2.369200e-06 ... -4.831400e-11 1.950000e-14 -3.420000e-18
1 7439-90-9 Krypton -7.920000e-07 ... -5.542800e-11 2.180000e-14 -3.690000e-18
2 7440-37-1 Argon 1.619600e-06 ... -4.126300e-11 1.660000e-14 -2.760000e-18
3 7440-01-9 Neon 2.301400e-06 ... -9.714100e-11 5.380000e-14 -1.103000e-17
[4 rows x 7 columns]
```
#### Problem description
Pandas is no longer reading a CVS file with numbers that look like "0.00000000000001953" correctly. In this case, Pandas reads that as 1.950000e-14 - a clear loss of precision. This appears to be a bug in the new "high precision" floating point parsing engine that was made the default in Pandas 1.2.0.
#### Expected Output
``` CAS Chemical A ... C D E
0 7440-63-3 Xenon -2.369200e-06 ... -4.831400e-11 1.953000e-14 -3.420000e-18
1 7439-90-9 Krypton -7.920000e-07 ... -5.542800e-11 2.187000e-14 -3.690000e-18
2 7440-37-1 Argon 1.619600e-06 ... -4.126300e-11 1.668000e-14 -2.760000e-18
3 7440-01-9 Neon 2.301400e-06 ... -9.714100e-11 5.386000e-14 -1.103000e-17
[4 rows x 7 columns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.0-2-amd64
Version : #1 SMP Debian 5.7.10-1 (2020-07-26)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.2.1
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.1.1
setuptools : 51.3.3
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.52.0
</details>
I was able to work around this with the {'float_precision': 'legacy'} option, but it is not great behavior and old versions of the [library](https://github.com/CalebBell/Chemicals) I wrote that experienced this issue will silently break. | {
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] | closed | false | null | [] | null | 3 | 2021-01-31T22:57:17Z | 2021-02-11T15:18:35Z | 2021-02-11T14:36:08Z | NONE | null | - [x] closes #39514
- [ ] 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
Trying this change out to see what the tests say. It fixes the current issue I experienced. | {
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} | 5 | 2021-02-01T01:04:20Z | 2021-02-27T19:00:33Z | 2021-02-27T18:15:01Z | CONTRIBUTOR | null | … (GH27554)
- [X] closes #27554
- [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
- [X] io entry
- [X] install entry
To Do List
- [ ] Add `parse_dates` feature for `read_xml`.
- [ ] Add tests for `storage_options`.
- [ ] Add tests for `ParserError`, `OSError`, `URLError`, etc.
- [ ] Add `xpath_vars` feature to pass `$` variables in `xpath` expression. See [lxml xpath() method](https://lxml.de/xpathxslt.html#the-xpath-method).
- [ ] Add `xsl_params` feature to pass values into XSLT script. See [lxml stylesheet parameters](https://lxml.de/xpathxslt.html#stylesheet-parameters).
- [ ] Add `iterparse` feature for memory efficient parsing of large XML. See [etree iterparse](https://docs.python.org/3/library/xml.etree.elementtree.html#xml.etree.ElementTree.iterparse) and [lxml iterparse](https://lxml.de/3.2/parsing.html#iterparse-and-iterwalk).
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} | 3 | 2021-02-01T02:09:44Z | 2021-02-02T15:10:15Z | 2021-02-02T13:35:29Z | 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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] | closed | false | null | [] | null | 3 | 2021-02-01T06:16:56Z | 2021-02-03T09:04:08Z | 2021-02-03T09:04:08Z | NONE | null | #### Is your feature request related to a problem?
I wish pandas can use nrows eagerly while processing xlsx or xlsb files
https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_pyxlsb.py#L73
If we see this code, this tries to process all the sheetdata and later apply nrows, which is very very slow in large files..in-fact in one of the xlsb files, the number of records are around 3k but, it was trying to loop around 100k records (because of xlsb metadata or something) even though I specify nrows as 3k, it has to wait processing all 100k records.
#### Describe the solution you'd like
If we can pass nrows to get_sheet_data and break the loop while it reaches the nrows number, then it will better.
#### API breaking implications
I am not really sure.
#### Describe alternatives you've considered
don't have alternatives at the moment, would like to hear suggestions about this.
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
df = pd.read_excel("sample.xlsb", nrows=100, engine="pyxlsb")
````
```python
def get_sheet_data(self, sheet, convert_float: bool, nrows: int) -> List[List[Scalar]]:
res = []
for index, r in enumerate(sheet.rows(sparse=False)):
res.append([self._convert_cell(c, convert_float) for c in r])
if index > nrows:
break
return res
```
We can set default value for nrows=infinity, incase if we dont want to break for others.
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