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MDExOlB1bGxSZXF1ZXN0NDA2OTU5MDA3
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BUG: DTA/TDA/PA setitem incorrectly allowing i8
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| 3
| 2020-04-21T23:26:39Z
| 2020-05-04T20:03:22Z
| 2020-04-30T13:44:37Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DEP: bump numpy min version?
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| 5
| 2020-04-21T23:52:44Z
| 2020-05-01T19:38:39Z
| 2020-05-01T19:38:39Z
|
MEMBER
| null |
We currently require 1.13.3 which was released 2017-09-29. The last bump was in 0.25.0 (#25554).
I think its time for another bump. The release dates for the 1.x.0 and 1.x.MAX versions out there:
1.14.0 -> 2018-01-06
1.14.6 -> 2018-09-23
1.15.0 -> 2018-07-23
1.15.4 -> 2018-11-04
1.16.0 -> 2019-01-13
1.16.6 -> 2019-12-29
1.17.0 -> 2019-07-06
1.17.5 -> 2020-01-01
1.15.4 looks not-unreasonable to me.
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MDU6SXNzdWU2MDQ2NTE3NDk=
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BUG: idxmin() fails for nullable integer data type (Int64)
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| 2
| 2020-04-22T10:41:20Z
| 2021-01-01T21:38:39Z
| 2021-01-01T21:38:39Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
s = pd.Series([1, 2, 3, 4], dtype="Int64")
s.idxmax()
```
#### Problem description
Currently, the `idxmax` call fails with
```python-traceback
File "~lib/python3.8/site-packages/pandas/core/series.py", line 2110, in idxmax
i = nanops.nanargmax(com.values_from_object(self), skipna=skipna)
File "~lib/python3.8/site-packages/pandas/core/nanops.py", line 64, in _f
raise TypeError(
TypeError: reduction operation 'argmax' not allowed for this dtype
```
being raised. This may be a duplicate of #32749, although the focus there seems to be generating the exception I already encountered.
#### Expected Output
Should be `3`, I guess.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13-arch2-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : None
None
</details>
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ENH: Control the escape scope of read_csv's escapechar
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| null |
NONE
| null |
#### Is your feature request related to a problem?
So far, the `escapechar` (`\` by default) of `read_csv` method is used to escape the special meaning of all other characters for all columns.
#### Describe the solution you'd like
In my situation, I want the escape char to take effect only for quoted values, while it can be interpreted as normal character in some other non-quoted columns, like the following example:
```csv
..., "This is \"quoted\"", C:\users\gary, bad value\, ...
```
The listed values are expected to be interpreted as `This is "quoted"`, `C:\users\gary` and `bad value\`. But now, the `read_csv` method will interpret all columns, resulting in `This is "quoted"` and `C:usersgary` (`\` is removed), and the separater `,` after the `bad value\` will be render out by the escapechar `\` at the end of `bad value\`, making the `bad value` concatenated with the next column's value, which is not right.
Therefore, it would be better to add an option to determine whether `escape_all` or `escape_in_quote`.
#### API breaking implications
Adding an option will not affect the default behavior of `read_csv` method.
#### Describe alternatives you've considered
Adding an option `escape_scope='escape_all' | 'escape_in_quote'`, with `escape_all` as default option.
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BUG: Exceptions not propagated from c_is_list_like in lib.pyx
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| 2020-04-22T12:59:57Z
| 2020-04-27T18:58:07Z
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CONTRIBUTOR
| null |
Python exceptions originating in `c_is_list_like()` are not propagated as the function doesn't declare an exception value (`except -1`).
https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/lib.pyx#L988
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MDExOlB1bGxSZXF1ZXN0NDA3Mjk5OTEz
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TYP: add types to DataFrame._get_agg_axis
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CONTRIBUTOR
| null |
Minor followup to #33610.
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BUG: Propagate Python exceptions from c_is_list_like (#33721)
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| 2020-04-22T13:45:47Z
| 2020-04-27T18:58:11Z
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|
CONTRIBUTOR
| null |
- [x] closes #33721
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: misc cleanups from LGTM.com
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BUG: argument 'axis=1' is silently ignored in DataFrame diff() method when dtype is nullable integer (Int64)
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NONE
| null |
```python
import numpy as np
import pandas as pd
pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.8
numba : None
```python
df = pd.DataFrame([[1,2], [3,np.nan]], index= ['a', 'b'], columns=['A', 'B']).convert_dtypes()
df
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>a</th>
<td>1</td>
<td>2</td>
</tr>
<tr>
<th>b</th>
<td>3</td>
<td><NA></td>
</tr>
</tbody>
</table>
</div>
```python
df.dtypes
```
A Int64
B Int64
dtype: object
```python
df.diff(axis=0)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>a</th>
<td><NA></td>
<td><NA></td>
</tr>
<tr>
<th>b</th>
<td>2</td>
<td><NA></td>
</tr>
</tbody>
</table>
</div>
```python
df.diff(axis=1)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>a</th>
<td><NA></td>
<td><NA></td>
</tr>
<tr>
<th>b</th>
<td>2</td>
<td><NA></td>
</tr>
</tbody>
</table>
</div>
Conversion to float fixes the issue
```python
df = df.astype(float)
df
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>a</th>
<td>1.0</td>
<td>2.0</td>
</tr>
<tr>
<th>b</th>
<td>3.0</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
```python
df.diff(axis=1)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th>a</th>
<td>NaN</td>
<td>1.0</td>
</tr>
<tr>
<th>b</th>
<td>NaN</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
```python
```
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REF: avoid passing SingleBlockManager to Series
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| 3
| 2020-04-22T18:14:00Z
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MEMBER
| null |
cc @jorisvandenbossche IIRC you didnt want to disallow SingleBlockManager because geopandas passes it. In those cases, does it also pass `fastpath=True`? If so, we can consider deprecating allowing SingleBlockManager in the non-fastpath case
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TST: pd.concat on two (or more) series produces all-NaN dataframe
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MEMBER
| null |
- [ ] closes #11058
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BLD: bump numpy min version to 1.15.4
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| 2020-04-22T20:31:06Z
| 2020-06-09T14:44:08Z
| 2020-05-01T19:38:39Z
|
MEMBER
| null |
- [x] closes #33718
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU2MDUwNjI2MjI=
| 33,730
|
DOC: add hvplot as option to plotting.backend
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| 2020-04-22T20:45:22Z
| 2020-11-17T22:37:28Z
| 2020-11-17T22:37:28Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
Couple of sections here
https://pandas.pydata.org/pandas-docs/stable/ecosystem.html#visualization
https://dev.pandas.io/docs/development/extending.html#plotting-backends
#### Documentation problem
Add information that [`hvplot`](https://hvplot.holoviz.org/) can be used as a backend
`pd.set_option("plotting.backend", "hvplot")`
#### Suggested fix for documentation
Not sure will this could go. Could add a hvplot section to https://github.com/pandas-dev/pandas/blob/master/doc/source/ecosystem.rst#visualization but perhaps want to make an explicit list of all the plotting backend options at https://dev.pandas.io/docs/development/extending.html#plotting-backends
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MDU6SXNzdWU2MDUwNzIzMTM=
| 33,731
|
API: deprecate silently casting np.nan to NaT
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| 2020-04-22T21:01:03Z
| 2020-04-23T15:32:42Z
| 2020-04-23T15:32:41Z
|
MEMBER
| null |
When doing a setitem into a dt64/td64 Series, we cast `np.nan` to `NaT`. The alternative would be to cast to object dtype and set np.nan unchanged.
Similarly anything that ends up calling `is_valid_nat_for_dtype` will allow through np.nan which ends up getting cast to NaT.
We should consider deprecating this behavior and requiring users to pass NaT directly.
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MDU6SXNzdWU2MDUwOTgwMjM=
| 33,732
|
BUG: groupby ignores sort=False when observed=True breaking transform
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| 2020-04-22T21:51:41Z
| 2020-08-21T17:06:04Z
| 2020-04-24T14:50:29Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
Maybe related to but distinct from https://github.com/pandas-dev/pandas/issues/27369
- [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
df = pd.DataFrame({'cat_col':pd.Categorical(['a','c','a','b']),'other_col':['q','w','w','q'],'val':[1,2,3,4]})
df.groupby(['cat_col','other_col'],observed=False,sort=False)['val'].transform(max)
```
returns
```
0 1.0
1 3.0
2 NaN
3 2.0
Name: val, dtype: float64
```
whereas
```python
df.groupby(['cat_col','other_col'],observed=True,sort=False)['val'].transform(max)
```
returns
```
0 1
1 2
2 3
3 4
Name: val, dtype: int64
```
What is going on is more clear when using `.agg()`
```python
df.groupby(['cat_col','other_col'],observed=False,sort=False)['val'].agg(max)
```
returns
```
a q 1.0
w 3.0
c q NaN
w 2.0
b q 4.0
w NaN
```
whereas
```python
df.groupby(['cat_col','other_col'],observed=True,sort=False)['val'].agg(max)
```
returns
```
a q 1
c w 2
a w 3
b q 4
Name: val, dtype: int64
```
#### Problem description
The `sort=False` is not respected when `observed=False`. It appears `transform()` assumes the original order still holds and maps the results to the shape of the original dataframe.
This is an issue when using `.transform()` to create a new column in the original (unsorted) dataframe as the rows will not be correctly associated.
#### Expected Output
`transform()` should give the same result whether `observed=True` or `observed=False` in the `groupby()`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.1
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDA3NTcxMzU0
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CLN: Remove unused assignment
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REF: implement test_astype
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| 2020-04-25T21:02:48Z
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MEMBER
| null |
Split/parametrized in some places.
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MDU6SXNzdWU2MDUxODQ2MjY=
| 33,735
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BUG: escapechar option of to_csv method does not function properly.
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[] | null | 0
| 2020-04-23T02:04:37Z
| 2020-06-06T02:53:07Z
| 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
t = pd.DataFrame({0:['"key":"value"'], 1:['mno,'], 2:['abc\\'], 3:['ijk']})
print(t)
# 0 1 2 3
#0 "key":"value" mno, abc\ ijk
t.to_csv('test.csv', header=None, escapechar='\\')
# cat test.csv
# 0,"""key"":""value""","mno,","abc\",ijk
t = pd.read_csv('test.csv', header=None, escapechar='\\')
```
#### Problem description
The `read_csv` cannot parse the file, and produce an `ParseError`
```sh
....ParserError: Error tokenizing data. C error: EOF inside string starting at row 0
```
This is because the third column with value `"abc\"`, in which the `\` is a normal character in the string, is not handled properly. With `escapechar='\\'` set for `to_csv`, the value `abc\` is quoted in the output `test.csv` file as:
```
0,"""key"":""value""","mno,","abc\",ijk
```
As a result the escapechar can be parsed as normal character if we read it by using `read_csv` without `escapechar='\\'` . **However, this is not compatible with `read_csv` with `escapechar='\\'` , as it will render the `\"` as a single normal character `"`, thus the quoting pair of this column is missing the end quote char, and hence the above error occurs.**
#### Expected Output
When writing file with`to_csv`, the `\` specified as escapechar should be rendered as `\\` in the output file, instead of quote the whole string with quotechar `"..."`. In this way, the meaning of `escapechar` option in `to_csv` and `read_csv` would be consistent, i.e., if we write with `escapechar` , the we read with `escapechar` ( otherwise the `\\` is read as two normal characters, without error occuring).
#### Output of ``pd.show_versions()``
<details>
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDA3NjQ4OTcx
| 33,736
|
REF: use _wrap_joined_index in PeriodIndex.join
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| 0
| 2020-04-23T02:51:33Z
| 2020-04-23T17:54:22Z
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|
MEMBER
| null |
PeriodIndex defines _apply_meta, but that is made unnecessary by implementing _wrap_joined_index correctly.
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MDExOlB1bGxSZXF1ZXN0NDA3NjUwODk4
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TST: fix tests for asserting matching freq
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| 1
| 2020-04-23T02:59:53Z
| 2020-04-23T18:56:34Z
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|
MEMBER
| null |
xref #33711, #33712
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ENH: rejected support for Python data structures containing Pandas DataFrames/Series
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[] | null | 2
| 2020-04-23T03:01:14Z
| 2020-08-21T17:06:04Z
| 2020-04-23T03:21:28Z
|
NONE
| null |
It is great that you have implemented to_csv and read_csv() to export and import tables in CSV format. It would be even better to be able to serialize/deserialize Python data structures such as list and dict containing DataFrames/Series. For example, something like [this](https://github.com/xuancong84/pandas-serializer).
Would that be possible? Thanks!
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| 605,222,487
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MDExOlB1bGxSZXF1ZXN0NDA3NjY3MDc1
| 33,739
|
PERF: fix #32976 slow group by for categorical columns
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| 2020-11-06T01:53:02Z
| 2020-11-06T01:53:02Z
|
CONTRIBUTOR
| null |
Aggregate categorical codes with fast cython aggregation for select `how` operations. Added new ASV benchmark copied from 32976 indicating > 99% improvement in performance for this case.
- [x] closes #32976
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU2MDUyNjgzMTk=
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BUG: pandas merge suffixes accepting `set` can interchange right and left suffix order
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| 2020-04-23T06:14:49Z
| 2020-06-08T01:36:52Z
| 2020-06-08T01:36:52Z
|
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.
---
```python
df_b = pd.DataFrame(dict(p=[1,2,3], q=[2,3,4]))
df_a = pd.DataFrame(dict(p=[2,3,4], q=[1,2,3]))
pd.merge(left=df_b, right=df_a, on=['p'], how='outer', suffixes={'_b', '_a'})
```
#### Current output:
| p | q_a | q_b
-- | -- | -- | --
0 | 1 | 2.0 | NaN
1 | 2 | 3.0 | 1.0
2 | 3 | 4.0 | 2.0
3 | 4 | NaN | 3.0
#### Expected Output:
| p | q_b | q_a
-- | -- | -- | --
0 |1 | 2.0 | NaN
1 | 2 | 3.0 | 1.0
2 | 3 | 4.0 | 2.0
3 | 4 | NaN | 3.0
#### Problem description
This [line](https://github.com/pandas-dev/pandas/blob/837daf18d480cce18c25844c591c39da19437252/pandas/core/reshape/merge.py#L670
) is causing the issue:
```
lsuf, rsuf = self.suffixes
```
When you unpack set it returns elements in sorted order:
```
l, r = (2, 1); print(l, r) # l=2, r=1
l, r = [2, 1]; print(l, r) # l=2, r=1
l, r = {2, 1}; print(l, r) # l=1, r=2
```
#### Output of ``pd.show_versions()``
<details>
[INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.165-133.209.amzn2.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
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 : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None]
</details>
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MDU6SXNzdWU2MDUyODI3NDc=
| 33,741
|
BUG: DatetimeIndex.get_indexer wrong result on mixed-type argument
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| 3
| 2020-04-23T06:45:30Z
| 2020-06-01T00:28:41Z
| 2020-06-01T00:28:41Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import datetime
import pandas as pd
dates = pd.bdate_range('2020-04-20', '2020-04-25')
print(dates.get_indexer([datetime.date(2020, 4, 20), datetime.date(2020, 4, 21)]))
# [0, 1]
print(dates.get_indexer([pd.Timestamp('2020-04-20'), datetime.date(2020, 4, 21)]))
# [0, -1]
```
#### Problem description
As a general rule I would expect that `index.get_indexer(as + bs) == np.concat([index.get_indexer(as), index.get_indexer(bs)])` for any lists `as` and `bs`, i.e. `index.get_indexer` operates elementwise. However in this case the type of a previous argument is surprisingly affecting the index that is assigned to a later one.
#### Expected Output
`[0, 1]` in both cases
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.936
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
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
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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DOC:
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NONE
| null |
I am not able to read a CSV file which is uploaded on Github itself. I want pandas to read the file and provide me the data frame. But I am getting a Parse Error. The Error is as:
df=pd.read_csv("https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
ParserError Traceback (most recent call last)
<ipython-input-8-91ca12bc272f> in <module>
----> 1 df=pd.read_csv("https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
~\Anaconda3\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
700 skip_blank_lines=skip_blank_lines)
701
--> 702 return _read(filepath_or_buffer, kwds)
703
704 parser_f.__name__ = name
~\Anaconda3\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
433
434 try:
--> 435 data = parser.read(nrows)
436 finally:
437 parser.close()
~\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1137 def read(self, nrows=None):
1138 nrows = _validate_integer('nrows', nrows)
-> 1139 ret = self._engine.read(nrows)
1140
1141 # May alter columns / col_dict
~\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
1993 def read(self, nrows=None):
1994 try:
-> 1995 data = self._reader.read(nrows)
1996 except StopIteration:
1997 if self._first_chunk:
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()
pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()
ParserError: Error tokenizing data. C error: Expected 1 fields in line 33, saw 2
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| 605,320,523
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MDU6SXNzdWU2MDUzMjA1MjM=
| 33,743
|
pandas boxplot props not working
|
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[] | null | 2
| 2020-04-23T07:54:18Z
| 2020-05-13T06:07:33Z
| 2020-05-13T06:07:33Z
|
NONE
| null |
I cannot change some of the plotting kwargs in `pd.DataFrame.boxplot`. I replot the same in matplotlib with almost the same code and I get the expected results.
Here's a minimum working example:
```
np.random.seed(1234)
df = pd.DataFrame(np.random.randn(10, 4),
columns=['Col1', 'Col2', 'Col3', 'Col4'])
df.boxplot(column=['Col1', 'Col2', 'Col3'], showmeans=True,
meanprops={'markerfacecolor': 'r'},
boxprops={'linewidth':5,'color': 'r'}
)
fig2, ax2 = plt.subplots()
ax2.boxplot([df[c] for c in df.columns],showmeans=True,
meanprops={'markerfacecolor': 'r'},
boxprops={'linewidth':5,'color': 'r'})
```
The resulting plots are different. While the boxes are rendered in the correct color using `ax2.boxplot`, the `df.boxplot` version still has blue boxes (though thicker, which means `linewidth` works here).
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MDU6SXNzdWU2MDUzNjE2OTM=
| 33,744
|
pandas statistics
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[] | null | 1
| 2020-04-23T08:58:35Z
| 2020-08-21T17:06:04Z
| 2020-04-23T08:59:00Z
|
CONTRIBUTOR
| null |
Just a small heads-up of how well pandas is doing. I wrote [hotware](https://github.com/nschloe/hotware) to check the popularity of software on GitHub and StackOverflow, and in the scientific Python department, pandas is clearly number 1:
https://github.com/nschloe/hotware#scientific-python
You might want to use this in your next funding proposal. :smile_cat:
Congrats on a great piece of software!
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BUG: support skew function for custom BaseIndexer rolling windows
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| 9
| 2020-04-23T09:53:20Z
| 2020-04-26T06:01:34Z
| 2020-04-25T22:03:14Z
|
MEMBER
| null |
- [X] xref #32865
- [X] 1 tests added / 1 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
## Scope of PR
This PR does a couple things to fix the performance of `skew`, and this also fixes the behavior of all the functions that have `require_min_periods` for small values of `min_periods`:
* clarify docs. Name all sample methods uniformly, add a note to the caveat that users should in general be careful about using sample methods with windows
* fix bug in `_apply` that made us never go into the `BaseIndexer` control flow branch
* fix bug in `_apply`: pass `window_indexer.window_size` into `calc_min_periods` instead of `min_periods or 1`. We want the custom indexer window size there, so it's not clear to me why we were passing `min_periods or 1` .
## Details
The algorithm itself is robust, but it defaults to sample skewness, which is why there was a difference between its output and `numpy`. To prevent misunderstandings, I clarified the docs a bit.
We were also passing a wrong value to `calc_min_periods`, and we weren't going into the proper if branch, because we were checking the type of `window` instead of `self.window`.
## Background on the wider issue
We currently don't support several rolling window functions when building a rolling window object using a custom class descended from `pandas.api.indexers.Baseindexer`. The implementations were written with backward-looking windows in mind, and this led to these functions breaking.
Currently, using these functions returns a `NotImplemented` error thanks to #33057, but ideally we want to update the implementations, so that they will work without a performance hit. This is what I aim to do over a series of PRs.
## Perf notes
No changes to algorithms.
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| 33,746
|
BUG: ExcelWriter: file is corrupted on save (and: does it accept a file object?)
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open
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[] | null | 5
| 2020-04-23T11:25:05Z
| 2021-08-13T16:32:25Z
| 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
`first.py`:
```python
import pandas as pd
import openpyxl
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
writer1 = pd.ExcelWriter('first.xlsx', engine='openpyxl')
df.to_excel(writer1)
writer1.save()
writer1.close()
```
`second.py`:
```python
import pandas as pd
import openpyxl
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
output2 = open('second.xlsx', 'wb')
writer2 = pd.ExcelWriter(output2, engine='openpyxl')
df.to_excel(writer2)
writer2.save()
writer2.close()
output2.flush()
output2.close()
```
**UPD 2**:
```python
writer2.save()
writer2.close()
```
Remove one line. It will start to work!
**UPD:** `third.py` (see https://github.com/pandas-dev/pandas/issues/33746#issuecomment-640169769 by @lordgrenville):
```python
import pandas as pd
import openpyxl
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
with open('third.xlsx', 'wb') as output3:
writer3 = pd.ExcelWriter(output3, engine='openpyxl')
df.to_excel(writer3)
writer3.save()
```
#### Problem description
```
$ python first.py
$ python second.py
$ du -b first.xlsx second.xlsx third.xlsx
4737 first.xlsx
9474 second.xlsx
4737 third.xlsx
```
~(Note: 9474 = 2 * 4737. But sometimes it's not true.)~
1. Why files differ?
2. Documentation [says](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.ExcelWriter.html): `path - str - Path to xls or xlsx file.` So does `pd.ExcelWriter.__init__` accept a file-like object?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.32-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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| 33,747
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CI not running linux / windows builds
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| 2
| 2020-04-23T14:03:19Z
| 2020-04-23T17:09:17Z
| 2020-04-23T17:09:17Z
|
CONTRIBUTOR
| null |
cc @pandas-dev/pandas-core
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MDU6SXNzdWU2MDU2MzQ1Mjc=
| 33,748
|
BUG: to_hdf and HDFStore raise KeyError for DataFrame subclasses
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| 4
| 2020-04-23T15:30:32Z
| 2020-12-19T02:19:00Z
| 2020-12-19T02:19:00Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
class SubDataFrame(pd.DataFrame):
@property
def _constructor(self):
return SubDataFrame
# fails with KeyError
sdf = SubDataFrame({'a':[1,2], 'b':[3,4]})
sdf.to_hdf('test.h5', 'test')
with pd.HDFStore('test.h5') as store:
store.put('test', sdf)
```
#### Problem description
``to_hdf()`` and ``HDFStore.put()`` fail for DataFrame subclasses.
This happens because in ``pandas/io/pytables.py`` in ``_create_storer`` line 1578 (or thereabouts), the ``_TYPE_MAP`` is accessed by ``type()``, whereas upon ``_create_storer`` entry, the check is done using ``isinstance()``.
#### Expected Output
The check upon entry of ``_create_storer`` should at least be consistent with the way ``_TYPE_MAP`` is accessed. So if the choice is not to support writing DataFrame subclasses to HDF, instead of a ``KeyError``, a ``TypeError("value must be None, Series, or DataFrame")`` should be raised.
But ideally, storing subclasses of DataFrame to HDF should be supported.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.111-1.el7.centos.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : 0.4.0
scipy : 1.2.1
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDA4MDU5NDk4
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|
BUG: Fix mixed datetime dtype inference
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| 4
| 2020-04-23T17:16:03Z
| 2020-06-01T00:29:55Z
| 2020-06-01T00:28:41Z
|
MEMBER
| null |
- [x] closes #33741
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDA4MDYwNzg0
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|
CLN: Remove is_null_period
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|
MEMBER
| null |
is_null_period seems only to be an alias for checknull_with_nat so I think it can be removed?
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TST: pd.NA TypeError in drop_duplicates with object dtype
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| 2020-04-25T09:14:57Z
| 2020-04-24T22:25:24Z
|
MEMBER
| null |
- [ ] closes #32992
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: weekday property raises TypeError in groupby method
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| 2021-07-31T04:38:09Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
n = 1000
s = pd.Series(range(n), index=pd.date_range('2020-01-01', periods=n, freq='H'))
s.groupby(lambda x: (x.weekday, x.hour)).mean()
```
#### Problem description
Used in the groupby method as a function argument in combination with another index time attribute `weekday` raises a TypeError whereas `dayofweek` works although they are both aliases for each other.
#### Expected Output
I do expect the same output as with `
s.groupby(lambda x: (x.dayofweek, x.hour)).mean()`
since it works as a single argument:
`s.groupby(lambda x: x.dayofweek).mean().equals(s.groupby(lambda x: x.weekday).mean())`
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-96-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
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 : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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CLN: Parametrize dtype inference tests
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ENH: .to_rpy2 method for DataFrame and Series
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| 2020-04-23T22:16:29Z
| 2020-08-21T17:06:05Z
| 2020-04-25T06:02:25Z
|
NONE
| null |
#### Is your feature request related to a problem?
https://rpy2.github.io/doc/v3.0.x/html/generated_rst/pandas.html
I've been using rpy2 a lot and considering that pandas was inspired from R it would be nice to have a .to_rpy2 method for seemless conversion.
#### Describe the solution you'd like
```
df = pd.DataFrame(data)
r_df = df.to_rpy2()
```
#### API breaking implications
It shouldn't affect the API at all. If `rpy2` isn't installed then throw an error just like writing excel files needs another package.
#### Describe alternatives you've considered
I made wrappers that I need to use.
#### Additional context
Current method with `rpy2` requires some boilerplate and is less seamless:
Here's my current hack:
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CI: Revert Cython Pin (take 2)
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| 2
| 2020-04-23T22:49:55Z
| 2020-04-27T23:47:08Z
| 2020-04-27T23:10:34Z
|
CONTRIBUTOR
| null |
- [x] closes #33507
reverts #33534
New Cython pre release available today - https://pypi.org/project/Cython/3.0a2/
cc @WillAyd
Update: 3.0a2 (no such luck - seems there is a regression which is being looked at)
https://mail.python.org/pipermail/cython-devel/2020-April/005340.html
https://github.com/cython/cython/issues/3544
Update now trying 3.0a3
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| 605,963,140
|
MDU6SXNzdWU2MDU5NjMxNDA=
| 33,756
|
BUG: DataFrame.truncate() returns incorrect data if the index is sorted in descending order
|
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| 0
| 2020-04-24T00:52:47Z
| 2020-04-26T21:04:16Z
| 2020-04-26T21:04:16Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas (1.0.3)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(index=range(0, 5), data={'A': 1})
df.sort_index(ascending=False, inplace=True)
df.truncate(before=3)
```
#### Problem description
When the index is sorted in descending order, the truncate() function behaves incorrectly. In the example above, it should truncate all rows whose index is < 3. Instead it is truncating all rows whose index is > 3.
Similarly when using after.
#### Output
```python
>>> df
A
4 1
3 1
2 1
1 1
0 1
>>> df.truncate(before=3)
A
3 1
2 1
1 1
0 1
```
#### Expected Output
```python
>>> df.truncate(before=3)
A
4 1
3 1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDA4Mjk2MTY3
| 33,757
|
BUG: MonthOffset.name
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| 1
| 2020-04-24T02:38:17Z
| 2020-04-25T00:39:36Z
| 2020-04-25T00:30:01Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
`is_anchored` is being accessed indirectly (its not a property). As a result we never go down the other path, which would raise AttributeError since there is no `code_rule`.
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| 606,170,039
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MDExOlB1bGxSZXF1ZXN0NDA4NDMzNzk4
| 33,758
|
REGR: disallow mean of period column again
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| 7
| 2020-04-24T09:20:59Z
| 2020-04-25T11:46:15Z
| 2020-04-24T21:35:45Z
|
MEMBER
| null |
This reverts parts of https://github.com/pandas-dev/pandas/pull/32426 and https://github.com/pandas-dev/pandas/pull/29941 (those are only on master, not yet released), to disallow taking the mean on a Period dtype column again. The mean for period is not supported on PeriodArray itself or for a Series of period dtype (on purpose, see discussion in https://github.com/pandas-dev/pandas/pull/24757 when mean for datetimelike was added), so for DataFrame columns it should also not work.
See also comment at https://github.com/pandas-dev/pandas/pull/32426#issuecomment-618872954
cc @jbrockmendel
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|
Can we reduce the size of file generated with empty_df.to_hdf() below 2MB?
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| 10
| 2020-04-24T09:25:19Z
| 2021-08-18T16:39:56Z
| 2021-08-18T14:07:49Z
|
NONE
| null |
I store my dataframes on disk with `df.to_hdf()`, but to my surprise, even for an empty dataframe the resulting file size is 2MB. Is there a way to make it smaller? Thanks!
```python
import pandas as pd
empty_df = pd.DataFrame()
empty_df.to_hdf('empty_df.hdf', 'data')
```
NB: Versions tested: Pandas 0.21.1, Pandas 1.03.
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BUG: inconsistent median support for datetimelike dtypes
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| 1
| 2020-04-24T09:30:01Z
| 2020-10-07T03:28:11Z
| 2020-10-07T03:28:11Z
|
MEMBER
| null |
https://github.com/pandas-dev/pandas/pull/29941 introduced support for taking the median of a datetime-like column.
In released version, (pandas 1.0.3) we get:
```
In [20]: df = pd.DataFrame({'a': pd.date_range("2012", periods=3, freq='D')})
In [21]: df.median()
Out[21]: Series([], dtype: float64)
In [22]: df.median(numeric_only=False)
...
TypeError: reduction operation 'median' not allowed for this dtype
In [23]: df['a'].median()
...
TypeError: DatetimeIndex cannot perform the operation median
```
on master we now allow this for DataFrame (warning it will be in the future by default, and already allow it with numeric_only=False):
```
In [2]: df.median()
/home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64, datetime64tz, and PeriodDtype columns in a future version.
#!/home/joris/miniconda3/envs/dev/bin/python
Out[2]: Series([], dtype: float64)
In [3]: df.median(numeric_only=False)
Out[3]:
a 2012-01-02 00:00:00
dtype: object
```
but for a single column it still fails:
```
In [4]: df['a'].median()
...
TypeError: cannot perform median with type datetime64[ns]
```
So we should make this support consistent (add "median" support on DatetimeArray, so it also works for Series)
cc @jbrockmendel
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REGR: fix DataFrame reduction with EA columns and numeric_only=True
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| 8
| 2020-04-24T09:39:35Z
| 2020-05-26T09:36:02Z
| 2020-05-01T18:19:12Z
|
MEMBER
| null |
Closes #33256
For the block-wise path in `DataFrame._reduce`, we ensure to use the EA reduction for blocks holding EAs, instead of passing the EA to the `op` function (which is typically the `nanops` nan function).
This needs https://github.com/pandas-dev/pandas/pull/33758 to be merged, since that PR fixes the test that is failing here.
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|
Stratified sampling
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|
NONE
| null |
- [ ] Added new method stratified_sample
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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REF: simplify _try_cast
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Part of a sequence of PRs aimed at sharing code between Series/Index/array
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BUG: RecursionError when aligning column MultiIndex
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| 2
| 2020-04-24T16:07:03Z
| 2021-04-29T23:34:45Z
| 2021-04-29T23:34:45Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
When trying to do arithmetic on two dataframes with a common MultiIndex level, I am having a max recursion error.
Please note that if using string column names (ie: labels) instead of ints, I have no recursion error but still a buggy NaN result.
```python
df1 = pd.DataFrame([[1, 2, 3]], columns=['Level1', 'Level2', 'Value'])
df1 = df1.set_index(['Level1', 'Level2']).T
df1
```
```python
df2 = pd.DataFrame([[1, 3]], columns=['Level1', 'Value'])
df2 = df2.set_index(['Level1']).T
df2
```
```python
df1 - df2
```
#### RecursionError on atlest pandas 1.0.2 and 1.0.3
#### Expected Output, working fine on pandas 1.0.1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
BUG: read_json returns wrong value for big integer
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| 1
| 2020-04-24T16:16:55Z
| 2020-04-24T16:55:42Z
| 2020-04-24T16:55:12Z
|
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.
---
#### Code Sample, a copy-pastable example
```bash
$ cat first-line.json
{"col":"72057594038135102"}
```
```python
>>> pd.read_json("first-line.json", lines=True)
col
0 72057594038135104
>>> pd.read_json("first-line.json", lines=True, dtype=False)
col
0 72057594038135102
```
#### Problem description
`pd.read_json()` silently returns a wrong value when reading a big integer. This creates a problem when the integers are used as unique IDs.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0
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 : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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MDExOlB1bGxSZXF1ZXN0NDA4NjQ1MDE5
| 33,767
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BUG: Fix missing tick labels on twinned axes
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| 14
| 2020-04-24T16:20:26Z
| 2020-09-30T07:30:48Z
| 2020-09-30T00:11:53Z
|
CONTRIBUTOR
| null |
## Background:
Multi-row and/or multi-column subplots can utilize shared axes.
An external share happens at axis creation when a sharex or sharey
parameter is specified.
An internal share, or twinning, occurs when an overlayed axis is created
by the Axes.twinx() or Axes.twiny() calls.
The two types of sharing can be distinguished after the fact in the
following manner. If two axes sharing an axis also have the same
position, they are not in an external axis share, they are twinned.
For externally shared axes Pandas automatically removes tick labels for
all but the last row and/or first column in
./pandas/plotting/_matplotlib/tools.py's function _handle_shared_axes().
## The problem:
_handle_shared_axes() should be interested in externally shared axes,
whether or not they are also twinned. It should, but doesn't, ignore
axes which are only twinned. Which means that twinned-only axes wrongly
lose their tick labels.
## The cure:
This commit introduces _has_externally_shared_axis() which identifies
externally shared axes and uses it to expand upon the existing use of
len(Axes.get_shared_{x,y}_axes().get_siblings(a{x,y})) in
_handle_shared_axes() which miss these cases.
## The demonstration test case:
Note especially the axis labels (and associated tick labels).
```python
#!/usr/bin/python3
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Create data
df = pd.DataFrame({'a': np.random.randn(1000),
'b': np.random.randn(1000)})
# Create figure
fig = plt.figure()
plots = fig.subplots(2, 3)
# Create *externally* shared axes
plots[0][0] = plt.subplot(231, sharex=plots[1][0])
# note: no plots[0][1] that's the twin only case
plots[0][2] = plt.subplot(233, sharex=plots[1][2])
# Create *internally* shared axes
# note: no plots[0][0] that's the external only case
twin_ax1 = plots[0][1].twinx()
twin_ax2 = plots[0][2].twinx()
# Plot data to primary axes
df['a'].plot(ax=plots[0][0], title="External share only").set_xlabel("this label should never be visible")
df['a'].plot(ax=plots[1][0])
df['a'].plot(ax=plots[0][1], title="Internal share (twin) only").set_xlabel("this label should always be visible")
df['a'].plot(ax=plots[1][1])
df['a'].plot(ax=plots[0][2], title="Both").set_xlabel("this label should never be visible")
df['a'].plot(ax=plots[1][2])
# Plot data to twinned axes
df['b'].plot(ax=twin_ax1, color='green')
df['b'].plot(ax=twin_ax2, color='yellow')
# Do it
plt.show()
```
See images produced by this code for problem and fixed cases at the bug report: #33819.
- [x] closes #33819
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDA4NjUzNDAw
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DOC: data_columns can take bool
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| 1
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|
CONTRIBUTOR
| null |
The ``data_columns`` argument of ``df.to_hdf`` can take ``True`` as an argument, meaning "all columns", yet the type hint is ``Optional[List[str]]``, triggering warnings in e.g. pyCharm.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 3
| 2020-04-24T17:08:07Z
| 2020-04-26T21:47:37Z
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MEMBER
| null |
- [x] closes #33756
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 2020-04-24T17:13:15Z
| 2020-05-01T20:36:03Z
| 2020-05-01T20:36:03Z
|
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
Prerequisites:
```console
$ pip install pandas numpy memoized-property
$ pip install git+https://github.com/frreiss/text-extensions-for-pandas
```
Reproduce the problem, using [`TensorArray`](https://github.com/frreiss/text-extensions-for-pandas/blob/master/text_extensions_for_pandas/array/tensor.py), our extension type for storing tensors in a Pandas series:
```python
>>> import pandas as pd
>>> import numpy as np
>>> import text_extensions_for_pandas as tp
>>> # Integers work
int_tensors = np.array([[1, 2], [3, 4]])
int_tensor_series = pd.Series(tp.TensorArray(int_tensors))
int_tensor_series
0 [1 2]
1 [3 4]
dtype: TensorType
>>> # Boolean values don't work
bool_tensors = np.array([[True, False], [False, True]])
bool_tensor_series = pd.Series(tp.TensorArray(bool_tensors))
bool_tensor_series
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/pd/covid-notebooks/env/lib/python3.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
[...many lines of stack trace...]
~/pd/covid-notebooks/env/lib/python3.7/site-packages/pandas/io/formats/format.py in _format_strings(self)
1255 fmt_values = []
1256 for i, v in enumerate(vals):
-> 1257 if not is_float_type[i] and leading_space:
1258 fmt_values.append(" {v}".format(v=_format(v)))
1259 elif is_float_type[i]:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
#### Problem description
`Series.__repr__()` invokes `ExtensionArrayFormatter` to render extension types. If the individual elements managed by an ExtensionArray are numpy arrays (or slices of a larger numpy array), then `ExtensionArrayFormatter` uses Pandas' facilities for rendering numpy arrays. These facilities comprise the base class `GenericArrayFormatter` and subclasses such as `FloatArrayFormatter` for handling specific types (see `pandas/io/formats/format.py`). The formatters for numeric types can render numpy arrays with more than one dimension, but the base class `GenericArrayFormatter` cannot. The limitation appears to be an oversight. I will submit a small pull request with a fix in a few minutes.
#### Expected Output
The above code should output something like this:
```python
0 [True False]
1 [False True]
dtype: TensorType
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.49.0
</details>
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[#33770] bug fix to prevent ExtensionArrays from crashing Series.__repr__()
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| 2
| 2020-04-24T17:23:45Z
| 2020-05-01T20:36:04Z
| 2020-05-01T20:36:04Z
|
CONTRIBUTOR
| null |
- [X] closes #33770
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
This pull request fixes a bug in `GenericArrayFormatter` that caused a crash when rendering arrays of arrays. This problem causes `Series.__repr__()` to fail with certain extension types; see #33770 for more information.
The root cause is this line:
``` python
1231 is_float_type = lib.map_infer(vals, is_float) & notna(vals)
```
The call to `notna()` here returns a 2D array if `vals` is a 2D array, while `lib.map_infer(vals, is_float)` returns a 1D array if `vals` is a 2D array.
In addition to fixing that line, this PR also adds tests for the issue.
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BUG: DataFrame.truncate() handles decreasing order(#33756)
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TST: more accurate freq attr in `expected`s
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MEMBER
| null |
preparing to add a freq check to assert_index_equal
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REF: misplaced sort_index tests
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MDExOlB1bGxSZXF1ZXN0NDA4NzA3MDY1
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BUG: Changing way of applying numpy functions on Series
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| 2020-04-24T18:41:29Z
| 2020-06-09T22:49:54Z
| 2020-06-09T22:49:54Z
|
CONTRIBUTOR
| null |
- [x] closes #33492
- [x] tests added and passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] numpy functions on Series are called like other functions
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CLN: avoid getattr(obj, "values", obj)
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MEMBER
| null |
xref #27167
@mroeschke can you pls double-check me on the change in window.rolling
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ENH: stratified samplig as a DataFrame method
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| 4
| 2020-04-24T18:45:49Z
| 2020-04-25T11:50:19Z
| 2020-04-25T11:50:19Z
|
NONE
| null |
#### Is your feature request related to a problem?
Often times when conducting hypothesis tests we need to prepare the data and sample it using stratified sampling (i.e.: a sample that mimics the population distribution according to a variable or strata). Pandas has a sample feature, but it does not take strata into account today.
#### Describe the solution you'd like
I would like to propose a solution (in fact I have already pulled the pandas repo and developed it).
I wrote a stratified_sample method that does exactly that. Given a DataFrame columns, it performs a stratified sample. This is a method of the object DataFrame just as the "sample" method.
It returns a sampled DataFrame using proportionate stratification.
#### API breaking implications
I think that this simple method will not break the api since it just samples a DataFrame object.
#### Describe alternatives you've considered
The method I developed returns a sampled DataFrame. The parameters it takes are:
* *n*: sample size. If not provided it estimates a sample size using the Adjusted Cochran sampling formula.
* *strata*: a list containing columns to be used as strata
* *random_state*: for reproducibility
* *reset_index*: True or False
#### Additional context
https://en.wikipedia.org/wiki/Stratified_sampling
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BUG: Undocumented behaviour of MultiIndex.rename
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[] | null | 0
| 2020-04-24T19:58:49Z
| 2021-07-31T04:41:09Z
| null |
MEMBER
| 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 numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1349.g930a7f837'
>>>
>>> df = pd.DataFrame(
... [[1, 2]],
... columns=pd.MultiIndex.from_product([["a", "b"], ["c"]], names=["level0", "level1"]),
... )
>>> df
level0 a b
level1 c c
0 1 2
>>>
>>>
>>> df.columns.rename(["foo", "bar"], False)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\indexes\base.py", line 1248, in set_names
raise TypeError("Names must be a string when a single level is provided.")
TypeError: Names must be a string when a single level is provided.
>>>
```
#### Problem description
from docs
`Index.rename(self, name, inplace=False)`
Undocumented behaviour arising from
https://github.com/pandas-dev/pandas/blob/73fd8a797dc45ab1ced1c1712e04852d8d4a7d09/pandas/core/indexes/multi.py#L247
This allows passing a level to rename since MultiIndex.rename is an alias for MutliIndex.set_names
https://pandas.pydata.org/docs/dev/reference/api/pandas.Index.set_names.html
there is no documentation for MultiIndex.rename, the documentation for Index.rename includes examples for MutliIndex
so the second argument is interpreted as `level`, but from the docs should be `inplace`
https://pandas.pydata.org/docs/dev/reference/api/pandas.Index.rename.html
#### Expected Output
```
MultiIndex([('a', 'c'),
('b', 'c')],
names=['foo', 'bar'])
>>>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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BUG: freq not retained on apply_index
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- [ ] closes #xxxx
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API: Should indexing on columns give views or copies?
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MEMBER
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Migrating the discussion from #33597:
We can improve performance (and memory footprint) by returning views in more cases. The downsides are 1) the returned DataFrames are less-consolidated and 2) users may be relying on getting copies in some situations.
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TST: more specific freq attrs
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MEMBER
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Getting ready to check matching freq in assert_index_equal
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BUG: Add warning if rows have more columns than expected
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CONTRIBUTOR
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- [x] closes #33037
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| 2020-04-27T02:17:12Z
| 2020-04-27T02:16:26Z
|
CONTRIBUTOR
| null |
Crosstab function should set dropna=False to keep the categories which not appear in the data, but in the DOC, it seems to be inconsistent with the description. Two examples for comparison may be better for users to get this point.
https://pandas.pydata.org/docs/dev/user_guide/reshaping.html#cross-tabulations
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MDExOlB1bGxSZXF1ZXN0NDA4ODQ5MTEw
| 33,784
|
BUG, TST, DOC: fix core.missing._akima_interpolate will raise AttributeError
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| 2
| 2020-04-25T04:54:33Z
| 2020-04-26T19:57:42Z
| 2020-04-26T19:57:38Z
|
CONTRIBUTOR
| null |
- [x] closes #33426
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Wasn't sure if this warranted a whatsnew entry since its mostly code cleanup
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| 606,691,019
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MDU6SXNzdWU2MDY2OTEwMTk=
| 33,785
|
Pandas won't drop index after rename using said index
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| 2020-04-25T18:01:10Z
| 2020-04-25T18:00:49Z
|
NONE
| null |
So, there should probably be a 'drop' option as part of the rename function to drop said index if using it, but like the title says, if you rename using an index, you can't then use the drop function to drop said index (tested with xlsx and csv)
works for whatever index, 1, 2, 3, etc.
[testfile.xlsx](https://github.com/pandas-dev/pandas/files/4532369/testfile.xlsx)
```
dataframe = pandas.read_excel(ENVT_SETTINGS['working_dir'] + 'testfile.xlsx', engine='openpyxl', header=None)
print(dataframe.head(5))
dataframe.rename(columns = dataframe.iloc[0], inplace = True)
print(dataframe.head(5))
dataframe.drop(dataframe.index[0])
print(dataframe.head(5))
```
interestingly, you CAN drop it if you then recreate the dataframe like
` dataframe = dataframe[1:]`
to literally cut out that row from the dataset, but otherwise, it won't go away.
[testfile.xlsx](https://github.com/pandas-dev/pandas/files/4532379/testfile.xlsx)
[testresult.xlsx](https://github.com/pandas-dev/pandas/files/4532380/testresult.xlsx)
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MDU6SXNzdWU2MDY3NDk1OTM=
| 33,786
|
BUG: DataFrame with index having tzlocal() timezone could not be saved to parquet
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| false
| null |
[] | null | 2
| 2020-04-25T11:27:57Z
| 2020-04-25T12:40:20Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
If Dataframe has index which timezone is set to `dateutil.tz.tzlocal()`, it could not be saved to parquet.
It might be related to #24310
#### Code Sample, a copy-pastable example
```python
from dateutil.tz import tzlocal
ind = pd.date_range('2020-02-01','2020-04-14').tz_localize(tzlocal())
x = pd.DataFrame([[1,2]]*len(ind), index=ind, columns=['A','B'])
x.to_parquet('tmp.parquet')
```
#### Problem description
The code raises `ValueError: Unable to convert timezone "tzlocal()" to string`.
However saving to e.g. CSV works well:
```python
x.to_csv('tmp.csv')
```
#### 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 : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 42.0.1.post20191125
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.3
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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MDU6SXNzdWU2MDY3NTI3MDg=
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|
BUG: Pandas 1.0.x read_json unable to convert lists with date values
|
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| 2
| 2020-04-25T11:47:07Z
| 2021-08-05T12:02:33Z
| 2021-08-05T12:02:33Z
|
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.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# this works fine in 0.25.x and 1.0.x
json_line= pd.DataFrame([([1, 2], "hector")], columns=['accounts', 'name']).to_json(lines=True, orient='records')
data = pd.read_json(json_line)
# this works fine in 0.25.x and 1.0.x --- convert_dates = False (non-default behavior)
json_line= pd.DataFrame([([1, 2], ['2020-03-05', '2020-04-08T09:58:49+00:00'], "hector")], columns=['accounts', 'date', 'name']).to_json(lines=True, orient='records')
json_line
data = pd.read_json(json_line, convert_dates=False)
# this does not error in 0.25.x but errors in 1.0.x -- error list is unhashable
json_line= pd.DataFrame([([1, 2], ['2020-03-05', '2020-04-08T09:58:49+00:00'], "hector")], columns=['accounts', 'date', 'name']).to_json(lines=True, orient='records')
json_line
data = pd.read_json(json_line)
```
#### Problem description
In pandas 0.25.x (and below), pandas read_json(path, lines=True) was able to read newline-delimited json files with blobs with entries that contained lists of all types. In Pandas 1.0.x, this same data causes a unhashable object error. The error appears to be due to lists that contain date-like objects that are converted by default with convert_dates=True.
In pandas 0.25.x, it handles the lists of dates, but does not convert those items. they are kept as strings.
#### Expected Output
pandas dataframe containing lists without error.
```
df.to_json()
```
returns
```
'{"accounts":{"0":[1,2]},"event_time":{"0":["2020-04-08T09:50:49+00:00","2020-04-08T09:58:49+00:00"]},"name":{"0":"hector"}}'
```
#### Output of 1.0.3
Using pd.read_json(json_line, lines=True)
Errors vary slightly depending on context:
* TypeError: unhashable type: 'list'
* TypeError: <class 'list'> is not convertible to datetime. in the small example above
using pd.read_json(json_line, lines=True, convert_dates=False) returns expected output consistent with pandas 0.25.x.
[paste the output of 0.25.3 here leaving a blank line after the details tag]
```
'{"accounts":{"0":[1,2]},"event_time":{"0":["2020-04-08T09:50:49+00:00","2020-04-08T09:58:49+00:00"]},"name":{"0":"hector"}}'
```
</details>
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DOC: Remove ambiguity in fill_value documentation
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| 1
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| 2020-04-26T08:00:14Z
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|
CONTRIBUTOR
| null |
- [x] closes #33380
- [ ] tests added / passed (Not needed)
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry (Not needed)
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| 1
| 2020-04-25T14:35:04Z
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|
MEMBER
| null |
This is already done in some places for PandasArray, e.g., min / max, and seems cleaner with less boilerplate
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API: Dispatch mechanism for EA reductions
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MEMBER
| null |
https://github.com/pandas-dev/pandas/pull/33538 implemented sum as a standalone method for IntegerArray which somewhat duplicates the already-existing _reduce path taken by DataFrame / Series for certain reductions (see https://github.com/pandas-dev/pandas/pull/33538#pullrequestreview-393402534). Likely it makes sense to implement these at the IntegerArray / BooleanArray / etc. level using that same approach, e.g., something like defining each reduction to simply call _reduce with the appropriate operation name instead of copy / pasting the logic into each method.
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| 8
| 2020-04-25T14:59:44Z
| 2020-04-29T22:49:11Z
| 2020-04-29T22:49:08Z
|
CONTRIBUTOR
| null |
- [x] closes #33130
- [ ] tests added / passed (Not needed)
- [x] passes `black pandas`
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MDExOlB1bGxSZXF1ZXN0NDA4OTM5MzE2
| 33,792
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REF: remove need to override get_indexer_non_unique in DatetimeIndexOpsMixin
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| 2020-04-25T17:05:31Z
| 2020-04-25T21:03:17Z
| 2020-04-25T20:51:26Z
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NDA4OTUwMzMx
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DOC: Fix typos and improve parts of docs
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CONTRIBUTOR
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: can't import pandas in directory containing file named inspect.py
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|
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
```bash
$ python3 -c "import pandas;print(pandas.__version__)" && touch inspect.py && python3 -c "import pandas"
1.0.3
Traceback (most recent call last): <error>
```
The error message:
<details>
File "<string>", line 1, in <module>
File "/home/f/.local/lib/python3.6/site-packages/pandas/__init__.py", line 55, in <module>
from pandas.core.api import (
File "/home/f/.local/lib/python3.6/site-packages/pandas/core/api.py", line 29, in <module>
from pandas.core.groupby import Grouper, NamedAgg
File "/home/f/.local/lib/python3.6/site-packages/pandas/core/groupby/__init__.py", line 1, in <module>
from pandas.core.groupby.generic import DataFrameGroupBy, NamedAgg, SeriesGroupBy
File "/home/f/.local/lib/python3.6/site-packages/pandas/core/groupby/generic.py", line 60, in <module>
from pandas.core.frame import DataFrame
File "/home/f/.local/lib/python3.6/site-packages/pandas/core/frame.py", line 104, in <module>
from pandas.core.generic import NDFrame, _shared_docs
File "/home/f/.local/lib/python3.6/site-packages/pandas/core/generic.py", line 151, in <module>
class NDFrame(PandasObject, SelectionMixin, indexing.IndexingMixin):
File "/home/f/.local/lib/python3.6/site-packages/pandas/core/generic.py", line 1111, in NDFrame
def rename_axis(self, mapper=lib.no_default, **kwargs):
File "/home/f/.local/lib/python3.6/site-packages/pandas/util/_decorators.py", line 229, in decorate
kind = inspect.Parameter.POSITIONAL_OR_KEYWORD
AttributeError: module 'inspect' has no attribute 'Parameter'
</details>
#### Problem description
When using pandas in a project, I must take care not to use the filename `inspect.py`.
#### Expected Output
No error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-96-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 41.5.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 2.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MDY4NTM4NDU=
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BUG: quantile with list of quantiles fails on MultiIndex column and groupby
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| 1
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| 2020-12-02T15:40:01Z
| 2020-12-02T15:40:01Z
|
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
steps = 6
simulations = 20
alpha = 0.05
idx_interim_date_forecast = pd.IndexSlice["2020-04-01":]
df_return_forecast = pd.concat(
[
pd.DataFrame(
data=np.random.rand(steps, simulations),
index=pd.date_range(
start=idx_interim_date_forecast.start, periods=steps, freq="M"
),
columns=pd.MultiIndex.from_product(
[("sample",), range(0, simulations)], names=["scenario", "simulation"]
),
),
pd.DataFrame(
data=np.random.rand(steps, simulations),
index=pd.date_range(
start=idx_interim_date_forecast.start, periods=steps, freq="M"
),
columns=pd.MultiIndex.from_product(
[("trend",), range(0, simulations)], names=["scenario", "simulation"]
),
),
],
axis=1,
sort=True,
)
df_return_forecast.groupby(axis=1, level=0).quantile(q=alpha / 2)
df_return_forecast.groupby(axis=1, level=0).quantile(q=1 - alpha / 2)
df_return_forecast.groupby(axis=1, level=0).quantile(q=[alpha / 2, 1 - alpha / 2])
```
```python
Traceback (most recent call last):
File "<ipython-input-1-6a99a78036cb>", line 37, in <module>
df_return_forecast.groupby(axis=1, level=0).quantile(q=[alpha / 2, 1 - alpha / 2])
File "C:\Users\Kurt\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 1951, in quantile
indices = np.arange(len(result)).reshape([len(q), self.ngroups]).T.flatten()
```
#### Problem description
I would expect the ```quantile``` call with an iterable list of quantiles to return at the specified locations. Individual calls at the required locations returns the correct quantiles. The issue seems to be a result of the multiindex column/groupby.
If instead I do the following I am able to get the desired result without error.
```python
df_return_forecast.T.groupby(axis=0, level=0).quantile(q=[alpha / 2, 1 - alpha / 2]).T
```
So there is a workaround but the behaviour is not as expected and I am not sure of the performance issues it my induce for large dataframes.
#### Expected Output
As per result obtained by:
```python
df_return_forecast.T.groupby(axis=0, level=0).quantile(q=[alpha / 2, 1 - alpha / 2]).T
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.10.4
sphinx : 3.0.2
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDA4OTY5NTIz
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CLN: remove unused PeriodEngine methods
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REF: mix NDArrayBackedExtensionArray into PandasArray
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MEMBER
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Share several more methods in NDArrayBackedExtensionArray.
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| 1
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| 2020-04-27T23:47:23Z
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|
CONTRIBUTOR
| null |
- [x] closes #29055
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: pandas.dataframe.eval fails to assign values to a subset of a column in some cases
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| 1
| 2020-04-26T01:29:02Z
| 2020-04-26T01:46:18Z
| 2020-04-26T01:32:50Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
`>>> import pandas as pd
>>> df=pd.DataFrame({'A':[1,2,0,0],'B':[3,4,0,0]})
>>> df
A B
0 1 3
1 2 4
2 0 0
3 0 0
>>> df.loc[0:1,:].eval('A=B',inplace=True)
>>> df
A B
0 3 3
1 4 4
2 0 0
3 0 0
>>> df.loc[0:1,:].eval('A=1000',inplace=True)
>>> df
A B
0 3 3
1 4 4
2 0 0
3 0 0
>>> df[0:2].eval('A=B*10',inplace=True)
>>> df
A B
0 30 3
1 40 4
2 0 0
3 0 0
>>> df.loc[0:1,:].eval('C=A+B',inplace=True)
>>> df
A B
0 30 3
1 40 4
2 0 0
3 0 0
>>> df[0:2].eval('C=A+B',inplace=True)
>>> df
A B
0 30 3
1 40 4
2 0 0
3 0 0
>>> df.eval('C=A+B',inplace=True)
>>> df
A B C
0 30 3 33
1 40 4 44
2 0 0 0
3 0 0 0
>>> df['D']=0
>>> df.loc[0:1,:].eval('D=A+B',inplace=True)
>>> df
A B C D
0 30 3 33 0
1 40 4 44 0
2 0 0 0 0
3 0 0 0 0
>>> df.insert(4,'E',0)
>>> df.loc[0:1,:].eval('E=A+B',inplace=True)
>>> df
A B C D E
0 30 3 33 0 0
1 40 4 44 0 0
2 0 0 0 0 0
3 0 0 0 0 0
>>> df=df.assign(F=0)
>>> df
A B C D E F
0 30 3 33 0 0 0
1 40 4 44 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
>>> df.loc[0:1,:].eval('F=9',inplace=True)
>>> df
A B C D E F
0 30 3 33 0 0 0
1 40 4 44 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
>>> df.loc[0:1,:].eval('F=A',inplace=True)
>>> df
A B C D E F
0 30 3 33 0 0 0
1 40 4 44 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 0
>>> df=df.assign(F=0)
>>> df.loc[0:1,:].eval('F=A',inplace=True)
>>> df
A B C D E F
0 3 3 6 0 0 3
1 4 4 8 0 0 4
2 0 0 0 0 0 0
3 0 0 0 0 0 0`
#### Problem description
eval() assignment with inplace=True on dataframe subset fails in the following cases:
- when trying to assign an integer A=1000 (it did work for A=B)
- when trying to assign an integer or column operation to a new column C=A+B
- when trying to assign an integer or column operation to a column added after dataframe initialization F=9 , F=A (it did work after the second df=df.assign(F=0))
#### Expected Output
eval() should have changed the original df in all cases
#### Output of ``pd.show_versions()``
<details>
</details>
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MDU6SXNzdWU2MDY4OTUxNzA=
| 33,800
|
BUG: get_dummies does not create dummy columns for values passed to CategoricalDtype
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| 2020-08-21T17:06:06Z
| 2020-04-26T02:32:43Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from pandas.api.types import CategoricalDtype
df = pd.DataFrame({'country': ['russia', 'germany', 'russia', 'germany', 'germany']})
df["country"] = df["country"].astype('category',CategoricalDtype(categories=["germany","russia","japan"]))
# there should be a column called `japan`, but it's missing
pd.get_dummies(df["country"])
```
#### Problem description
Calling `pd.get_dummies()` on a DataFrame column of type `category` having extra `categories` passed does **not** generate columns for **extra categories** other than those that exist in the DataFrame.
#### Expected Output
The output should be a dataframe with one-hot encoded columns for values `germany`, `russia` **and** for `japan`.
(This was the output in Pandas 0.24 when we could still use `.astype('category', categories=[...])` without needing to define a `CategoricalDtype`)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-42-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDA5MDAxNjU2
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|
BUG: incorrect freq in PeriodIndex-Period
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| 1
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MEMBER
| null |
introduced in #33552
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Why not support `GroupBy.rank(numeric_only=False)`?
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NONE
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BUG: .corr() fails when input is Int64Dtype()
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| 2020-04-26T08:02:46Z
| 2020-04-27T23:53:59Z
| 2020-04-27T23:53:59Z
|
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] This bug exists on the master branch of pandas. [see below](https://github.com/pandas-dev/pandas/issues/33803#issuecomment-619574491)
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'First':[1,2,3,4,np.nan],
'Second':[True, True, False, False, True],
'Third':[2,3,8,5,6]})
print(df.dtypes)
print(df.corr())
df
```
```
df_conv = df.convert_dtypes()
print(df_conv.dtypes)
print(df_conv)
df_conv.corr() # results in the following TypeError
TypeError: float() argument must be a string or a number, not 'NAType'
```
#### Problem description
When applying convert_dtypes() to a DataFrame, columns containing NaNs (np.nan) and integer values (stored as float64) are converted to "pd.NA" and actual integers (Int64). While this is desirable, it results in an error when calling the .corr() method on the DataFrame with the new dtypes.
**Before convert_dtypes():**
**After convert_dtypes():**
**Error message:**
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200325
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDA5MDQ1MTA0
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|
BUG: support corr and cov functions for custom BaseIndexer rolling windows
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| 6
| 2020-04-26T08:51:23Z
| 2020-07-09T15:12:53Z
| 2020-04-27T18:59:44Z
|
MEMBER
| null |
- [X] closes #32865
- [X] reverts #33057
- [X] 2 tests added / 2 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
## Scope of PR
This is the final PR needed to solve #32865. It fixes `cov` and `corr` by passing the correct argument to `rolling` constructors if the functions are called with a custom `BaseIndexer` subclass. A separate test is added for `cov` and `corr` as the previous tests were single-variable and wouldn't work with two columns of data. I also propose we revert #33057.
## Details
The reason `cov` and `corr` didn't work was that we were passing `_get_window` results (usually, window width), instead of the `BaseIndexer` subclass to `rolling` constructors called inside the function. Passing `self.window` (when necessary) fixes the issue.
The new test asserts only against an explicit array, because shoehorning a comparison against `np.cov` an `np.corrcoef` is ugly (I've tried). Our `rolling.apply` isn't really suited to be called with bivariate statistics functions, and the necessary gymnastics didn't look good to me. I don't believe the minor benefit would be worth, but please say if you think coomparing against numpy is necessary.
Thanks to @mroeschke for implementing the `NotImplemented` error-raising behavior in #33057 . Now that all the functions are fixed, I propose we revert that commit.
## Background on the wider issue
We currently don't support several rolling window functions when building a rolling window object using a custom class descended from `pandas.api.indexers.Baseindexer`. The implementations were written with backward-looking windows in mind, and this led to these functions breaking.
Currently, using these functions returns a `NotImplemented` error thanks to #33057, but ideally we want to update the implementations, so that they will work without a performance hit. This is what I aim to do over a series of PRs.
## Perf notes
No changes to algorithms.
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TST: Added message to bare pytest.raises in test_join_multi_levels (#…
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| 2
| 2020-04-26T16:01:09Z
| 2020-04-26T20:31:07Z
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|
CONTRIBUTOR
| null |
…30999)
Updated tests in test_join_multi_levels to include messages for pytest.raises.
- [ ] closes #xxxx
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API: always treat tuples as object-dtype-scalars for arithmetic?
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| 2020-04-26T16:48:16Z
| 2020-09-22T01:33:43Z
| null |
MEMBER
| null |
For arithmetic-like ops when we see a list we cast it to ndarray, while with tuple we _sometimes_ cast to ndarray, sometimes treat it as scalar-like. We should be consistent about this.
I lean towards always-scalar-like, but would be fine either way.
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CLN: use unpack_zerodim_and_defer in timedeltas
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BUG: Don't raise in DataFrame.corr with pd.NA
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| 2020-09-27T03:27:52Z
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MEMBER
| null |
- [x] closes #33803
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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TST: add Feather V2 round-trip test
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| 2020-04-26T19:33:30Z
| 2021-08-03T09:11:56Z
| 2021-07-31T04:46:05Z
|
CONTRIBUTOR
| null |
no that pyarrow 0.17 has landed, we should have a round-trip Feather V2 test to ensure we have dtype preservation (we can likely re-use some of our test frames from the parquet tests).
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| 1
| 2020-04-26T19:51:44Z
| 2020-04-26T20:55:29Z
| 2020-04-26T20:52:37Z
|
MEMBER
| null |
introduced in #33552
|
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MDExOlB1bGxSZXF1ZXN0NDA5MTQ5MjE4
| 33,812
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TST: check freq in assert_equal
|
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| 1
| 2020-04-26T20:07:40Z
| 2020-04-26T20:55:14Z
| 2020-04-26T20:53:29Z
|
MEMBER
| null |
Before long we'll move this check into assert_index_equal, for now just want to get validation in place where feasible.
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MDExOlB1bGxSZXF1ZXN0NDA5MTUyNDIy
| 33,813
|
TST: Clean moments consistency
|
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| 3
| 2020-04-26T20:28:20Z
| 2020-05-03T00:38:59Z
| 2020-04-26T21:23:58Z
|
MEMBER
| null |
As discussed in https://github.com/pandas-dev/pandas/pull/30577#issuecomment-570014613
This PR is the first step to achieve it, it basically does one thing: break up the giant consistency test in `common` into four tests based on test contents: `var`, `std`, `cov`, and `series data`.
cc @jreback
|
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MDU6SXNzdWU2MDcxNDIxMjg=
| 33,814
|
BUG: Merge between partial index and index fails if result is empty
|
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| 0
| 2020-04-26T23:32:41Z
| 2021-01-03T23:21:42Z
| 2021-01-03T23:21:42Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas
pandas.merge(
pandas.DataFrame({'a': [1], 'i': [2]}).set_index(['a', 'i']),
pandas.DataFrame({'i': [1]}).set_index(['i']),
left_on=['i'],
right_index=True,
)
```
#### Problem description
This merge fails with the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 88, in merge
return op.get_result()
File "/usr/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 668, in get_result
self._maybe_add_join_keys(result, left_indexer, right_indexer)
File "/usr/lib/python3.8/site-packages/pandas/core/reshape/merge.py", line 824, in _maybe_add_join_keys
key_col.name = name
AttributeError: 'numpy.ndarray' object has no attribute 'name'
```
Thing is, it works fine if I change `i` in the left DataFrame from `[2]` to `[1]`. It only fails if there's no overlap between the join keys.
This behavior is problematic because the failure depends on the outcome of the merge, so it's difficult to avoid.
#### Expected Output
I expect this to not fail and just return an empty result.
#### Output of ``pd.show_versions()``
<details>
```
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.2-arch1-2
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_FYL.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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MDExOlB1bGxSZXF1ZXN0NDA5MjA4ODg4
| 33,815
|
TST: check freq on series.index in assert_series_equal
|
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| 13
| 2020-04-27T02:14:35Z
| 2020-05-06T20:08:13Z
| 2020-04-27T13:00:18Z
|
MEMBER
| null |
Before long we'll roll that check into assert_index_equal
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| 607,242,021
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MDExOlB1bGxSZXF1ZXN0NDA5MjU2NjMz
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ERR: Add NumbaUtilError
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| 2
| 2020-04-27T05:18:54Z
| 2020-04-27T20:46:12Z
| 2020-04-27T20:17:45Z
|
MEMBER
| null |
- xref https://github.com/pandas-dev/pandas/pull/33388#discussion_r415172278
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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