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MDExOlB1bGxSZXF1ZXN0MzY5NzM5NjMw
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REGR: DataFrame.__setitem__(slice, val) is positional
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| 4
| 2020-01-31T19:36:38Z
| 2020-02-05T12:34:52Z
| 2020-02-01T14:22:06Z
|
MEMBER
| null |
- [x] closes #31469
- [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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query works for string data type columns with missing values but not for boolean indexing
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| 2020-01-31T19:43:18Z
| 2020-04-26T21:13:34Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame({'a': ['asdf', None]}, dtype='string')
>>> df.query('a == "asdf"') # works
>>> df[df['a'] == 'asdf'] # fails
>>> df.query('a <= "asdf"') # fails
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-1d73f7d1a2fe> in <module>
----> 1 df[df['a'] == 'asdf']
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/frame.py in __getitem__(self, key)
2784
2785 # Do we have a (boolean) 1d indexer?
-> 2786 if com.is_bool_indexer(key):
2787 return self._getitem_bool_array(key)
2788
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/common.py in is_bool_indexer(key)
132 if is_extension_array_dtype(key.dtype):
133 if np.any(key.isna()):
--> 134 raise ValueError(na_msg)
135 return True
136 elif isinstance(key, list):
ValueError: cannot mask with array containing NA / NaN values
```
#### Problem description
This is a specific bug found from discussion in #31503. The new string data type should fail for any `query` call where there are missing values in the evaluated condition. It correctly fails for boolean indexing and for inequality conditions in `query`
#### Expected Output
`df.query('a == "asdf"')` should fail
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0+untagged.1.gce8af21.dirty
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NTgzMzQ5NDY=
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pd.DataFrame.replace doesn't work after converting to new dtypes in 1.0.0
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| 3
| 2020-01-31T19:43:47Z
| 2021-05-06T09:13:19Z
| 2020-02-01T23:10:40Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.show_versions()
df = pd.DataFrame({'grp':[1,2,3,4,5]})
df = df.convert_dtypes()
df.replace(1,10)
```
#### Problem description
pd.DataFrame.replace is not working after using pd.DataFrame.convert_dtypes.
Stack trace:
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-9-fb2a3a449007> in <module>
3 df = pd.DataFrame({'grp':[1,2,3,4,5]})
4 df = df.convert_dtypes()
----> 5 df.replace(1,10)
~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in replace(self, to_replace, value, inplace, limit, regex, method)
4167 limit=limit,
4168 regex=regex,
-> 4169 method=method,
4170 )
4171
~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method)
6735 elif not is_list_like(value): # NA -> 0
6736 new_data = self._data.replace(
-> 6737 to_replace=to_replace, value=value, inplace=inplace, regex=regex
6738 )
6739 else:
~/anaconda3/lib/python3.6/site-packages/pandas/core/internals/managers.py in replace(self, value, **kwargs)
587 def replace(self, value, **kwargs):
588 assert np.ndim(value) == 0, value
--> 589 return self.apply("replace", value=value, **kwargs)
590
591 def replace_list(self, src_list, dest_list, inplace=False, regex=False):
~/anaconda3/lib/python3.6/site-packages/pandas/core/internals/managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
~/anaconda3/lib/python3.6/site-packages/pandas/core/internals/blocks.py in replace(self, to_replace, value, inplace, filter, regex, convert)
769
770 try:
--> 771 blocks = self.putmask(mask, value, inplace=inplace)
772 # Note: it is _not_ the case that self._can_hold_element(value)
773 # is always true at this point. In particular, that can fail
~/anaconda3/lib/python3.6/site-packages/pandas/core/internals/blocks.py in putmask(self, mask, new, align, inplace, axis, transpose)
1671 mask = _safe_reshape(mask, new_values.shape)
1672
-> 1673 new_values[mask] = new
1674 return [self.make_block(values=new_values)]
1675
~/anaconda3/lib/python3.6/site-packages/pandas/core/arrays/integer.py in __setitem__(self, key, value)
415 mask = mask[0]
416
--> 417 self._data[key] = value
418 self._mask[key] = mask
419
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
#### Expected Output
Should work just like it does before using pd.DataFrame.convert_dtypes.
```import pandas as pd
#pd.show_versions()
df = pd.DataFrame({'grp':[1,2,3,4,5]})
#df = df.convert_dtypes()
df.replace(1,10)```
Output:
```
| | grp |
|---:|------:|
| 0 | 10 |
| 1 | 2 |
| 2 | 3 |
| 3 | 4 |
| 4 | 5 |
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 20.0.1
setuptools : 45.1.0
Cython : 0.29
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 2.2.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.9
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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MDExOlB1bGxSZXF1ZXN0MzY5NzYwOTUy
| 31,518
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TYP: Arraylike
|
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| 2020-01-31T20:36:09Z
| 2020-02-01T16:44:33Z
| 2020-02-01T16:44:21Z
|
MEMBER
| null |
@simonjayhawkins mypy doesn't like this
```
$ mypy pandas
pandas/core/base.py:679: error: "ArrayLike" has no attribute "nbytes"
pandas/core/indexes/base.py:246: error: Type variable "pandas._typing.ArrayLike" is unbound
pandas/core/indexes/base.py:246: note: (Hint: Use "Generic[ArrayLike]" or "Protocol[ArrayLike]" base class to bind "ArrayLike" inside a class)
pandas/core/indexes/base.py:246: note: (Hint: Use "ArrayLike" in function signature to bind "ArrayLike" inside a function)
pandas/core/indexes/base.py:1152: error: "ArrayLike" has no attribute "copy"
pandas/core/indexes/base.py:3881: error: ArrayLike? has no attribute "view"
pandas/core/series.py:252: error: "ArrayLike" has no attribute "copy"
pandas/io/pytables.py:2240: error: "ExtensionDtype" has no attribute "itemsize"
pandas/io/pytables.py:2245: error: "ExtensionArray" has no attribute "size"
pandas/io/pytables.py:2248: error: "ExtensionArray" has no attribute "codes"
pandas/io/pytables.py:4985: error: "ExtensionArray" has no attribute "codes"
```
Is this a product of ArrayLike being a TypeVar instead of a Union? At least the last two look like they need to be Categorical instead of ExtensionArray, not sure about the others.
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| 558,374,173
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MDExOlB1bGxSZXF1ZXN0MzY5NzcyMTgw
| 31,519
|
REF: simplify DTI._parse_string_to_bounds
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| 1
| 2020-01-31T21:09:53Z
| 2020-04-05T17:34:10Z
| 2020-02-05T01:46:22Z
|
MEMBER
| null |
Sits on top of #31475. i.e. once that is merged, the only diff remaining here will be in indexes.datetimes.
After this, we are within striking distance of sharing the method between DTI/PI.
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| 558,388,913
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MDExOlB1bGxSZXF1ZXN0MzY5NzgzODE2
| 31,520
|
REGR: to_datetime, unique with OOB values
|
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| 0
| 2020-01-31T21:45:02Z
| 2020-02-01T14:29:37Z
| 2020-02-01T14:29:37Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/31491
This turned up two bugs:
1. `to_datetime(oob_values, cache=True, errors='coerce')` would raise. We had tests intended to catch this, but we didn't actually hit it because caching is disabled for inputs less than length 50. We only tested with length 3. I've updated the test to pass that threshold
2. The original report, that `to_datetime` was raising for non-ns resolution values that aren't OOB for ns resolution. This was a bug in `unique`.
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MDExOlB1bGxSZXF1ZXN0MzY5NzkyMjYz
| 31,521
|
REGR: Fixed slicing DatetimeIndex with date
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| 7
| 2020-01-31T22:10:44Z
| 2020-02-03T16:49:38Z
| 2020-02-01T14:56:13Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/31501
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MDU6SXNzdWU1NTg0MTMxMzA=
| 31,522
|
AssertionError when grouping with max/min as aggregation functions (pandas-1.0.0)
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| 12
| 2020-01-31T22:44:03Z
| 2020-08-13T18:02:48Z
| 2020-02-05T14:55:26Z
|
NONE
| null |
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],
'key2' : ['one', 'two', 'one', 'two', 'one'],
'key3' : ['three', 'three', 'three', 'six', 'six'],
'data1' : np.random.randn(5),
'data2' : np.random.randn(5)})
df.groupby('key1').min()
```
#### Problem description
Since `pandas-1.0.0`, an `AssertionError` is thrown when grouping a `DataFrame` by a key and using `max`/`min` as aggregation functions. It works fine if only 1 key (other than the grouping key) is of the type `object` in the DataFrame, but it doesn't when the number of keys of type `object` is bigger than 1 (as shown in the example). This configuration worked fine on previous versions of `pandas` (e.g., `pandas-0.25.3`).
#### Expected Output
| key1 | key2 | key3 | data1 | data2 |
|:-------|:-------|:-------|---------:|----------:|
| a | one | six | -0.67246 | -1.6302 |
| b | one | six | -1.72628 | -0.907298 |
#### Output of ``pd.show_versions()``
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
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MDU6SXNzdWU1NTg0MjAyMDE=
| 31,523
|
RLS: 1.0.1
|
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| 9
| 2020-01-31T23:04:58Z
| 2020-04-05T10:45:23Z
| 2020-04-05T10:45:22Z
|
CONTRIBUTOR
| null |
A decent number of regressions have been reported on 1.0.0. I think we should target 1.0.1 for sometime early next week. Perhaps on Tuesday, depending on how much progress we make on the regressions.
cc @pandas-dev/pandas-core.
make sure these are [backported](
https://github.com/pandas-dev/pandas/pulls?q=is%3Apr+label%3A%22Still+Needs+Manual+Backport%22)
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| 558,422,997
|
MDExOlB1bGxSZXF1ZXN0MzY5ODA5MDI2
| 31,524
|
BUG: non-iterable value in meta raise error in json_normalize
|
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| 23
| 2020-01-31T23:14:26Z
| 2020-03-11T15:02:43Z
| 2020-03-11T15:02:32Z
|
MEMBER
| null |
- [x] closes #31507
- [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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| 31,525
|
Unpin openpyxl
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| 5
| 2020-01-31T23:59:19Z
| 2020-04-07T14:43:02Z
| 2020-02-02T19:32:23Z
|
MEMBER
| null |
I *think* we pinned this originally because of issues with 3.0.2 specifically, but looks like a newer version is available on conda
ref #30009
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MDU6SXNzdWU1NTg0NjEwMDI=
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|
DOC: 10 minutes - <TAB> completion section showing inconsistent suggestions
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| 5
| 2020-02-01T02:05:43Z
| 2020-02-09T17:23:54Z
| 2020-02-09T17:23:54Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
From the very end of the section "[Object Creation](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html#object-creation)"
```python
In [12]: df2.<TAB> # noqa: E225, E999
df2.A df2.bool
df2.abs df2.boxplot
df2.add df2.C
df2.add_prefix df2.clip
df2.add_suffix df2.clip_lower
df2.align df2.clip_upper
df2.all df2.columns
df2.any df2.combine
df2.append df2.combine_first
df2.apply df2.consolidate
df2.applymap
df2.D
```
And the following text:
```rst
As you can see, the columns ``A``, ``B``, ``C``, and ``D`` are automatically
tab completed. ``E`` is there as well; the rest of the attributes have been
truncated for brevity.
```
#### Problem description
In the text we read that columns `A`, `B`, `C` and `D` are shown. However, `df2.B` isn't shown at all
#### Expected Output
I think we could just add some more <TAB> completions, along side `df2.B` to make the documentation more consistent to what is being said.
Something similar to:
```python
In [12]: df2.<TAB> # noqa: E225, E999
df2.A df2.bool
df2.abs df2.boxplot
df2.add df2.C
df2.add_prefix df2.clip
df2.add_suffix df2.columns
df2.align df2.combine
df2.all df2.combine_first
df2.any df2.D
df2.append df2.describe
df2.apply df2.diff
df2.applymap df2.div
df2.B df2.divide
```
##### Note
I'd like to know what does `# noqa: E225, E999` means. I didn't see this reference early in this guide
|
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MDExOlB1bGxSZXF1ZXN0MzY5ODQzOTgy
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CLN: libperiod
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| 1
| 2020-02-01T03:04:35Z
| 2020-02-01T23:34:49Z
| 2020-02-01T23:23:06Z
|
MEMBER
| null |
separating cleanups from non-cleanup branches
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MDExOlB1bGxSZXF1ZXN0MzY5ODQ3MDg1
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REGR: Categorical with np.str_ categories
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| 10
| 2020-02-01T03:40:31Z
| 2020-02-04T16:31:28Z
| 2020-02-04T16:24:00Z
|
MEMBER
| null |
- [x] closes #31499
- [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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| 2020-02-01T16:10:25Z
| 2020-02-01T15:24:45Z
|
MEMBER
| null |
- [x] closes #31457
- [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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MDU6SXNzdWU1NTg1MTYzMjc=
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`style` settings overwrite all `display` settings
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[] | null | 2
| 2020-02-01T09:56:43Z
| 2021-07-27T05:45:10Z
| 2021-07-27T05:45:10Z
|
NONE
| null |
The following code reproduces the issue:
```python
import pandas as pd
from IPython.display import display
pd.options.display.float_format = "{:,.1f}".format
df = pd.DataFrame([1000.1234, 2000.1234])
display(df)
display(df.style.hide_index())
```
Output:
IMHO `style.hide_index()` should not overwrite the previous `display.float_format` setting, only `style.format` should do it.
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MDU6SXNzdWU1NTg1MTgwODU=
| 31,531
|
DOC issue of rendering for the docsting of pd.set_option
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| 2020-02-01T10:14:13Z
| 2020-02-01T13:46:17Z
| 2020-02-01T13:46:17Z
|
CONTRIBUTOR
| null |
It seems that the `pd.set_option` docsting is not parsed properly by `sphinx`:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.set_option.html
It was already the case in 0.25 doc but it was working in 0.23:
https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.set_option.html
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MDU6SXNzdWU1NTg1MTkyOTA=
| 31,532
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BUG display.max_colwidth do not accept -1 for unlimited width
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| 2
| 2020-02-01T10:22:39Z
| 2020-02-02T18:30:39Z
| 2020-02-02T18:30:39Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.set_option("display.max_colwidth", -1)
```
#### Problem description
There is a regression with `"display.max_colwidth"`. In the past, it was only accepting integer. The way to no limit the size was to pass `-1`. In pandas 1,0, this option becomes more consistent and not limiting the width should be set with `None`. However, the support for negative integer was removed.
Thus, one would need to either set to `-1` or `None` depending on the pandas version. It would be best to support both options. Potentially, support for negative integer could be removed with a deprecation cycle.
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MDU6SXNzdWU1NTg1MjMzNzU=
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Formatting of `pandas.set_option` seems broken
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| 2020-02-01T10:56:37Z
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MEMBER
| null |
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.set_option.html
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MDExOlB1bGxSZXF1ZXN0MzY5ODg1OTM1
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CI: Reverted changes related to 'jedi' warnings
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| 2020-02-20T03:17:09Z
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|
MEMBER
| null |
- [x] closes #31407
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: Replace isinstace(foo, Class) with isinstance(foo, ABCClass)
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| 1
| 2020-02-01T11:37:51Z
| 2020-02-01T15:24:23Z
| 2020-02-01T15:08:17Z
|
MEMBER
| null |
- [x] ref #27353
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzY5ODk2ODE1
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|
TST: Added regression test case
|
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| 2020-02-01T13:11:59Z
| 2020-04-06T09:49:03Z
| 2020-04-06T09:48:29Z
|
MEMBER
| null |
Resurrection of #28966
- [x] closes #18549
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1NTg1NDA0NzQ=
| 31,537
|
DOC: Mention that boolean indexing is impossible for new nullable integer/boolean and string data types when they contain missing values
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CONTRIBUTOR
| null |
The new data types nullable integer, nullable boolean, and string cannot do boolean indexing when the columns contain missing values.
I searched the [pd.NA][0] and the [boolean indexing][1] sections and have not seen this mentioned. Users need to be aware of this limitation as it greatly reduces their usability.
[0]: https://pandas.pydata.org/docs/user_guide/missing_data.html#experimental-na-scalar-to-denote-missing-values
[1]: https://pandas.pydata.org/docs/user_guide/indexing.html#boolean-indexing
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MDExOlB1bGxSZXF1ZXN0MzY5ODk5NTEw
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TST: Added regression test
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| 2020-02-01T13:36:47Z
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MEMBER
| null |
- [x] closes #10329
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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|
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MDU6SXNzdWU1NTg1NDU5MDQ=
| 31,539
|
WEB: Wrong links on web page
|
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| 2020-02-01T14:10:32Z
| 2020-02-02T15:24:00Z
| 2020-02-02T15:22:33Z
|
CONTRIBUTOR
| null |
On [this page ](https://pandas.pydata.org/ )The link at "what's new in 1.0.0" (upper right corner) points to the v0.25 whatsnew.
On [this page](https://pandas.pydata.org/docs/index.html) the link on the upper left side logo points to the same page. I would expect it to point to the main page (https://pandas.pydata.org/).
|
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| 558,548,598
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MDExOlB1bGxSZXF1ZXN0MzY5OTA0Mjcx
| 31,540
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Backport PR #31520 on branch 1.0.x (REGR: to_datetime, unique with OOB values)
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| 2020-02-01T14:30:13Z
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NONE
| null |
Backport PR #31520: REGR: to_datetime, unique with OOB values
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Backport PR #31456 on branch 1.0.x (BUG: Groupby.apply wasn't allowing for functions which return lists)
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Backport PR #31456: BUG: Groupby.apply wasn't allowing for functions which return lists
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Backport PR #31513 on branch 1.0.x (REGR: Fixed truncation with na_rep)
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| 2020-02-01T15:21:22Z
| 2020-02-01T19:39:15Z
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NONE
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Backport PR #31513: REGR: Fixed truncation with na_rep
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Backport PR #31529 on branch 1.0.x (BUG: Series multiplication with timedelta scalar numexpr path)
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NONE
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Backport PR #31529: BUG: Series multiplication with timedelta scalar numexpr path
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Reading with read_stata in chunks messes up categories
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| 2020-02-01T15:39:57Z
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MEMBER
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#### Code Sample, a copy-pastable example if possible
```python
In [2]: df = pd.DataFrame({'col{}'.format(k) : pd.Categorical(['a_label'] +
['another_label']*500)
for k in range(2)})
In [3]: df.dtypes
Out[3]:
col0 category
col1 category
dtype: object
In [4]: df.dtypes[0]
Out[4]: CategoricalDtype(categories=['a_label', 'another_label'], ordered=False)
In [5]: df.to_stata('/tmp/stata_test.dta', write_index=False)
In [6]: pd.read_stata('/tmp/stata_test.dta').dtypes
Out[6]:
col0 category
col1 category
dtype: object
# ... that's good
In [7]: reader = pd.read_stata('/tmp/stata_test.dta', chunksize=100)
In [8]: reader.value_labels()
Out[8]:
{'col0': {0: 'a_label', 1: 'another_label'},
'col1': {0: 'a_label', 1: 'another_label'}}
# ... still all good
In [9]: out_chunks = [chunk for chunk in reader]
In [10]: out_chunks[1].dtypes[0]
Out[10]: CategoricalDtype(categories=['another_label'], ordered=True)
# Ooops... where's the other label gone?
In [11]: reader.close()
In [12]: all_together = pd.concat(out_chunks)
In [13]: all_together.dtypes[0]
Out[13]: dtype('O')
# Ouch!
```
#### Problem description
My data has categories, but they are lost only because I'm reading it in chunks. I noticed this because I was reading in chunks a large database of which I only needed a subset of columns: ironically, precisely the fact that I was reading it in chunks made memory usage explode when I reattached them.
An by the way, ``Out[8]:`` shows that pandas _is_ aware of the actual categories, even before iterating... so this is the information that should be used to consistently recreate them, and all chunks should have exactly the _same_ (as in ``is``) categorical dtype.
#### Expected Output
``Out[10]`` should feature both categories, and ``Out[13]`` should still be a categorical.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-6-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 1.1.0.dev0+276.g2495068ad
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : 4.6.3
hypothesis : 3.71.11
sphinx : 1.8.4
blosc : 1.7.0
feather : None
xlsxwriter : 0.9.3
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.4.9
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.6.3
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.18
tables : 3.4.4
tabulate : 0.8.3
xarray : 0.11.3
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 0.9.3
numba : 0.45.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5OTEzOTI0
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BUG&TST: df.replace fail after converting to new dtype
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| 2020-02-01T23:10:57Z
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| null |
- [x] closes #31517
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Manual backport 31446
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BUG: GH31142 Fix for combine.Series
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Last tick label not showing
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| 2020-02-01T18:53:55Z
| 2021-07-27T05:45:37Z
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MEMBER
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Noticed while working on #31207
#### Code Sample, a copy-pastable example if possible
```python
from pandas import date_range, Series
import matplotlib.pyplot as plt
index = date_range("1/1/2011", periods=2, freq="H", tz='UTC')
ts = Series([188.5, 328.25], index=index)
ts.plot()
plt.show()
```
#### Problem description
Here's the output I see:
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
Would have expected both xticks to be labelled
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : d448f7df88534da7c791efa3ef7eff3c72985cb3
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.26.0.dev0+2017.gfc813e76e
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.0.0.post20200113
Cython : 0.29.14
pytest : 5.3.3
hypothesis : 5.1.5
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.3
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5OTMyNzY2
| 31,549
|
replace NotImplementedError with AttributeError
|
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| 22
| 2020-02-01T19:50:25Z
| 2020-04-22T22:52:33Z
| 2020-04-21T12:53:13Z
|
NONE
| null |
Fixes Python inspection of members - bug reported in https://github.com/pandas-dev/pandas/issues/31474
- [ ] closes #31474
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzY5OTMzNTg5
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BUG: parase_dates column is in dataframe (#31251)
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| 4
| 2020-02-01T19:59:54Z
| 2020-02-28T09:38:57Z
| 2020-02-08T15:20:47Z
|
CONTRIBUTOR
| null |
- [ ] closes #31251
- [ ] add test_missing_column in pandas/tests/io/parser/test_parse_dates.py
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzY5OTM3ODk2
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|
ADMIN: Create separate issue templates for different use cases
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| 6
| 2020-02-01T20:47:13Z
| 2020-03-31T17:41:49Z
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|
MEMBER
| null |
I recall this being mentioned on one of the core dev calls a few months back, and took inspiration from [rapidsai/cudf](https://github.com/rapidsai/cudf) for the various templates.
Added templates for the following use cases:
- Bug Report
- This is largely the same as the current issue template
- Documentation Enhancement
- Documentation Error
- Feature Request
- Submit Question
- This attempts to direct users to StackOverflow for usage questions
We could also add a Blank Template for opening an issue without any template provided, but opted not to do that for now, as I don't want to encourage people to bypass these templates. Could certainly add one if there's a consensus that we want this.
I've created a [local repo](https://github.com/jschendel/pandas-templates/issues) where you can see these templates in action since I couldn't figure out another way to actually display these. Click the "New Issue" button to see what these changes would look like.
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage
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BUG: to_json not allowing uploads to S3 (#28375)
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| 7
| 2020-02-01T20:48:08Z
| 2020-06-26T12:55:47Z
| 2020-02-02T22:29:04Z
|
CONTRIBUTOR
| null |
- [x] closes #28375
- [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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REF: collect MultiIndex indexing, set methods
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REF: move _convert_to_indexer to Loc
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MEMBER
| null |
It is overriden in iLoc, so by putting it directly in Loc we can simplify bits of it that check `self.name` and keep only the `self.name == "loc"` branches. This lets us get rid of a bit of dead code (L1638-L1642).
Most of the diff is misleading; it is only the one method that is moved, with a placeholder kept in NDFrameIndexer
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Backport PR #31545 on branch 1.0.x (BUG&TST: df.replace fail after converting to new dtype)
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| 2020-02-01T23:10:51Z
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NONE
| null |
Backport PR #31545: BUG&TST: df.replace fail after converting to new dtype
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[MRG] f-string updates for issue #29547
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| 7
| 2020-02-02T01:22:48Z
| 2020-02-04T00:41:26Z
| 2020-02-04T00:41:19Z
|
CONTRIBUTOR
| null |
Addresses, in part, https://github.com/pandas-dev/pandas/issues/29547
…ape/pivot, reshape/reshape
- [ x] xref #29547
- [n/a ] tests added / passed
- [x ] passes `black pandas`
- [ x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [n/a ] whatsnew entry
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REF: organize Series indexing tests
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| 2020-02-02T02:15:51Z
| 2020-02-02T17:38:48Z
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|
MEMBER
| null |
We've got indexing tests pretty scattered; this is organizing the ones in tests.series.indexing by method
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MDExOlB1bGxSZXF1ZXN0MzY5OTYzODE4
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|
REF: organize DataFrame tests for indexing, by-method
|
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| 2020-02-02T02:32:19Z
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MEMBER
| null |
The only change that isnt just rearrangement is implementing where_frame fixture in test_where and using it to get rid of for-loops inside a bunch of tests in that file
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MDExOlB1bGxSZXF1ZXN0MzY5OTY1MTA1
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|
REF: CategoricalIndex indexing tests
|
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| 1
| 2020-02-02T02:56:45Z
| 2020-02-02T18:41:49Z
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| 558,643,671
|
MDU6SXNzdWU1NTg2NDM2NzE=
| 31,560
|
Add TODOs Badge to the README
|
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| 2020-02-02T05:02:48Z
| 2020-02-21T03:29:13Z
| 2020-02-02T16:51:37Z
|
NONE
| null |
Hi there! I wanted to propose adding the following badge to the README to indicate how many `// TODO` comments are in this codebase:
[](https://www.tickgit.com/browse?repo=github.com/pandas-dev/pandas)
The badge links to `tickgit.com` which is a free service that indexes and displays TODO comments in public github repos. It can help surface latent work and be a way for new contributors to find areas of code to improve.
The markdown is:
```
[](https://www.tickgit.com/browse?repo=github.com/pandas-dev/pandas)
```
Thanks for considering, feel free to close this issue if it's not appropriate or you prefer not to!
|
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| 558,655,891
|
MDExOlB1bGxSZXF1ZXN0MzY5OTgxMTgw
| 31,561
|
Try manual backport
|
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| 2020-02-02T07:26:29Z
| 2020-02-02T07:36:33Z
| 2020-02-02T07:36:33Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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| 558,658,061
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MDExOlB1bGxSZXF1ZXN0MzY5OTgyODM4
| 31,562
|
Backport PR: BUG: Array.__setitem__ failing with nullable boolean mask (#31484)
|
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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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ENH: add fold support to Timestamp constructor
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| 71
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| 2020-03-06T08:24:04Z
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MEMBER
| null |
**PERF Note:** Current implementation slows down the `Timestamp` constructor. I've tested this thoroughly and tracked it down to ba7fcd5 where I changed the function signatures at the very beginning of working on the PR. The performance overhead appeared before any of the logic was implemented.
- [X] closes #25057, closes #31338
- [X] tests added 32 / passed 32
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
### Reasoning for changes
We currently don't support fold from [PEP 495](https://www.python.org/dev/peps/pep-0495/#the-fold-attribute), and this causes us a lot of grief, including, but not limited to, broken Timestamp representations at the edge of DST shift (see #31338). The values can also break.
Support of the fold attribute helps us deal with that. Now, if the wall clock time occurs twice due to a DST shift, we will know exactly at which point in time we are.
This also removes inconsistencies between using `pytz` and `dateutil`: now both the values and representations match for both timezone packages near DST summer/winter shifts.
### Scope of PR
Implementing fold into Timestamp can easily get out of hand. Parent `pydatetime` has it easy, as the object doesn't need to sync its underlying epoch time, fold, and the representation, like we do.
This PR is already large, so I propose we limit its scope to minimal consistent fold support:
- when fold is explicitly supplied, attempt to shift value across the DST boundary according to the value supplied
- infer fold from value during tz-aware Timestamp construction. For example, if Timestamp is built from epoch time, then we can infer fold.
- pass the resulting fold to the underlying `datetime` constructor in `create_timestamp_from_ts` so that it gets stored in the object (can't assign it directly as it's read-only).
- implement local timezone support for fold. Don't have any idea how to do this though.
Things I suggest we leave to discussion and other PRs:
- conflicts resolution. A user can supply `fold=1` for a datetime that is nowhere near a DST shift. We can raise and Error or a Warning in the future. For now, we check whether `fold` can be 1, and if not, we leave it as default `fold=0`.
- consider reintroducing ambiguous time errors. Currently, the implementation assumes `fold == 0` for ambiguous time like Timestamp("2019-10-27 01:30:00", tz='Europe/London'). The error was dropped to mirror the `fold=0` default in pydatetime and to allow the fold attribute to be `bint` in Cython functions.
**Note:** `pydatetime` doesn't infer fold from value and doesn't raise errors when you assign `fold=1` nowhere near a fold. Example:
```python
from datetime import datetime
from dateutil.tz import gettz
dt = datetime.datetime(2015, 10, 27, 5, 30, 0, 0, gettz("Europe/London"), fold=1)
dt.fold
OUT:
1
```
### Performance problems
This implementation behaves as I expect it to, but it slows down scalar constructor benchmarks by 30-70%, even for tz-naive benchmarks.
Update: since the benchmark slowdown appeared before any of the functionality, this has nothing to do with the logic introduced and all to do with adding `bint fold` to function signatures, apparently. I'm afraid I don't know Cython well enough to research further.
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CI: deprecation warning tests failing on linux py37_local pipeline
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| 1
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| 2020-02-07T07:41:19Z
| 2020-02-02T16:54:11Z
|
MEMBER
| null |
#### Problem description
As of now, the Linux py37_locale pipeline seems to fail independent of PR:
```
_________________ TestRegistration.test_registering_no_warning _________________
2020-02-02T08:55:26.6331296Z [gw1] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.plotting.test_converter.TestRegistration object at 0x7f97699062d0>
def test_registering_no_warning(self):
plt = pytest.importorskip("matplotlib.pyplot")
s = Series(range(12), index=date_range("2017", periods=12))
_, ax = plt.subplots()
# Set to the "warn" state, in case this isn't the first test run
register_matplotlib_converters()
with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
# GH#30588 DeprecationWarning from 2D indexing
> ax.plot(s.index, s.values)
E AssertionError: Did not see expected warning of class 'DeprecationWarning'
```
#### Full failures output
<details>
```
2020-02-02T08:55:26.6329275Z =================================== FAILURES ===================================
2020-02-02T08:55:26.6329476Z _________________ TestRegistration.test_registering_no_warning _________________
2020-02-02T08:55:26.6331296Z [gw1] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
2020-02-02T08:55:26.6331555Z
2020-02-02T08:55:26.6331736Z self = <pandas.tests.plotting.test_converter.TestRegistration object at 0x7f97699062d0>
2020-02-02T08:55:26.6331919Z
2020-02-02T08:55:26.6332094Z def test_registering_no_warning(self):
2020-02-02T08:55:26.6332272Z plt = pytest.importorskip("matplotlib.pyplot")
2020-02-02T08:55:26.6332458Z s = Series(range(12), index=date_range("2017", periods=12))
2020-02-02T08:55:26.6332629Z _, ax = plt.subplots()
2020-02-02T08:55:26.6332795Z
2020-02-02T08:55:26.6333232Z # Set to the "warn" state, in case this isn't the first test run
2020-02-02T08:55:26.6333431Z register_matplotlib_converters()
2020-02-02T08:55:26.6333618Z with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
2020-02-02T08:55:26.6333793Z # GH#30588 DeprecationWarning from 2D indexing
2020-02-02T08:55:26.6333984Z > ax.plot(s.index, s.values)
2020-02-02T08:55:26.6334129Z
2020-02-02T08:55:26.6334293Z pandas/tests/plotting/test_converter.py:71:
2020-02-02T08:55:26.6334479Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-02-02T08:55:26.6334637Z
2020-02-02T08:55:26.6334800Z self = <contextlib._GeneratorContextManager object at 0x7f9769b95250>
2020-02-02T08:55:26.6334986Z type = None, value = None, traceback = None
2020-02-02T08:55:26.6335129Z
2020-02-02T08:55:26.6335310Z def __exit__(self, type, value, traceback):
2020-02-02T08:55:26.6335477Z if type is None:
2020-02-02T08:55:26.6335637Z try:
2020-02-02T08:55:26.6335813Z > next(self.gen)
2020-02-02T08:55:26.6336232Z E AssertionError: Did not see expected warning of class 'DeprecationWarning'
2020-02-02T08:55:26.6336444Z
2020-02-02T08:55:26.6336863Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/contextlib.py:119: AssertionError
2020-02-02T08:55:26.6337076Z ___________________ TestRegistration.test_option_no_warning ____________________
2020-02-02T08:55:26.6337500Z [gw1] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
2020-02-02T08:55:26.6337681Z
2020-02-02T08:55:26.6337867Z self = <pandas.tests.plotting.test_converter.TestRegistration object at 0x7f9769c690d0>
2020-02-02T08:55:26.6338027Z
2020-02-02T08:55:26.6338188Z def test_option_no_warning(self):
2020-02-02T08:55:26.6338368Z pytest.importorskip("matplotlib.pyplot")
2020-02-02T08:55:26.6338539Z ctx = cf.option_context("plotting.matplotlib.register_converters", False)
2020-02-02T08:55:26.6338733Z plt = pytest.importorskip("matplotlib.pyplot")
2020-02-02T08:55:26.6338903Z s = Series(range(12), index=date_range("2017", periods=12))
2020-02-02T08:55:26.6339083Z _, ax = plt.subplots()
2020-02-02T08:55:26.6339242Z
2020-02-02T08:55:26.6339402Z # Test without registering first, no warning
2020-02-02T08:55:26.6339586Z with ctx:
2020-02-02T08:55:26.6339759Z # GH#30588 DeprecationWarning from 2D indexing on Index
2020-02-02T08:55:26.6339947Z with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
2020-02-02T08:55:26.6340121Z > ax.plot(s.index, s.values)
2020-02-02T08:55:26.6340414Z
2020-02-02T08:55:26.6340592Z pandas/tests/plotting/test_converter.py:105:
2020-02-02T08:55:26.6340763Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-02-02T08:55:26.6340928Z
2020-02-02T08:55:26.6341093Z self = <contextlib._GeneratorContextManager object at 0x7f976ac1aa90>
2020-02-02T08:55:26.6341279Z type = None, value = None, traceback = None
2020-02-02T08:55:26.6341440Z
2020-02-02T08:55:26.6341601Z def __exit__(self, type, value, traceback):
2020-02-02T08:55:26.6341764Z if type is None:
2020-02-02T08:55:26.6341941Z try:
2020-02-02T08:55:26.6342099Z > next(self.gen)
2020-02-02T08:55:26.6342547Z E AssertionError: Did not see expected warning of class 'DeprecationWarning'
2020-02-02T08:55:26.6342814Z
2020-02-02T08:55:26.6343228Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/contextlib.py:119: AssertionError
```
</details>
Saw this in two PRs that have gone through checks recently, and none of them touch anything that relates to the failing tests (#31562 #31559).
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MDExOlB1bGxSZXF1ZXN0MzY5OTkwMjQ5
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Fix docstring for to_markdown buf parameter
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| 2020-02-02T17:35:56Z
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- [ ] ~~closes #xxxx~~ (N/A)
- [ ] ~~tests added / passed~~ (N/A)
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] ~~whatsnew entry~~ (N/A)
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MDExOlB1bGxSZXF1ZXN0MzY5OTkyOTgw
| 31,566
|
TST: DataFrame.interpolate(axis='columns') throws exception while DataFrame.interpolate(axis=1) not (#25190)
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| 8
| 2020-02-02T09:53:06Z
| 2020-05-12T12:20:46Z
| 2020-05-12T12:18:35Z
|
CONTRIBUTOR
| null |
Make sure that DataFrame.interpolate allows setting having "columns" or "index" as the `axis` argument.
I included the `whatsnew` entry as well. However, since the bug was already fixed in v1.0.0, it might be reasonable to just drop it. I leave it to the reviewer's discretion, that's why I've put it in a separate commit.
- [x] closes #25190
- [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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MDExOlB1bGxSZXF1ZXN0MzY5OTk0NzQz
| 31,567
|
WEB: Changed whatsnew href from v0.25.0 to v1.0.0
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| 2020-02-02T15:22:33Z
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CONTRIBUTOR
| null |
- [ ] closes #31539
|
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Description of Plotting Backends added in the end.
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NONE
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- [ ] closes #xxxx
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MDExOlB1bGxSZXF1ZXN0MzcwMDAxNjc1
| 31,569
|
BUG accept and deprecate negative integer for max_colwidth
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| 2020-02-02T11:44:06Z
| 2020-02-03T10:38:51Z
| 2020-02-02T18:30:39Z
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CONTRIBUTOR
| null |
- [x] closes #31532
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| 558,685,247
|
MDU6SXNzdWU1NTg2ODUyNDc=
| 31,570
|
pandas.read_json(..., split='orient') builts DatetimeIndex instead of using 'index' attribute of json file
|
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[] | null | 2
| 2020-02-02T11:50:01Z
| 2021-03-26T05:25:07Z
| 2021-03-26T05:25:07Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pathlib import Path
import json
import pandas as pd
path_to_json = Path('path/to/json')
with open(path_to_json, 'r') as feature_file:
features = pd.read_json(feature_file, orient='split')
with open(path_to_json, 'r') as feature_file:
feats_= json.load(feature_file)
print(all(a == b for a, b in zip(feats_['index'],list(features.index))))
features.index = feats_['index'] # shouldn't be necessary
print(features.index)
```
#### Problem description
I serialised a dataframe into json using the split options.
When deserializing, a DatetimeIndex is built instead of using the 'index' attribute of my JSON file.
DatetimeIndex(['1970-02-03 08:19:30.607525111',
'1970-02-14 00:59:30.563649155',
'1970-02-13 07:19:30.563381155',
'1970-02-24 13:12:50.531381187',
'1970-02-08 10:26:10.515841203',
'1970-03-08 23:19:30.493779225',
'1971-08-16 21:55:04.791319239',
'1970-02-26 21:36:10.419351299',
'1970-02-23 23:29:30.419351299',
'1970-02-19 06:36:10.403239135',
...
'1970-01-31 23:21:55.267811637',
'1970-01-24 21:11:55.235221241',
'1970-03-14 10:11:55.143305301',
'1970-01-23 10:41:54.983117151',
'1970-02-15 09:58:34.929541359',
'1970-03-23 04:05:14.823177145',
'1970-03-13 09:31:54.807145225',
'1970-03-10 18:05:14.347205225',
'1970-01-20 07:21:54.347143145',
'1970-01-17 06:11:54.347269257'],
dtype='datetime64[ns]', length=5294, freq=None)
My JSON file:
[TCGA-2A-AAYO-01Z-00-DX1.A5BB3437-956A-4A42-AC28-1217B8016970.zip](https://github.com/pandas-dev/pandas/files/4144042/TCGA-2A-AAYO-01Z-00-DX1.A5BB3437-956A-4A42-AC28-1217B8016970.zip)
#### Expected Output
True,
Index(['28811_70607_525_111', '38051_70563_649_155', '37415_70563_381_155',
'47131_70531_381_187', '33207_70515_841_203', '57863_70493_779_225',
'51227_70479_1319_239', '49161_70419_351_299', '46637_70419_351_299',
'42573_70403_239_135',
...
'26761_15267_811_637', '20635_15235_221_241', '62575_15143_305_301',
'19393_14983_117_151', '39239_14929_541_359', '70131_14823_177_145',
'61687_14807_145_225', '59403_14347_205_225', '16681_14347_143_145',
'14047_14347_269_257'],
dtype='object', length=5294)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-28-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.0
numpy : 1.16.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : None
pytest : 4.5.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.5.0
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.43.1
</details>
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MDExOlB1bGxSZXF1ZXN0MzcwMDA0MTk0
| 31,571
|
DOC fix *_option() docstring
|
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| 8
| 2020-02-02T12:12:30Z
| 2020-02-03T10:39:34Z
| 2020-02-03T10:39:33Z
|
CONTRIBUTOR
| null |
- [x] closes #28780
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1NTg2OTgxMjQ=
| 31,572
|
what is the future of nanosecond in parquet?
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| 2020-02-02T13:40:56Z
| 2020-02-04T13:31:52Z
| 2020-02-03T12:28:24Z
|
CONTRIBUTOR
| null |
pandas 1.0.0
sample code as below:
```python
import pandas as pd
df = pd.DataFrame(data=range(123456000, 123456789), columns=["c1"])
df["c2"] = pd.Timestamp("2020-01-31") + pd.to_timedelta(df["c1"], "ns")
df.to_parquet('nanosecond-test.parquet',
# use_deprecated_int96_timestamps=True
)
```
without `use_deprecated_int96_timestamps=True`, an exception will raise:
```
...
File "C:\dev\.venv\lib\site-packages\pyarrow\parquet.py", line 474, in write_table
self.writer.write_table(table, row_group_size=row_group_size)
File "pyarrow\_parquet.pyx", line 1375, in pyarrow._parquet.ParquetWriter.write_table
File "pyarrow\error.pxi", line 78, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Casting from timestamp[ns] to timestamp[ms] would lose data: 1580428800123456000
```
my question is: will nanoseconds not be supported by parquet format in the future? or there will be some other way to convert nanoseconds between pandas and parquet?
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CI: Remove warning raising after new matplotlib release
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| 5
| 2020-02-02T13:45:49Z
| 2020-02-02T16:42:32Z
| 2020-02-02T16:42:20Z
|
MEMBER
| null |
- [ ] xref #31562
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
seems CI is broken after the new release of matplotlib
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MDExOlB1bGxSZXF1ZXN0MzcwMDI2MTYy
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TYP: Arraylike
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| 2020-05-01T14:57:54Z
| 2020-05-01T14:57:54Z
|
MEMBER
| null |
reopening #31518
cc @jbrockmendel @WillAyd
|
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pandas 1.0.0 read_csv() is broken use `open( buffering=0)` option.
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| 4
| 2020-02-02T16:11:58Z
| 2020-02-05T13:27:05Z
| 2020-02-05T13:27:05Z
|
CONTRIBUTOR
| null |
#### Code Sample
```python
import os
import pandas
import tempfile
fname = ""
with tempfile.NamedTemporaryFile(delete=False, mode="w+", encoding="shift-jis") as f:
f.write("てすと\nbar")
fname = f.name
print(fname)
try:
with open(fname,mode="r", encoding="shift-jis") as f:
result = pandas.read_csv(f)
print("read shift-jis")
print(result)
with open(fname,mode="r", encoding="shift-jis") as f:
result = pandas.read_csv(f,encoding="utf-8")
print("open shift-jis file and read_csv with encoding: utf-8")
print(result)
with open(fname,mode="rb") as f:
result = pandas.read_csv(f,encoding="shift-jis")
print("open binary with buffered and read_csv with encoding: shift-jis")
print(result)
with open(fname,mode="rb",buffering=0) as f:
result = pandas.read_csv(f,encoding="shift-jis")
print("open binary without burrered and read_csv with encoding: shift-jis")
print(result)
except Exception as e:
print(e)
os.unlink(fname)
```
#### Problem description
Pandas 1.0.0, this sample does not work. But pandas 0.25.3, this sample works fine.
Open file with buffering=0 option, f is `RawIOBase`. This case seems encoding option will be ignored.
https://github.com/pandas-dev/pandas/pull/30771/files#diff-0335ae9037e4eb4747749a9f94cffd32R641
https://github.com/pandas-dev/pandas/pull/30771/files#diff-777d7549579ddc0c6e67596ad87e0d27R1879
#### Expected Output
```
/tmp/tmpxxxxxxxxxxx
read shift-jis
てすと
0 bar
open shift-jis file and read_csv with encoding: utf-8
てすと
0 bar
open binary with buffered and read_csv with encoding: shift-jis
てすと
0 bar
open binary without burrered and read_csv with encoding: shift-jis
てすと
0 bar
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ja_JP.UTF-8
LOCALE : ja_JP.UTF-8
pandas : 1.0.0
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
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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MDExOlB1bGxSZXF1ZXN0MzcwMDI5Nzk4
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Backport PR #31573 on branch 1.0.x (CI: Remove warning raising after new matplotlib release)
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NONE
| null |
Backport PR #31573: CI: Remove warning raising after new matplotlib release
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REF: de-duplicate Period freq conversion code
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| 1
| 2020-02-02T16:42:54Z
| 2020-02-02T17:39:20Z
| 2020-02-02T17:34:38Z
|
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MDExOlB1bGxSZXF1ZXN0MzcwMDMyNTUz
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Backport PR #31482 on branch 1.0.x (BUG: objToJson.c - fix return value)
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| 2020-02-02T17:09:29Z
| 2020-02-02T18:32:03Z
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NONE
| null |
Backport PR #31482: BUG: objToJson.c - fix return value
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CLN: Fix ReadMe Badges
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| 3
| 2020-02-02T17:11:58Z
| 2020-02-02T18:31:43Z
| 2020-02-02T18:31:37Z
|
CONTRIBUTOR
| null |
Noticed when discussing https://github.com/pandas-dev/pandas/issues/31560 our badges are written in HTML + take up a lot of vertical space on our Readme.
My branch (a lot more compact vs master):
https://github.com/alimcmaster1/pandas/tree/mcmali-readme#pandas-powerful-python-data-analysis-toolkit
I've also added a NumFocus one :)
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|
Checking plot labels works locally but not during CI
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| 1
| 2020-02-02T17:28:53Z
| 2020-02-05T01:02:58Z
| 2020-02-05T01:02:58Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
xref #31207
If I modified the test as follows (with `import re` at the top of the file)
```python
with tm.assert_produces_warning(None):
fig, ax = self.plt.subplots()
ts.plot(ax=ax)
fig.canvas.draw()
labels = [i.get_text() for i in ax.get_xticklabels()]
# Extract H:M component, check first point is in correct timezone.
# NOTE: this test could be updated once GH 31548 is fixed,
# so that the last point is checked as well.
assert re.findall(r"[^:]?(\d{2}:\d{2})", labels[0])[0] == "00:00"
_check_plot_works(ts.plot)
```
then the test worked locally, but not during CI. Looking at the job details for linux py36_local, I could see that `labels` was
```
['01 00:01', ...1 00:51', ...]
```
I don't understand where that extra minute is coming from (nor can I understand why it only appears during CI).
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : 6e2d3aeb6179d40abfb68fdcc5b064a1d6b0bc59
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.26.0.dev0+2036.g6e2d3aeb6
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.0.0.post20200113
Cython : 0.29.14
pytest : 5.3.3
hypothesis : 5.1.5
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.3
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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Assigning column with DataFrame(dtype='category') doesn't preserve categorical dtype, but Series does
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| 2020-02-02T18:06:16Z
| 2021-07-27T05:46:09Z
| null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame([1,2,3])
df['col1'] = pd.DataFrame([1,2,3], dtype='category')
df['col2'] = pd.Series([1,2,3], dtype='category')
df.dtypes
```
#### Problem description
This returns
```
0 int64
col1 int64
col2 category
dtype: object
```
#### Expected Output
```
0 int64
col1 category
col2 category
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : d9f028dba97e9a3f21077ceeb96ca6552909c3b3
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.26.0.dev0+2031.gd9f028dba.dirty
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.0.0.post20200113
Cython : 0.29.14
pytest : 5.3.3
hypothesis : 5.1.5
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.3
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzcwMDQxODA0
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Dispatch IntervalIndex.argsort to IntervalArray
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| 2021-01-20T00:13:22Z
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MEMBER
| null |
cc @jschendel thoughts on if we can do better in the non-ascending/"quicksort" case?
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REF: Timestamp constructor tests
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Backport PR #31545: BUG&TST: df.replace fail after converting to new …
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CONTRIBUTOR
| null |
xref #31555
xref #31545
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REF: define _convert_to_indexer in Loc
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| 1
| 2020-02-02T20:18:45Z
| 2020-02-02T22:39:38Z
| 2020-02-02T22:22:17Z
|
MEMBER
| null |
_convert_to_indexer is overriden by iloc, so the version currently defined in LocationIndexer is only used in Loc. This moves it directly to Loc.
Also, since ix has been removed, we have an unnecessary level of the class hierarchy, so this merges NDFrameIndexer and LocationIndexer.
Follow-ups will move base class methods that are actually Loc-specific to Loc.
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MDU6SXNzdWU1NTg3NTc3ODA=
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|
pd.cut returning incorrect output in some cases
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| 2020-02-02T21:05:06Z
| 2020-04-03T05:48:41Z
| null |
MEMBER
| null |
(Below is from master)
```python
import numpy as np
import pandas as pd
arr = np.arange(10).astype(object)
arr[::2] = np.nan
print(arr)
# [nan 1 nan 3 nan 5 nan 7 nan 9]
result = pd.cut(arr, 2)
print(result)
# [NaN, (0.992, 5.0], NaN, (0.992, 5.0], NaN, (0.992, 5.0], NaN, (0.992, 5.0], NaN, (0.992, 5.0]]
# Categories (2, interval[float64]): [(0.992, 5.0] < (5.0, 9.0]]
print(result.unique())
# [NaN, (0.992, 5.0]]
# Categories (1, interval[float64]): [(0.992, 5.0]]
```
Using `cut` with an array of object dtype containing missing values seems to return the wrong intervals in some cases (e.g., in the example above only the first interval appears in the result). Actually, the only situation where I've been able to reproduce this problem is specifically when the NaN values are evenly spaced, which is strange.
Looks like the problem is due to `searchsorted` in `numpy`:
```python
import numpy as np
arr = np.array([1, 2, 3, 4, 5], dtype=object)
arr[::2] = np.nan
print(arr)
# [nan 2 nan 4 nan]
bins = np.array([1, 3, 5])
# Inserts into same position (incorrect)
bins.searchsorted(arr)
# array([0, 1, 0, 1, 0])
# Now inserts into different positions (correct)
bins.searchsorted(arr.astype(float))
# array([3, 1, 3, 2, 3])
np.__version__
# '1.17.5'
```
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MDExOlB1bGxSZXF1ZXN0MzcwMDYzMDA3
| 31,587
|
Backport PR #31440 on branch 1.0.x (BUG: Fix qcut for nullable integers)
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| 0
| 2020-02-02T22:19:37Z
| 2020-02-02T22:57:17Z
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NONE
| null |
Backport PR #31440: BUG: Fix qcut for nullable integers
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MDExOlB1bGxSZXF1ZXN0MzcwMDY1MDM5
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|
BUG: Handle object arrays with NaN in cut
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| 2020-02-13T22:57:03Z
| 2020-02-13T22:56:59Z
|
MEMBER
| null |
- [x] closes #31586
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzcwMDY4Mjc0
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REF: Move Loc-only methods to Loc
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| 5
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| 2020-02-08T01:41:43Z
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pip3 install pandas==1.0.0 fails to find source distribution on ARM host
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| 2020-02-02T23:20:24Z
| 2020-02-03T03:51:08Z
| 2020-02-03T03:51:08Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
$ time pip3 install pandas==1.0.0
Collecting pandas==1.0.0
Could not find a version that satisfies the requirement pandas==1.0.0 (from versions: 0.1, 0.2b0, 0.2b1, 0.2, 0.3.0b0, 0.3.0b2, 0.3.0, 0.4.0, 0.4.1, 0.4.2, 0.4.3, 0.5.0, 0.6.0, 0.6.1, 0.7.0rc1, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.8.0rc1, 0.8.0rc2, 0.8.0, 0.8.1, 0.9.0, 0.9.1, 0.10.0, 0.10.1, 0.11.0, 0.12.0, 0.13.0, 0.13.1, 0.14.0, 0.14.1, 0.15.0, 0.15.1, 0.15.2, 0.16.0, 0.16.1, 0.16.2, 0.17.0, 0.17.1, 0.18.0, 0.18.1, 0.19.0rc1, 0.19.0, 0.19.1, 0.19.2, 0.20.0rc1, 0.20.0, 0.20.1, 0.20.2, 0.20.3, 0.21.0rc1, 0.21.0, 0.21.1, 0.22.0, 0.23.0rc2, 0.23.0, 0.23.1, 0.23.2, 0.23.3, 0.23.4, 0.24.0rc1, 0.24.0, 0.24.1, 0.24.2, 0.25.0rc0, 0.25.0, 0.25.1, 0.25.2, 0.25.3)
No matching distribution found for pandas==1.0.0
```
#### Problem description
Unable to build pandas on a machine that has to build from source.
Here's my host information:
```
$ uname -a
Linux rockpro64 4.4.180 #1 SMP Fri May 31 17:07:08 EDT 2019 aarch64 GNU/Linux
```
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MDExOlB1bGxSZXF1ZXN0MzcwMDc1MzQx
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|
ENH: Enable indexing with nullable Boolean
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| 2020-02-23T14:55:01Z
| 2020-02-22T15:37:38Z
|
MEMBER
| null |
closes #31503
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I think from the discussion in https://github.com/pandas-dev/pandas/issues/31503 that this is something people want to allow.
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REF: parametrize indexing tests
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Import libindex in series.py
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| 2020-02-03T00:56:28Z
| 2020-02-03T00:59:59Z
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MEMBER
| null |
I think we may be missing an import in `series.py`. cc @jbrockmendel
|
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TST: troubleshoot npdev build
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| 31,595
|
`Series.isin` does not return all values in the column
|
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| 2020-02-03T01:52:49Z
| 2020-02-03T02:13:42Z
| 2020-02-03T02:13:15Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
print(pd.__version__)
df = pd.DataFrame({'t': np.arange(0.5, 2.0, 0.1)})
slxn = df.t.isin(np.arange(0.5, 2.0, 0.5))
slxn.sum()
```
#### Problem description
`df` is a data frame with a single column called `t`. The values of this column range from 0.5 to 2.0 in increments of 0.1.
There should be three values in `slxn` which are `True`, however the expression above yields 1.
#### Expected Output
```python
>>>slxn.sum()
3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.0.0
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
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 : None
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 558,855,794
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MDExOlB1bGxSZXF1ZXN0MzcwMTMxMTM3
| 31,596
|
BUG: read_csv used in file like object RawIOBase is not recognize encoding option
|
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| 7
| 2020-02-03T05:40:21Z
| 2020-02-05T20:56:20Z
| 2020-02-05T13:27:05Z
|
CONTRIBUTOR
| null |
- [x] closes #31575
- [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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MDExOlB1bGxSZXF1ZXN0MzcwMTQyMjgx
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REF: implement tests/indexes/objects/
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| 4
| 2020-02-03T06:20:01Z
| 2020-02-05T01:33:37Z
| 2020-02-05T01:31:09Z
|
MEMBER
| null |
Many of the tests in test_base are about `Index.__new__` cases that return a subclass. This is intended specifically for Index-only tests.
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MDExOlB1bGxSZXF1ZXN0MzcwMTQ1MTk2
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|
REF: simplify PeriodIndex.get_loc
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| 2
| 2020-02-03T06:29:49Z
| 2020-02-10T23:32:30Z
| 2020-02-09T20:41:34Z
|
MEMBER
| null |
This makes PeriodEngine follow patterns in DatetimeEngine, so soon we can have it subclass DatetimeEngine instead of Int64Engine, which will lead to more simplifications.
This also puts us within striking distance of sharing get_loc code
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MDExOlB1bGxSZXF1ZXN0MzcwMTQ2NjM0
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CLN: _convert_list_indexer only called by Loc
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MDU6SXNzdWU1NTg4OTc0MDE=
| 31,600
|
How to determine the severity of skewness(nanskew) such as highly skewed,moderately skewed,normally distributed,etc by interpreting the returned numerical values from the .skew() function.
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| 2020-02-03T07:36:17Z
| 2020-02-03T11:05:36Z
| 2020-02-03T11:05:29Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
The given documentation available below doesn't provide any specifics about the skewness results interpretation.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.skew.html
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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MDExOlB1bGxSZXF1ZXN0MzcwMTY1ODY5
| 31,601
|
Backport PR #31207 on branch 1.0.x (BUG: no longer raise user warning when plotting tz aware time series)
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| 2
| 2020-02-03T07:37:08Z
| 2020-02-03T10:57:46Z
| 2020-02-03T10:57:46Z
|
NONE
| null |
Backport PR #31207: BUG: no longer raise user warning when plotting tz aware time series
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MDExOlB1bGxSZXF1ZXN0MzcwMTkzOTIz
| 31,602
|
Manual backport 31594
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| 2020-02-03T08:47:10Z
| 2020-02-03T08:57:18Z
| 2020-02-03T08:57:07Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzcwMjEzNzk3
| 31,603
|
Backport PR #31569 on branch 1.0.x
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| 1
| 2020-02-03T09:32:56Z
| 2020-02-03T10:38:41Z
| 2020-02-03T10:38:41Z
|
CONTRIBUTOR
| null |
backport of #31569 in 1.0.x
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MDU6SXNzdWU1NTg5NjI4MTY=
| 31,604
|
DOC: pandas.Series.dt.day has incorrect docs
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| 2020-02-03T09:44:59Z
| 2020-02-03T12:41:37Z
| 2020-02-03T12:41:37Z
|
NONE
| null |
See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.day.html#pandas.Series.dt.day.
The documentation shows the description for dt.month and not dt.day.
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MDU6SXNzdWU1NTg5NjQ3Mzk=
| 31,605
|
Unexpected TypeError with groupby
|
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| 5
| 2020-02-03T09:48:30Z
| 2020-02-05T01:26:33Z
| 2020-02-05T01:26:33Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import numpy as np
import pandas as pd
def fct(group):
return group[1].values.flatten()
df = pd.DataFrame([('A', 1), ('A', 2), ('A', 3), ('B', 4), ('B', 5), ('C', np.nan),])
df.groupby(0).apply(fct)
```
#### Problem description
This code works with pandas 0.25.1 BUT NOT with pandas 1.0.0.
With pandas 1.0.0, the output is:
TypeError: copy() takes no keyword arguments
#### Expected Output
With pandas 0.25.1, the output is:
Series:
0
A [1.0, 2.0, 3.0]
B [4.0, 5.0]
C [nan]
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : fr_FR.cp1252
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 558,975,377
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MDExOlB1bGxSZXF1ZXN0MzcwMjI5MTMy
| 31,606
|
DOC: add back google analytics with the new doc theme
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| 2
| 2020-02-03T10:07:33Z
| 2020-02-03T12:35:38Z
| 2020-02-03T12:07:11Z
|
MEMBER
| null |
WIth the new theme, we didn't have google analytics anymore (how we had it before https://github.com/pandas-dev/pandas/pull/27662), so did a quick PR to add that to the theme with an option: https://github.com/pandas-dev/pydata-bootstrap-sphinx-theme/pull/84
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MDExOlB1bGxSZXF1ZXN0MzcwMjM5ODQ1
| 31,607
|
DOC: combine regressions in section in v1.0.1 whatsnew
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| 6
| 2020-02-03T10:30:05Z
| 2020-02-03T14:32:14Z
| 2020-02-03T14:05:38Z
|
MEMBER
| null |
Similarly how we did it for 0.24.x, gathering all regressions in the first section of the whatsnew (as those are the most important part of 1.0.1)
cc @TomAugspurger
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|
Backport PR #31571 on branch 1.0.x (DOC fix *_option() docstring)
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| 2020-02-03T10:39:43Z
| 2020-02-03T11:27:01Z
| 2020-02-03T11:27:01Z
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NONE
| null |
Backport PR #31571: DOC fix *_option() docstring
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MDU6SXNzdWU1NTg5OTYyMDk=
| 31,609
|
_check_for_bom at parsers.py remove all the fields behind BOM
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| 1
| 2020-02-03T10:42:54Z
| 2020-09-19T22:02:56Z
| 2020-09-19T22:02:56Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd # version 0.23.0, the same as 1.0.0
df=pd.read_csv("1.csv",sep=r",") # 1.csv treat first row as header, which start with BOM due to some unknow reason.
print(df.columns)
```
#### Problem description
_check_for_bom will not just remove BOM, but also remove all the fields except for the first one.
due to code:
```
first_row = first_row[0] // line 2571 at parsers.py @0.23.0
first_row_bom = first_row[0] // line 2758 at parsers.py @latest code at github
```
Solution:
Change line 2775 at parsers.py @latest code at github to
```
return [first_row_bom[1:]]+first_row[1:]
```
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MDExOlB1bGxSZXF1ZXN0MzcwMjQ4MDU2
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Backport PR #31594: TST: troubleshoot npdev build
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MEMBER
| null |
Backport of https://github.com/pandas-dev/pandas/pull/31594
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Backport PR #31606 on branch 1.0.x (DOC: add back google analytics with the new doc theme)
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NONE
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Backport PR #31606: DOC: add back google analytics with the new doc theme
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|
Groupby apply result shape depends on internal apply path
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| 7
| 2020-02-03T13:11:48Z
| 2020-07-16T14:17:44Z
| 2020-07-16T14:17:43Z
|
MEMBER
| null |
#### Problem description
The shape returned by groupby.apply currently depends on the path the internal apply takes (fast apply vs slow apply) which is opaque to the user. The following examples show the behaviour for two simple examples where the fast path returns the group key as index while the slow path returns the original index.
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'A': [0, 0, 1], 'b': range(3)})
def slow(group):
# slow apply because of check `result is input`, c.f. https://github.com/pandas-dev/pandas/blob/44782c0809e296a8e57b7f77d963e999c7e0f4a7/pandas/_libs/reduction.pyx#L494
return group
def fast(group):
return group.copy()
```
```
...: df.groupby("A").apply(slow)
...:
Out[3]:
A B
0 0 0
1 0 1
2 1 2
In [4]: df.groupby("B").apply(fast)
Out[4]:
A B
B
0 0 0 0
1 1 0 1
```
#### Expected Output
A transparent and consistent output data type. In particular, the behaviour should not be coupled to private, performance related internals.
#### Related issues
There are a lot of related issues which may or may not have the same root cause. Here an excerpt
#12824
#14538
#12977
#28662
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.0
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
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 : 7.12.0
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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BUG: Ensure same index is returned for slow and fast path in groupby.apply
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MEMBER
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This _fixes_ the internal check to be consistent with the slow apply path
- [x] closes #31612
- [x] closes #14927
closes #13056
- [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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MDExOlB1bGxSZXF1ZXN0MzcwMzE1NTcz
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REGR: fix non-reduction apply with tz-aware objects
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MEMBER
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Closes https://github.com/pandas-dev/pandas/issues/31505
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