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BUG: set_index screws up the dtypes on empty DataFrames
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| 8
| 2020-12-11T23:53:48Z
| 2020-12-13T17:41:40Z
| 2020-12-13T17:41:40Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
#30517 is similar in concept but relates to non-empty DataFrames, whereas the current bug is about empty ones.
- [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
d1 = pandas.DataFrame({'a': pandas.Series(dtype='datetime64[ns]'), 'b': pandas.Series(dtype='int64'), 'c': []})
d2 = d1.set_index(['a', 'b'])
assert (d1.loc[:, ['a', 'b']].dtypes == d2.index.to_frame().dtypes).all()
```
```
>>> d1.loc[:, ['a', 'b']].dtypes
a datetime64[ns]
b int64
dtype: object
>> d2.index.to_frame().dtypes
a object
b int64
dtype: object
```
#### Problem description
The dtype of the columns are silently changed when `.set_index` is called. This only happens when the DataFrame is empty, which suggests that this is an edge-case bug.
This is problematic because the behavior of `.set_index()` varies depending on whether the DataFrame has any rows.
#### Expected Output
The assertions should not fail.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.9.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.13-arch1-1
Version : #1 SMP PREEMPT Tue, 08 Dec 2020 12:09:55 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_FYL.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.1
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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MDExOlB1bGxSZXF1ZXN0NTM3NjA3OTc2
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BUG: read_csv not recognizing numbers appropriately when decimal is set
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| 6
| 2020-12-12T00:54:19Z
| 2021-01-03T18:47:35Z
| 2021-01-03T16:36:21Z
|
MEMBER
| null |
- [x] closes #31920
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The old regex was shorter but pretty buggy.
Let's assume ``decimal=","`` and ``thousands="."``
For example something like ``1a.2,3`` was interpreted as numeric by the regex and converted to ``1a2.3``, because the ``.`` was not escaped in the regex.
Also something like ``1,2,3`` was interpreted as numeric by the regex and converted to ``1.2.3``.
This one is not quite finished. We have to define a few thousand separator related things:
How strict would we want to be? Should ``1.2,3`` be interpreted as numeric and be converted to ``12.3`` or is only something like ``1.234,5`` relevant as thousands separator? C Engine validation is not strict
Additionally should ``,2`` be the number ``0.2``? -> Currently it is, because the C engine behaves the same
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REF: simplify maybe_casted_values
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| 2020-12-12T02:14:24Z
| 2020-12-12T23:53:23Z
| 2020-12-12T23:09:17Z
|
MEMBER
| null |
The meat of this function boils down to a call to algorithms.take.
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MEMBER
| null |
- [x] closes #38340
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Needs tests; the MaskedArray.isin method is not currently reached.
cc @jorisvandenbossche
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MDExOlB1bGxSZXF1ZXN0NTM4MDU0MTM2
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CLN: MultiIndex.union align with Index.union()
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MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Similar to MultiIndex.intersection()
Stumbled over a issue in the maybe_match_names function. Can not return plain None, because MultiIndex.rename can not handle this.
cc @jbrockmendel
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MDU6SXNzdWU3NjM4NzU1NjA=
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shift default excel read engine from xlrd to openpyxl
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| 2020-12-23T23:01:22Z
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NONE
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since v2.0.0, xlrd no longer supports excel files other than ".xls". manually specify pd.read_excel engine (to openpyxl and etc) is a bit annoying (otherwise pandas would complain about the missing xlrd module). is it possible to shift the default engine to sth more commonly used (eg. openpyxl )
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ENH: Allow numerical values in groupby to be within a certain tolerance
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NONE
| null |
#### Is your feature request related to a problem?
It is rather simple to group exact values together:
```python
# Example without noise:
import numpy as np
import pandas as pd
# Create input data without noise:
y = np.array([100, 100, 100, 100, 300, 300, 300, 300, 500, 500, 500, 500])
z = np.array([1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2])
time = np.arange(*y.shape)*0.1
df = pd.DataFrame(data=dict(y=y, z=z), index=time)
# Plot the groups:
df.y.groupby(by=df.y).plot(style="o")
```
yields
But once the values are a little noisy the default groupby function does not work:
```python
# Example with noise where groupby is successful:
import numpy as np
import pandas as pd
# Create input data with noise:
y = np.array([100, 100, 100, 100, 300, 300, 300, 300, 500, 500, 500, 500])
z = np.array([1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2])
noise = 1 + 0.1*(np.random.rand(*y.shape) - 0.5)
y = y * noise
z = z * noise
time = np.arange(*y.shape)*0.1
df = pd.DataFrame(data=dict(y=y, z=z), index=time)
# Plot the groups:
df.y.groupby(by=df.y).plot(style="o")
```
yields
#### Describe the solution you'd like
I want to add the optional arguments `atol=0 `and `rtol=0` (similar to `np.isclose`) to groupby.
Then `df.y.groupby(by=df.y, rtol=0.1).plot(style="o")` would yield the same groups as without any noise.
#### Describe alternatives you've considered
At the moment I have a function that does this. But it is not an elegant 1-liner. And at least I didn't think it was super obvious how to group like this either.
```python
# Example with noise with a workaround:
import numpy as np
import pandas as pd
# Create input data with noise:
y = np.array([100, 100, 100, 100, 300, 300, 300, 300, 500, 500, 500, 500])
z = np.array([1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2])
noise = 1 + 0.1*(np.random.rand(*y.shape) - 0.5)
y = y * noise
z = z * noise
time = np.arange(*y.shape)*0.1
df = pd.DataFrame(data=dict(y=y, z=z), index=time)
def groupby_isclose(series, atol=0, rtol=0):
# Sort values to make sure values are monotonically increasing:
s = series.sort_values()
# Calculate tolerance value:
tolerance = atol + rtol * s
# Calculate a monotonically increasing index that increase when the
# differnce between current and previous value changes:
by = s.diff().fillna(0).gt(tolerance).cumsum()
# s_old = s.shift().fillna(s)
# by = ((s - s_old).abs() > tolerance).cumsum().sort_index()
return by
# groupby values that are close:
by = groupby_isclose(df.y, rtol=0.1)
# Plot the groups:
df.y.groupby(by=by).plot(style="o")
```
yields
#### API breaking implications
`groupby_isclose` sorts the values to determine when values changes significantly. Unless there's another way of calculating this it might make the `sort` argument unusable.
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DOC: Clarify that DataFrame.sort_values is stable for sorting by multiple columns or labels
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| 8
| 2020-12-12T17:31:36Z
| 2020-12-22T20:34:34Z
| 2020-12-22T20:32:57Z
|
CONTRIBUTOR
| null |
- [x] closes #38357
- ~~tests added / passed~~
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- ~~whatsnew entry~~
From what I understand from the code, for `by` containing more than one entry, `pandas.DataFrame.sort_values` uses `pandas.core.sorting.lexsort_indexer`, which converts the columns to ordered `Categorical`s, gets grouped lexicographic group indices via `get_group_index`, compressed these via `compress_group_index` and then sorts using counting sort or mergesort via `get_group_index_sorter`. Both of these are stable, as explained in `get_group_index_sorter`'s docstring. Therefore, the sorting should be stable if `sort_values` is called with `len(by)>1`.
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MDExOlB1bGxSZXF1ZXN0NTM4MzMxODYz
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REGR: Assigning label with registered EA dtype raises
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| 2020-12-12T17:35:29Z
| 2021-01-02T14:16:01Z
| 2020-12-14T14:11:03Z
|
MEMBER
| null |
- [x] closes #38386
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Previous behavior in `io.parsers` depended on `is_bool_dtype("boolean")` erroneously raising `AttributeError`.
Test currently iterates over the names:
> ['category', 'interval', 'boolean', 'Float32', 'Float64', 'Int8', 'Int16', 'Int32', 'Int64', 'UInt8', 'UInt16', 'UInt32', 'UInt64', 'string']
It skips any EA where the name is a property; I don't see a good way to iterate over these in the test.
I only added a note about `is_bool_dtype` in the whatsnew as the `DataFrame.__setitem__` issue does not occur on 1.1.x. It didn't seem appropriate for any section besides other.
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REF: simplify maybe_upcast
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| 0
| 2020-12-12T17:39:07Z
| 2020-12-13T00:02:50Z
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|
MEMBER
| null |
It reduces to a 2-liner. It is currently used in 4 places, 2 of which I'm looking to remove in the near future. At that point it will make sense to inline those 2 lines and remove maybe_upcast entirely.
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CI: SQL tests on py38 slow build
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In #38344, we found that SQL tests were not run on py38 slow build. Currently, we are testing SQL on py37-locale and py37-coverage. If that is enough, we could move py38-slow to Azure. Otherwise, we could add more builds for SQL.
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MDExOlB1bGxSZXF1ZXN0NTM4NDY4Mjg4
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Fixed #38419 - BUG: set_index screws up the dtypes on empty DataFrames
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| 2
| 2020-12-12T20:53:22Z
| 2020-12-13T17:41:44Z
| 2020-12-13T17:41:40Z
|
CONTRIBUTOR
| null |
Fixes #38419 - BUG: set_index screws up the dtypes on empty DataFrames
- [ test_set_index_empty_dataframe] tests added / passed
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MDExOlB1bGxSZXF1ZXN0NTM4NDc1OTQ5
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BUG: read_csv raising TypeError for engine=c with names and parse_dates
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| null |
- [x] closes #33699
- [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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Added test_set_index_dtypes_on_empty_DataFrames to check dtypes after…
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| 2020-12-12T21:33:21Z
| 2020-12-12T22:11:04Z
| 2020-12-12T22:11:03Z
|
NONE
| null |
… using set_index on a DataFrame with empty Series that have dtype
- [x] closes #38419
- [x] tests added / passed test_set_index_dtypes_on_empty_DataFrames
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry. Added a test to check that dtypes don't change after using set_index on a DataFrame with empty Series
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BUG/API: allowing scalars in Categorical constructor
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| 2
| 2020-12-12T21:54:07Z
| 2020-12-23T18:44:29Z
| 2020-12-23T18:44:29Z
|
MEMBER
| null |
I expected this to raise
```
>> pd.Categorical("A")
['A']
Categories (1, object): ['A']
```
This gets listified inside the call to `maybe_infer_to_datetimelike` in `Categorical.__init__`
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|
REGR: DataFrame.shift(axis=1) raises TypeError when columns is CategoricalIndex
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| 1
| 2020-12-12T23:08:16Z
| 2020-12-17T13:46:45Z
| 2020-12-17T13:46:45Z
|
CONTRIBUTOR
| null |
A regression since 1.1:
```python
>>> ci = pd.CategoricalIndex(["a", "b"])
>>> df = pd.DataFrame([[1, 2], [3, 4]], index=ci, columns=ci)
>>> df.shift(axis=0) # ok
a b
a NaN NaN
b 1.0 2.0
>>> df.shift(axis=1) # not ok, worked in v1.1
TypeError: 'fill_value=0' is not present in this Categorical's categories
```
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| 4
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| 2020-12-13T19:19:45Z
| 2020-12-13T17:09:57Z
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NONE
| null |
Backport PR #38247: BUG: Limited available colors
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Backport PR #38331 on branch 1.2.x (BUG: first("1M") returning two months when first day is last day of month)
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| 2020-12-13T12:46:12Z
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NONE
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Backport PR #38331: BUG: first("1M") returning two months when first day is last day of month
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BUG: `to_json()/read_json()` can't correctly dump/load numbers requiring >15 digits of precision
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| 2020-12-13T01:22:40Z
| 2021-01-24T19:12:54Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example
Demonstration of the serialization issue:
```python
import pandas as pd
df = pd.DataFrame([0.9884619112598676])
js = df.to_json(double_precision=15) # raises exception if double_precision>=16 is set
print(f"orig: { df[0][0]} ({ df[0].dtypes})")
print(f"JSON: {js}")
```
Output:
```
orig: 0.9884619112598676 (float64)
JSON: {"0":{"0":0.988461911259868}}
```
Demonstration that deserialization silently disregards the last digit:
```
import numpy as np
js = '{"0":{"0":0.9884619112598676}}'
df = pd.read_json(js)
flt = np.float64("0.9884619112598676")
print(f" JSON: {js}")
print(f" numpy: {flt}")
print(f"Pandas: {df[0][0]} ({df[0].dtypes})")
```
Output:
```
JSON: {"0":{"0":0.9884619112598676}}
numpy: 0.9884619112598676
Pandas: 0.988461911259867 (float64)
```
#### Problem description
64-bit floating point numbers require up to 17 decimal digits to be fully round-tripped to textual representation and back (e.g., see https://stackoverflow.com/questions/6118231/why-do-i-need-17-significant-digits-and-not-16-to-represent-a-double/). Pandas' ujson-based decoder cuts them off at 15 digits, causing loss of precision. This introduces inconsistencies when a pandas dataframe is transmitted from point A to point B via different serializations vs. when it's not (e.g., in our case, this issue cropped up while validating a REST API for a near-Earth asteroid orbit computation service).
I traced this down to an old version of `ultrajsonenc.c` that's been imported to Pandas code and forces this cut. Modern versions don't seem to have this limitation (and do away with `double_precision` argument to `ujson.dump` alltogether) -- e.g., see [here](https://github.com/ultrajson/ultrajson/blob/master/lib/ultrajsonenc.c).
#### Expected Output
Modern `ujson` seems to handle this fine, keeping the required precision:
```
import ujson
print(ujson.__version__)
print(ujson.dumps(0.9884619112598676))
>>> 4.0.1
>>> 0.9884619112598676
```
A solution may be to update the version shipped with Pandas.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTM4Njc3OTE1
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BUG: add sorting_reamaining=True to avoid IndexError GH25831
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| 2021-02-11T01:39:57Z
| 2021-02-11T01:39:56Z
|
NONE
| null |
- [x] closes #25831
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: Subtracting two series with unordered index and all-nan index produces unexpected result
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| 7
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| 2021-09-16T02:37:38Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> import numpy as np
>>> a_index = pd.MultiIndex.from_tuples([
... (81.0, np.nan, '2018-06-01'),
... (81.0, np.nan, '2018-07-01'),
... (82.0, np.nan, '2018-07-01'),
... (82.0, np.nan, '2018-08-01'),
... (np.nan,np.nan, np.nan)],
... names=['id', 'sub_ix', 'data']
... )
>>> a_values = [25, 22, 20, 21, np.nan]
>>> b_index = pd.MultiIndex.from_tuples([
... (81.0, np.nan, '2018-06-01'),
... (np.nan, np.nan, np.nan),
... (81.0, np.nan, '2018-07-01'),
... (82.0, np.nan, '2018-07-01'),
... (82.0, np.nan, '2018-08-01')],
... names=['id', 'sub_ix', 'data']
... )
>>> b_values = [28.28, np.nan, 28.28, 25.25, 25.25]
>>> a = pd.Series(a_values, index=a_index)
>>> b = pd.Series(b_values, index=b_index)
>>> a
id sub_ix data
81.0 NaN 2018-06-01 25.0
2018-07-01 22.0
82.0 NaN 2018-07-01 20.0
2018-08-01 21.0
NaN NaN NaN NaN
dtype: float64
>>> b
id sub_ix data
81.0 NaN 2018-06-01 28.28
NaN NaN NaN NaN
81.0 NaN 2018-07-01 28.28
82.0 NaN 2018-07-01 25.25
2018-08-01 25.25
dtype: float64
>>> a - b
id sub_ix data
81.0 NaN 2018-06-01 -3.28
2018-07-01 NaN <-- this shouldn't be NaN, the index (81.0, NaN, 2018-07-01) exists in both `a` and `b` (it's just not ordered in `b`)
82.0 NaN 2018-07-01 -8.28 <-- also wrong, expected -5.25
2018-08-01 -4.25
NaN NaN NaN NaN
dtype: float64
>>> a - b.sort_index()
id sub_ix data
81.0 NaN 2018-06-01 -3.28
2018-07-01 -6.28 <-- expected value
82.0 NaN 2018-07-01 -5.25
2018-08-01 -4.25
NaN NaN NaN NaN
dtype: float64
```
#### Problem description
When combining two series with both the same index and with an all-nan index row at different positions, the result of the arithmetic operation (`+`, `-`, `/`) is not as expected. The issue can be worked around by sorting both indices (series.sort_index). I tried a different example with unordered indices, but without the all-nan index row and the result is as expected (so it's not an issue of the unsorted indices).
```
id sub_ix data
81.0 NaN 2018-07-01 25
2018-06-01 22
82.0 NaN 2018-07-01 20
2018-08-01 21
dtype: int64
>>> b
id sub_ix data
81.0 NaN 2018-06-01 1
2018-07-01 2
82.0 NaN 2018-07-01 3
2018-08-01 4
dtype: int64
>>> a - b
id sub_ix data
81.0 NaN 2018-06-01 21
2018-07-01 23
82.0 NaN 2018-07-01 17
2018-08-01 17
dtype: int64
```
#### Expected Output
Operands should be aligned as per index (despite all nan-rows in the index).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 65f0463d364d77470289309a51d56eb54b2d254a
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.11-200.fc33.x86_64
Version : #1 SMP Tue Nov 24 18:18:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.2.0.dev0+1441.g65f0463d3
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : 0.29.21
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : 1.8.6
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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BENCH/REF: parametrize CSV benchmarks on engine
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| 2020-12-17T01:43:28Z
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- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
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The diff in basic PR implementing the `pyarrow`-based CSV engine (#38370) is quite big. A part of that PR is a small refactor of the CSV I/O benchmarks such that they take `engine` as a parameter. Most of that refactor is not dependent on having the pyarrow engine so I'm spinning it off here to de-bloat #38370.
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| 2020-12-13T09:10:20Z
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| 2020-12-13T11:16:20Z
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MEMBER
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This would be an on-demand bot to run pre-commit checks on a PR, which can be triggered by commenting
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MDExOlB1bGxSZXF1ZXN0NTM4OTUyODA5
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BUG: first("2M") returning incorrect results
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| 2020-12-13T13:37:15Z
| 2020-12-19T17:21:21Z
| 2020-12-19T02:14:08Z
|
MEMBER
| null |
- [x] closes #29623
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This would fix #29623 for offsets with n > 1
cc @simonjayhawkins
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ENH: regex replace capacities are missing from pd.Categorical.replace()
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| null |
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
pd.Categorical doesn't implement `regex=`, preventing a generalisable solution at #32542.
#### Describe the solution you'd like
As far as I can tell, the best solution would be an refactor of .replace() methods and logic.
#### API breaking implications
-
#### Describe alternatives you've considered
Parts of the logic from array_algos.replace.replace_regex() can be copied over to pd.Categorical, but that's not desirable. Therefore the suggestion for the refactor.
#### Additional context
This came up while working with the suggestion [here](https://github.com/pandas-dev/pandas/pull/32542#discussion_r532945181).
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MDExOlB1bGxSZXF1ZXN0NTM4OTU3MjY0
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|
Revert "BUG: first("1M") returning two months when first day is last day of month"
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| 2020-12-13T14:08:12Z
| 2020-12-13T15:45:08Z
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MEMBER
| null |
Reverts pandas-dev/pandas#38331
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BUG: to_parquet() / read_parquet() cannot round-trip datetime.timezone.utc
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| 2020-12-13T14:20:07Z
| 2020-12-13T14:44:42Z
| 2020-12-13T14:22:33Z
|
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.
- [ ] (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 datetime
import pandas as pd
utc = datetime.timezone.utc
now = lambda: datetime.datetime.now(utc)
df = pd.DataFrame([[now(), "a"], [now(), "b"]], columns=("date", "val"))
df.set_index('date', inplace=True)
df.to_parquet('/tmp/a.parquet')
d2 = pd.read_parquet('/tmp/a.parquet')
# Kaboom
```
#### Problem description
Current behaviour:
* Silently writes a Parquet file that cannot be re-read
* Failure appears to be a missing check during write, however error confusing appears during read
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-24-generic
Version : #26-Ubuntu SMP Thu Nov 14 01:33:18 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_GB.UTF-8
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.5
numpy : 1.19.0
pytz : 2020.4
dateutil : 2.8.0
pip : 20.3.1
setuptools : 40.8.0
Cython : 0.29.20
pytest : 6.2.0
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.20
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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Inconsistent SettingWithCopyWarning behavior
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| 2021-08-14T22:21:58Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
def example_1():
df = pd.DataFrame(dict(x=np.arange(100), y=np.arange(100)))
df_ = df[pd.Series([True] * 50 + [False] * 50, name="y")]
df_.x *= 2
def example_2():
df = pd.DataFrame(dict(x=np.arange(100), y=np.arange(100)))
df_ = df[df.y < 50]
df_.x *= 2
def example_3():
df = pd.DataFrame(dict(x=np.arange(100), y=np.arange(100)))
df_ = df[(df.y < 50) | (np.random.random(100) < 0.5)]
df_.x *= 2
```
#### Problem description
Examples 2 and 3 above show (example 1 doesn't):
```
/usr/local/lib/python3.8/site-packages/pandas/core/generic.py:5170: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
```
I'm failing to see how 2 is different to 1 and how the behavior in 2 is so sticky than even adding a random mask isn't enough to stop it.
I believe that 1 and 2 should behave the same way and, if not, this extremely surprising behavior should at least be clarified in the documentation.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.2.1
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.20
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3NjU0NjIxNDA=
| 38,451
|
DOC: name not defined in section "2.14.1 Basic plotting: plot"
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| 10
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| 2021-01-05T12:57:09Z
| 2021-01-05T12:57:09Z
|
CONTRIBUTOR
| null |
In the [pdf version of document](https://pandas.pydata.org/docs/pandas.pdf), section "User Guide -> Visualization -> 2.14.1 Basic plotting: plot", page 588, there are `name 'pd' (or 'ts') is not defined` problems.
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MDExOlB1bGxSZXF1ZXN0NTM4OTc3NDIx
| 38,452
|
ENH: Improve error message when names and usecols do not match for read_csv with engine=c
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| 1
| 2020-12-13T16:21:43Z
| 2020-12-13T18:29:29Z
| 2020-12-13T17:37:04Z
|
MEMBER
| null |
- [x] closes #29042
- [x] tests added / passed This case was already covered with differing error messages. I adjusted the message accordingly
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
BUG: read_csv names argument inconsisten between c and python engine
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| 2020-12-13T17:14:21Z
| 2020-12-13T17:14:33Z
| 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.
---
**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
After #38445 there is another inconsistency left to adress.
```python
s = """a, b, c, d
1,2,3,4,
5,6,7,8,"""
pd.read_csv(io.StringIO(s), header=0, names=['A', 'B', 'C', 'D', "E"], engine="c")
pd.read_csv(io.StringIO(s), header=0, names=['A', 'B', 'C', 'D', "E"], engine="python")
```
#### Problem description
The bug is caused from the differing lenghts of the header and the names argument.
This returns
```
A B C D E
0 1 2 3 4 NaN
1 5 6 7 8 NaN
```
for the ``c`` engine and raises
```
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2020.3/scratches/scratch_4.py", line 323, in <module>
print(pd.read_csv(io.StringIO(s), header=0, names=['A', 'B', 'C', 'D', "E"], engine="python"))
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 605, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 457, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 814, in __init__
self._engine = self._make_engine(self.engine)
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 1045, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 2303, in __init__
) = self._infer_columns()
File "/home/developer/PycharmProjects/pandas/pandas/io/parsers.py", line 2692, in _infer_columns
raise ValueError(
ValueError: Number of passed names did not match number of header fields in the file
Process finished with exit code 1
```
#### Expected Output
Would expect that both return the same and python engine does not raise.
#### Output of ``pd.show_versions()``
<details>
master
</details>
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MDU6SXNzdWU3NjU1NDI2MDQ=
| 38,454
|
ENH: option to export df to Stata dataset with value labels
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| 4
| 2020-12-13T17:19:59Z
| 2021-09-10T08:49:19Z
| 2021-09-10T08:49:19Z
|
NONE
| null |
#### Is your feature request related to a problem?
One feature of Stata (.dta) datasets is that it's possible to have categorical columns with numerical values and string value labels. At the moment, `df.to_stata` doesn't have an option to create this kind of columns.
#### Describe the solution you'd like
It would be nice if `df.to_stata` could have a `value_labels` option that accepts a dictionary mapping numerical values to value labels.
#### API breaking implications
.
#### Describe alternatives you've considered
.
#### Additional context
.
|
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MDExOlB1bGxSZXF1ZXN0NTM4OTkwMDA4
| 38,455
|
REF: Separate values-casting from Block.astype
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| 2
| 2020-12-13T17:44:13Z
| 2020-12-14T00:36:08Z
| 2020-12-13T23:41:35Z
|
MEMBER
| null |
This separates Block._astype from Block.astype. Block._astype only requires Block.values, the idea being that we can roll it into astype_nansafe before long.
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DOC: update wording about when xlrd engine can be used
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| 15
| 2020-12-13T18:18:03Z
| 2020-12-23T17:04:25Z
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|
CONTRIBUTOR
| null |
See https://github.com/pandas-dev/pandas/issues/38424#issuecomment-744043899.
|
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STYLE: use types_or in pre-commit
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| 4
| 2020-12-13T23:51:27Z
| 2020-12-16T00:46:54Z
| 2020-12-16T00:46:50Z
|
CONTRIBUTOR
| null |
- [x] closes #38022
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #38141 on branch 1.2.x (BUG: do not stringify file-like objects)
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| 0
| 2020-12-14T01:10:30Z
| 2020-12-14T10:24:50Z
| 2020-12-14T10:24:49Z
|
NONE
| null |
Backport PR #38141: BUG: do not stringify file-like objects
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REF: simplify _sanitize_column
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| 1
| 2020-12-14T04:08:07Z
| 2020-12-14T18:01:21Z
| 2020-12-14T17:59:46Z
|
MEMBER
| null |
It gets called indirectly from `iloc._setitem_with_indexer`, which we're trying to simplify
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| 765,863,275
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MDExOlB1bGxSZXF1ZXN0NTM5MTE0MDk3
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REF: roll DatetimeBlock.astype into Block._astype
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| 0
| 2020-12-14T04:13:09Z
| 2021-11-20T23:21:31Z
| 2020-12-14T14:18:16Z
|
MEMBER
| null |
Anything past this requires getting a handle on #36153.
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| 765,873,656
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|
TYP: DataFrame.to_gbq, DataFrame.to_html (easy: copy-paste from format module)
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| 1
| 2020-12-14T04:28:12Z
| 2020-12-14T15:43:46Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Spinning off from #38416
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Update 10min.rst
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| 8
| 2020-12-14T12:59:39Z
| 2020-12-15T04:31:51Z
| 2020-12-14T20:38:03Z
|
CONTRIBUTOR
| null |
Added Links for Matplotlib Close along with a line explaining what it does.
I had faced difficulty when I read it for the first time and had to google search what close does
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU3NjY0NTI0Mzg=
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|
BUG: resample apply is actually aggregate
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[] | null | 8
| 2020-12-14T13:43:54Z
| 2021-09-11T10:46:24Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
this piece of code does it, I don't really understand why these things are OK to be the same function here
https://github.com/pandas-dev/pandas/blob/7ffbf1a5ab6b0126c7efa5991e7db505979a752c/pandas/core/resample.py#L308
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame([[1,2],[3,4], [5,6]], index=pd.date_range("2020-01-30", "2020-02-01"))
df.resample("1M").apply(lambda df: df.mean().mean())
```
#### Problem description
current behavior calls the function on all columns, in all groups, as in aggregate, but is should on all dfs of the groups as in apply
#### Expected Output
```
2020-01-31 2.5
2020-02-29 5.5
Freq: M, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.1-1.el7.elrepo.x86_64
Version : #1 SMP Sun Jul 14 08:15:04 EDT 2019
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : 4.6.11
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Backport PR #38427 on branch 1.2.x (REGR: Assigning label with registered EA dtype raises)
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NONE
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Backport PR #38427: REGR: Assigning label with registered EA dtype raises
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API: EAs should default _is_numeric / _is_boolean
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| 5
| 2020-12-14T14:16:13Z
| 2020-12-15T08:50:46Z
| 2020-12-15T00:01:57Z
|
CONTRIBUTOR
| null |
I think we should define
```
@property
def _is_numeric(self) -> bool:
return False
@property
def _is_boolean(self) -> bool:
return False
```
on the EA base class.
Just makes less fragile checks on all EAs as we can assume the attributes exist
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CLN: astype_nansafe require dtype object
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MDExOlB1bGxSZXF1ZXN0NTM5Njk4NjEz
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CLN: unnecessary checks
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MDExOlB1bGxSZXF1ZXN0NTM5NzI1Njcy
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TYP : DataFrame.(merge, join) core.reshape.merge.(merge, ...) (easy)
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- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
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- [ ] whatsnew entry
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BUG: .loc selection using str datetime label on dataframe with MultiIndex containing datetime column returns series
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| 3
| 2020-12-14T19:35:51Z
| 2020-12-14T20:20:12Z
| 2020-12-14T20:08:44Z
|
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
df = pd.DataFrame({
'a': ['20201214 12:00:00', '20201214 12:00:01'],
'b': [1, 2],
'c': [3446, 2342],
'd': [10, 11]
})
df['a'] = pd.to_datetime(df['a'])
df = df.set_index(['a', 'b'])
# expected this to return a scalar, but returns Series:
df.loc[('2020-12-14 12:00:01', 2), 'c']
a b
2020-12-14 12:00:01 2 2342
Name: c, dtype: int64
# returns a scalar as expected:
df.loc[(pd.Timestamp('2020-12-14 12:00:01'), 2), 'c']
2342
```
#### Problem description
I was confused that `.loc[(str, int), str]` returned a series with one value rather than a single scalar (`int` in this case). Research showed that `.at` requires the exact type, so I tried that with `.loc` and got the expected results which is confusing.
#### Expected Output
```python
# returns a scalar:
df.loc[('2020-12-14 12:00:01', 2), 'c']
2342
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.3
numpy : 1.18.1
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 51.0.0.post20201207
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 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Added Link to Conda Website in the README
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| null |
Added Link to Conda Website in the README for easy access especially for first-timers
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: fixes for assert_frame_equal check_freq argument
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CONTRIBUTOR
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- [ ] closes #xxxx
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This is just a minor documentation change to fix the `versionadded` tag on the `check_freq` argument to `assert_frame_equal` and also to add a description of this backwards incompatible chage to the 1.1.0 Whats New (as recommended in https://github.com/pandas-dev/pandas/issues/35570#issuecomment-674064533).
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BUG: require arraylike in infer_dtype_from_array
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New Pandas Assertion Error
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NONE
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I am running into an assertion error when using group_by.min() which was never a problem when I was using the older version of pandas
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| 2020-12-17T02:18:57Z
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follow on PR for #37823
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- [ ] closes #xxxx
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This benchmark behavior changed after https://github.com/pandas-dev/pandas/pull/38372
Additionally, exploring if its feasible to always run the benchmarks to catch these changes earlier.
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NONE
| null |
- [ ] closes #34231
- [ ] adds skip_blank_columns functionality
- [ ] updates API documentation
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MEMBER
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closes #38483
Found in the npdev build.
Noted in pytest changlog: https://docs.pytest.org/en/stable/changelog.html#deprecations
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- [ ] closes #xxxx
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- [x] passes `black pandas`
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- [ ] whatsnew entry
https://github.com/pandas-dev/pandas/pull/38096 originally did not do the trick. This change worked for me locally.
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REF: share .astype code for astype_nansafe + TDA.astype
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This changes TDI.astype and TDA.astype in the cast with no-NAs to return float64 instead of int64, matching Series behavior.
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DOC: get_indexer returns non-matching with -1 positional
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| 2021-11-06T02:45:42Z
| 2021-11-06T02:45:42Z
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NONE
| null |
[An answer on my recent SO post](https://stackoverflow.com/a/65085536/4107349) used `get_indexer`, which worked great, but I soon learned if the indexer doesn't find a match it seems to use the last entry without raising an error:
```
df = pd.DataFrame({"ID": [6, 2, 4],
"to ignore": ["foo", "whatever", "idk"],
"value": ["A", "B", "asdf"],
})
df2 = pd.DataFrame({"ID_number": [1, 2, 3, 4, 5, 6],
"A": [0.91, 0.42, 0.85, 0.84, 0.81, 0.88],
"B": [0.11, 0.22, 0.45, 0.38, 0.01, 0.18],
})
df2 = df2.set_index('ID_number')
df['new_col'] = df2.values[df2.index.get_indexer(df['ID']), df2.columns.get_indexer(df['value'])]
```
I presumed the row with "asdf" would have raised an error, not returned the value for "B". This was problematic because I unwittingly processed data incorrectly and I enjoy being employed.
My interpretation of the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.get_indexer.html) was that if `method` was not supplied it would be "default: exact matches only". Supplying `method = 'default'` and `tolerance = 0` was also not accepted when not using another method.
Also, I'm no serious programmer so I have likely misunderstood something and am only trying to help. Please feel free to correct / tell me to go away
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CI: --strict option in pytest failing builds
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| 3
| 2020-12-15T00:13:07Z
| 2020-12-15T00:58:27Z
| 2020-12-15T00:14:35Z
|
CONTRIBUTOR
| null |
seems we are passing `--strict` to pytest, we must have picked up an updated version and this is now raising: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=50126&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=ce687173-08c6-5301-838d-71b2dda24510
temporarily can just fix the pytest version or just remove this
cc @pandas-dev/pandas-core
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BUG: Segmentation Fault when read_table is given None
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| 2020-12-15T00:43:31Z
| 2020-12-15T00:41:26Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
~~- [ ] I have confirmed this bug exists on the latest version of pandas.~~ (Not applicable)
~~- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.~~ (Not applicable)
---
**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 pyarrow.parquet as pq
pq.read_table(None)
```
#### Problem description
The above snippet will produce a Segmentation Fault, which is highly undesirable. The reason I discovered this, was I had a function that was supposed to return a filepath, but on my first iteration I forgot to return. Thus, when I ran my module with `pq.read_table(generate_fp())`, it produced a Segmentation Fault.
#### Expected Output
Ideally this will raise an `ValueError`, indicating to the user that `None` is an invalid source/filepath.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.4.0-56-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.8.0
pip: 19.2.3
setuptools: 41.2.0
Cython: 0.29.13
numpy: 1.17.4
scipy: 1.1.0
pyarrow: 0.17.0
xarray: None
IPython: 7.14.0
sphinx: 3.1.2
patsy: None
dateutil: 2.6.1
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.6.1
numexpr: 2.6.9
feather: None
matplotlib: 3.1.1
openpyxl: 2.5.4
xlrd: 1.0.0
xlwt: None
xlsxwriter: 1.1.2
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.16
pymysql: None
psycopg2: 2.8.5 (dt dec pq3 ext lo64)
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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DOC: Missing ipython block in user_guide/indexing.rst
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| 2020-12-15T01:24:00Z
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MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/user_guide/indexing.html
#### Documentation problem
Looks like a missing ipython block:
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DOC: Fix missing ipython block in user_guide/indexing.rst
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MEMBER
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- [x] closes #38485
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REF: simplify maybe_upcast_putmask
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| 38,488
|
BUG: wrong result in rolling mean\sum (possibly related with cython implementation)
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| 2020-12-15T04:06:49Z
| 2021-01-01T21:54:54Z
| 2020-12-15T18:06:56Z
|
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
# Your code here
df = pd.Series(np.random.random(20), dtype='float64')
df[:10] *= 1e6
df[-10:] = 0
df
Out[124]:
0 235266.408832
1 979278.480747
2 242108.917259
3 513980.266425
4 44316.957433
5 408655.731842
6 490360.015982
7 855399.526047
8 321975.011810
9 831082.388210
10 0.000000
11 0.000000
12 0.000000
13 0.000000
14 0.000000
15 0.000000
16 0.000000
17 0.000000
18 0.000000
19 0.000000
dtype: float64
df.rolling(6).mean()
Out[126]:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 2.682618e+05
6 2.723245e+05
7 2.937347e+05
8 3.589816e+05
9 2.737199e+05
10 2.429527e+05
11 2.411146e+05
12 1.258042e+05
13 9.726441e+04
14 1.032524e+04
15 2.546585e-11
16 2.546585e-11
17 2.546585e-11
18 2.546585e-11
19 2.546585e-11
dtype: float64
df.rolling(6).sum()
Out[127]:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 1.609571e+06
6 1.633947e+06
7 1.762408e+06
8 2.153889e+06
9 1.642319e+06
10 1.457716e+06
11 1.446688e+06
12 7.548251e+05
13 5.835865e+05
14 6.195146e+04
15 1.527951e-10
16 1.527951e-10
17 1.527951e-10
18 1.527951e-10
19 1.527951e-10
dtype: float64
```
#### Problem description
if the head is substantially larger than the 0s in the tail, then rolling mean\sum will not generate correct results. I roughly looked into the source code and guess maybe it's related to cython "rolling_mean"
#### Expected Output
0 in row 15 to 20, not 2.546585e-11
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-112-generic
Version : #113-Ubuntu SMP Thu Jul 9 23:41:39 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : None
pymysql : 0.9.2
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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MDU6SXNzdWU3NjcxNTg5ODU=
| 38,489
|
BUG: read_csv: C and python engines have inconsistent behavior when `parse_dates=[]`
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| 2
| 2020-12-15T04:20:29Z
| 2020-12-15T20:15:45Z
| 2020-12-15T20:14:15Z
|
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 io
import pandas as pd
s = """some_header
2020-01-01 01:00:00"""
print('python,parse_dates=False')
print(pd.read_csv(io.StringIO(s), parse_dates=False, engine='python', delimiter=' '))
print('python,parse_dates=[]')
print(pd.read_csv(io.StringIO(s), parse_dates=[], engine='python', delimiter=' '))
print('c, parse_dates=False')
print(pd.read_csv(io.StringIO(s), parse_dates=False, engine='c', delimiter=' '))
print('c, parse_dates=[]')
print(pd.read_csv(io.StringIO(s), parse_dates=[], engine='c', delimiter=' '))
```
#### Problem description
In the code above, the first three `read_csv` calls succeed, but the last one crashes:
```
Traceback (most recent call last):
File "bug.py", line 16, in <module>
print(pd.read_csv(io.StringIO(s), parse_dates=[], engine='c', delimiter=' '))
File "/home/hong/wsrc/dax-api/.tox/dev/lib64/python3.8/site-packages/pandas/io/parsers.py", line 688, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/hong/wsrc/dax-api/.tox/dev/lib64/python3.8/site-packages/pandas/io/parsers.py", line 460, in _read
data = parser.read(nrows)
File "/home/hong/wsrc/dax-api/.tox/dev/lib64/python3.8/site-packages/pandas/io/parsers.py", line 1198, in read
ret = self._engine.read(nrows)
File "/home/hong/wsrc/dax-api/.tox/dev/lib64/python3.8/site-packages/pandas/io/parsers.py", line 2200, in read
values = self._maybe_parse_dates(values, i, try_parse_dates=True)
File "/home/hong/wsrc/dax-api/.tox/dev/lib64/python3.8/site-packages/pandas/io/parsers.py", line 2261, in _maybe_parse_dates
if try_parse_dates and self._should_parse_dates(index):
File "/home/hong/wsrc/dax-api/.tox/dev/lib64/python3.8/site-packages/pandas/io/parsers.py", line 1569, in _should_parse_dates
j = self.index_col[i]
TypeError: 'NoneType' object is not subscriptable
```
#### Expected Output
The content of the CSV being printed.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1160.6.1.el7.x86_64
Version : #1 SMP Wed Oct 21 13:44:38 EDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 50.3.2
Cython : None
pytest : 6.2.0
hypothesis : None
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
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REF: share astype code in MaskedArray
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| 2
| 2020-12-15T04:36:05Z
| 2020-12-22T00:11:44Z
| 2020-12-21T23:47:06Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @jorisvandenbossche i dont know if this is the optimal way to do this, but it seems like there is a lot of share-ability here.
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Updated README
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| 4
| 2020-12-15T04:52:09Z
| 2020-12-16T17:31:23Z
| 2020-12-16T16:18:40Z
|
CONTRIBUTOR
| null |
I have updated the README with the following -
1. Added Links for Conda, Cython and AQR.
2. Added a brief description for what all the Dependencies do so people who are unfamiliar with them can atleast get a basic idea about them
3. Corrected PyPI spelling in one instance
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BUG: CategoricalIndex.reindex fails when Index passed with labels all in category
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| 1
| 2020-12-15T05:59:55Z
| 2020-12-18T11:23:46Z
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|
CONTRIBUTOR
| null |
- [x] closes #28690
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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TYP: func argument to DataFrame.apply, DataFrame.applymap, core.apply.frame_apply
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MEMBER
| null |
- [ ] closes #xxxx
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- [ ] whatsnew entry
Spinning off from #38416, xref https://github.com/pandas-dev/pandas/pull/38416#discussion_r542911035
|
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TST: don't use global fixture in the base extension tests
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| 2
| 2020-12-15T08:00:28Z
| 2020-12-15T15:01:31Z
| 2020-12-15T14:57:09Z
|
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/37867. In the discussion about using this fixture, I forgot that we should not use global pandas fixtures in those tests, but only the ones that are defined in the local /pandas/tests/extension/conftest.py, otherwise this doesn't work for downstream projects (eg https://github.com/geopandas/geopandas/issues/1735). For this case it's easier to simply not use a fixture, I think (but still share the common definition of the list)
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Major Performance regression of df.groupby(..).indices
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| 2020-12-15T08:57:00Z
| 2021-01-03T18:44:14Z
| 2021-01-03T18:44:14Z
|
NONE
| null |
I'm experiencing major performance regressions with pandas=1.1.5 versus 1.1.3
Version 1.1.3:
```
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.19.0
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)] on win32
In[2]: import time
... : import numpy as np
... : import pandas as pd
... : pd.__version__
Out[2]: '1.1.3'
In[3]: numel = 10000000
... : df = pd.DataFrame(dict(a=np.random.rand(numel), b=np.random.randint(0,4000, numel)))
... : start = time.time()
... : groupby_indices = df.groupby('b').indices
... : time.time() - start
Out[3]: 0.46085023880004883
```
Version 1.1.5:
```
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.19.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.19.0
Python 3.7.9 | packaged by conda-forge | (default, Dec 9 2020, 20:36:16) [MSC v.1916 64 bit (AMD64)] on win32
In[2]: import time
... : import numpy as np
... : import pandas as pd
... : pd.__version__
Out[2]: '1.1.5'
In[3]: numel = 10000000
... : df = pd.DataFrame(dict(a=np.random.rand(numel), b=np.random.randint(0,4000, numel)))
... : start = time.time()
... : groupby_indices = df.groupby('b').indices
... : time.time() - start
Out[3]: 57.36550998687744
```
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Backport PR #38486 on branch 1.2.x (DOC: Fix missing ipython block in user_guide/indexing.rst)
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NONE
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Backport PR #38486: DOC: Fix missing ipython block in user_guide/indexing.rst
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Backport PR #38478 on branch 1.2.x (CI: Use --strict-markers for pytest 6.0.2)
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NONE
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Backport PR #38478: CI: Use --strict-markers for pytest 6.0.2
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|
ENH: Default Excel engine only compatible with XLS files -> replace by new one
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[] | null | 1
| 2020-12-15T13:02:35Z
| 2020-12-15T16:41:59Z
| 2020-12-15T16:41:46Z
|
NONE
| null |
#### Is your feature request related to a problem?
With the release of [xlrd 2.0.0 the last 11th of December](https://github.com/python-excel/xlrd/blob/master/CHANGELOG.rst#200-11-december-2020), they drop support for any file format except the (very old) XLS. As this is the default Excel engine for Pandas, it is likely there will be a cascade of complains popping up very soon.
#### Describe the solution you'd like
Replace the default `xlrd` engine by another engine compatible with modern file formats, maybe [openpyxl](https://openpyxl.readthedocs.io/en/stable/).
#### API breaking implications
Shouldn't have any... I think.
#### Describe alternatives you've considered
There is a bunch of other possible alternative engines: http://www.python-excel.org
#### Additional context
None
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DOC: Fix incorrect doc string for infer_dtype (#38375)
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| 2020-12-15T14:30:52Z
| 2020-12-16T02:14:22Z
| 2020-12-16T02:14:19Z
|
CONTRIBUTOR
| null |
- [x] closes #38375
- ~~tests added / passed~~
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- ~~whatsnew entry~~
A minor fix to https://github.com/pandas-dev/pandas/issues/38375 correcting the output of an example in `infer_dtype` doc string
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Backport PR #38494 on branch 1.2.x (TST: don't use global fixture in the base extension tests)
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Backport PR #38494: TST: don't use global fixture in the base extension tests
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Backport PR #38480: CI: Supress moto server logs in tests
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BUG: Incomplete join with categorical MultiIndex
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| 1
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| 2021-01-13T13:25:52Z
| 2021-01-13T13:25:52Z
|
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 we join two dataframes with similar MultiIndexes, where one level is a `CategoricalDtype`, some of the rows are not properly joined:
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame()
df['major'] = pd.Series(list('AAAAB'))
df['minor'] = pd.Series(
list('YXYXX'), dtype=pd.CategoricalDtype(['Y', 'X']))
df['value'] = pd.Series([1, 2, 3, 4, 5])
df.set_index(['major', 'minor'], inplace=True)
df1 = df.iloc[:2]
df2 = df.iloc[2:]
df1.join(df2, lsuffix='_left', rsuffix='_right')
```
#### Problem description
The code sample above gives us `df1`:
```python
value
major minor
A Y 1
X 2
```
and `df2`:
```python
value
major minor
A Y 3
X 4
B X 5
```
If we left-join the two dataframes index-on-index, we expect the first two rows of `df2` to be matched against the two rows of `df1` (see `Expected Output` below). Instead, we find that the second row of `df2` is ignored:
```python
value_left value_right
major minor
A Y 1 3.0
X 2 NaN
```
##### Clues
I am way out of my depth here, but these are some things I noticed while trying to find a minimal example:
* **The issue goes away if we define the categories for the `minor` index level in alphabetical order (i.e. `dtype=pd.CategoricalDtype(['X', 'Y'])` instead of `['Y', 'X']`).**
* The `ordered` parameter of `CategoricalDtype` has no influence.
* The third row in `df2`, although dropped during the left-join, is crucial. Joining `df1` and `df2.iloc[:2]` yields the expected output.
* The issue does not occur when we perform an `outer` join
* I was not able to reproduce this issue with a single-level index.
* I was able to reproduce this bug w/ v1.1.3 as well but have not checked with any earlier versions.
* *(Unrelated: Boy was I excited to finally give something back to pandas - even if it's just a bug report - after all that it's done for me over the years!)*
#### Expected Output
```python
value_left value_right
major minor
A Y 1 3
X 2 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.9.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.9.13-arch1-1
Version : #1 SMP PREEMPT Tue, 08 Dec 2020 12:09:55 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.3.1
setuptools : 51.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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| 2020-12-30T13:49:09Z
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CONTRIBUTOR
| null |
Reference Issues/PRs
Fixes: #38287
Implemented changes:
Changed the docstring of all the methods using np.sort as it now includes one additional stable algorithm
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REG: DataFrame.shift with axis=1 and CategoricalIndex columns
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- [x] closes #38434
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REF: require listlike in maybe_cast_to_datetime
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WIP: CI: Supress urllib3 tests in pytest
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- [ ] closes #xxxx
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xref https://github.com/pandas-dev/pandas/pull/38480, there are some remaining urllib3 logs coming from somewhere. Testing if this gets rid of those.
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REF: handle ravel inside astype_nansafe
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MDExOlB1bGxSZXF1ZXN0NTQwNjAwMjM1
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BUG: Series(dt64, dtype="Sparse[object]")
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- [ ] closes #xxxx
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As a side effect of fixing astype_nansafe, we can rip some code out of Block._astype.
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BUG: Series[td64].astype("string").astype("timedelta64[ns]") raises
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MEMBER
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```
tdi = pd.timedelta_range("1 Day", periods=3)
ser = pd.Series(tdi)
>>> ser.astype("string")
0 1 days
1 2 days
2 3 days
dtype: string
>>> ser.astype("string").astype(ser.dtype)
[...]
ValueError: Could not convert object to NumPy timedelta
```
We have a round-trip test for string -> dt64 in test_astype_roundtrip, should amend that to do td64 when this is fixed
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BUG: None and np.nan behave differently for .replace()
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| 2021-08-14T22:25:45Z
| 2021-08-14T22:25:44Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
`pip freeze | grep pandas -> pandas==1.1.5`
- [ ] (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
In [9]: df = pd.DataFrame([1, None, np.nan])
In [10]: df
Out[10]:
0
0 1.0
1 NaN
2 NaN
In [11]: df.replace(1, None)
Out[11]:
0
0 1.0
1 NaN
2 NaN
In [12]: df.replace(1, np.nan)
Out[12]:
0
0 NaN
1 NaN
2 NaN
```
#### Problem description
This is a problem because when creating a dataframe, both None and np.nan are converted into NaN. Therefore, one expect replacing will also treat them equivalently.
#### Expected Output
```
df = pd.DataFrame([1, None, np.nan])
df.replace(1, None)
0
0 1.0
1 NaN
2 NaN
```
should return the same as
```
df = pd.DataFrame([1, None, np.nan])
df.replace(1, np.nan)
0
0 NaN
1 NaN
2 NaN
```
#### 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: MultiIndex.equals returning incorrectly True when Indexes contains NaN
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| null |
- [x] xref #38439
- [x] tests added / passed
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- [x] whatsnew entry
Depending on the expected behavior of align, this may have hidden an additional bug in align in the referenced issue.
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df.melt() column name 'variable'
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NONE
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- [x] closes #37716
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This is my first time contributing to the open source project. Although I didn't make a significant change, I saw a bug report and asked to just raise a warning instead of fixing it. That's what I did and returned original DataFrame instead of returning wrong frame.
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CI: un-xfail
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| 2021-01-21T14:50:03Z
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MEMBER
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No idea why this stopped failing, but not going to look a gift horse in the mouth.
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MDExOlB1bGxSZXF1ZXN0NTQwNzg0NjA2
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ENH: support none in header list #37625
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| 2020-12-15T23:33:28Z
| 2021-02-11T01:32:24Z
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NONE
| null |
closes #37625
tests passed
read_excel funtion can now accept header={'Sheet1': 0, 'Sheet2': None} as dictionary with header for each sheet.
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API: CategoricalDtype.__eq__ with categories=None stricter
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| 2020-12-16T02:35:21Z
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MEMBER
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- [ ] closes #xxxx
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xref #37929
This will allow us to remove special-casing for Categorical in Block._astype, Index.astype, and Categorical.astype.
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BUG: read_excel forward-filling MI names
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| 1
| 2020-12-16T02:58:53Z
| 2021-01-01T20:02:32Z
| 2021-01-01T19:45:28Z
|
MEMBER
| null |
- [x] closes #34673
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Added test with OP's issue as a sheet in the style of existing sheets in `tests/io/data/excel/testmultiindex.*`
(the same as the "both_name" sheet, but with the cell below the MI name empty)
New sheet looks like:
<img width="393" alt="Screen Shot 2020-12-15 at 9 50 02 PM" src="https://user-images.githubusercontent.com/37011898/102298700-961dbf00-3f1f-11eb-9c98-034268ad9d68.png">
In master without this change, the empty cell was being filled with the index name `ilvl2`
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| 768,431,842
|
MDExOlB1bGxSZXF1ZXN0NTQwODczNzgx
| 38,518
|
REF: use astype_nansafe in Index.astype
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| 0
| 2020-12-16T04:09:54Z
| 2020-12-16T23:09:11Z
| 2020-12-16T22:32:24Z
|
MEMBER
| null |
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