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- [x] tests added / passed
(no new tests added)
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This resolves the issue of `pd.DataFrame(dict)` or `pd.DataFrame.from_dict(dict)` not preserving the order of the index specified in `dict`.
This required fixing up tests that relied on alphabetical ordering. The changes to these tests also test for this new preserved ordering; let me know if I should add an additional explicit test. | {
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} | 21 | 2019-04-16T23:09:29Z | 2019-07-10T23:01:51Z | 2019-07-10T19:16:55Z | CONTRIBUTOR | null | - [x] xref #25725
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Now that Python2 is being removed, ``.iteritems`` should be deprecated, and ``.items`` should be used instead.
EDIT: This PR ended up not deprecating .iteritems, but only change its usage internally with using .items | {
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} | 3 | 2019-04-17T08:03:47Z | 2019-04-17T12:51:00Z | 2019-04-17T12:45:50Z | MEMBER | null | WIP towards getting sphinx doc build warning free | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26118 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26118/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26118/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26118/events | https://github.com/pandas-dev/pandas/issues/26118 | 434,223,869 | MDU6SXNzdWU0MzQyMjM4Njk= | 26,118 | Should assert_frame_equal allow for a 'None' diff when there is actually a difference? | {
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] | closed | false | null | [] | null | 7 | 2019-04-17T11:07:25Z | 2020-09-01T02:03:52Z | 2020-03-08T18:24:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
From this module used in dataframes' comparison [here](https://github.com/pandas-dev/pandas/blob/master/pandas/util/testing.py#L829).
#### Problem description
First of all, great job, I am excited to be using Pandas and thanks for everything! :)
While asserting for dataframe equality in PyTest using PyCharm I get a message that some values are different (100.0%) but the diff is None (diff = None). This is rather confusing and since there is no obvious difference between these values I struggle to find it.
#### Expected Output
If diff is none, I would expect for `assert_frame_equal` method to not throw an exception and return true. If the assertion is false and an exception is being raised I would expect it to always have some diff to show. If for any reason, having a "diff" is not feasible, I would expect this to be injected in the output message.
#### Output of ``pd.show_versions()``
<details>
Latest
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26119/events | https://github.com/pandas-dev/pandas/issues/26119 | 434,235,829 | MDU6SXNzdWU0MzQyMzU4Mjk= | 26,119 | Modify index of Series derived from Dataframe affects index of it's column. | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 3 | 2019-04-17T11:38:58Z | 2020-09-22T04:18:10Z | null | NONE | null | ```python
import pandas as pd
from datetime import datetime
import pytz
dt_from = datetime(2019, 3, 30, 0, tzinfo=pytz.utc)
dt_to = datetime(2019, 3, 30, 4, tzinfo=pytz.utc)
idx = pd.date_range(dt_from, dt_to, freq='H')
df = pd.DataFrame(data={
'a': {i: v for i, v in zip(idx, range(idx.size))},
'b': {i: v+10 for i, v in zip(idx, range(idx.size))}}
)
>>> df
a b
2019-03-30 00:00:00+00:00 0 10
2019-03-30 01:00:00+00:00 1 11
2019-03-30 02:00:00+00:00 2 12
2019-03-30 03:00:00+00:00 3 13
2019-03-30 04:00:00+00:00 4 14
s = df['a'] # df.loc[:, 'a'] instead of df['a'] works the same
s.index = s.index.tz_convert('Europe/Prague')
>>> s
2019-03-30 01:00:00+01:00 0
2019-03-30 02:00:00+01:00 1
2019-03-30 03:00:00+01:00 2
2019-03-30 04:00:00+01:00 3
2019-03-30 05:00:00+01:00 4
Name: a, dtype: int64
# good
>>> df
a b
2019-03-30 00:00:00+00:00 0 10
2019-03-30 01:00:00+00:00 1 11
2019-03-30 02:00:00+00:00 2 12
2019-03-30 03:00:00+00:00 3 13
2019-03-30 04:00:00+00:00 4 14
# good
>>> df['a']
2019-03-30 01:00:00+01:00 0
2019-03-30 02:00:00+01:00 1
2019-03-30 03:00:00+01:00 2
2019-03-30 04:00:00+01:00 3
2019-03-30 05:00:00+01:00 4
Name: a, dtype: int64
# BAD INDEX
>>> df['b']
2019-03-30 00:00:00+00:00 10
2019-03-30 01:00:00+00:00 11
2019-03-30 02:00:00+00:00 12
2019-03-30 03:00:00+00:00 13
2019-03-30 04:00:00+00:00 14
Name: b, dtype: int64
# good
```
#### Workaround
```python
s = df['a'].copy()
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.14.6
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.5
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
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] | closed | false | null | [] | null | 1 | 2019-04-17T12:49:29Z | 2019-04-17T20:04:48Z | 2019-04-17T20:03:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Creating pandas>Series with timedeltas as index - 1 microsecond step:
import numpy as np
import pandas as pd
time = np.arange(0,1, 1e-6)
time_index = pd.to_timedelta(time, unit='s')
values = np.ones(len(time_index))
sig = pd.Series(data=values, index=time_index)
# shift signal for 1000 samples
sig_shifted_2 = sig.shift(1000)
print("Sample 0: {}".format(sig_shifted_2[0]))
print("Sample 999: {}".format(sig_shifted_2[999]))
print("Sample 1000: {}".format(sig_shifted_2[1000]))
print("Sample 1001: {}".format(sig_shifted_2[1001]))
```
#### Problem description
When signal is shifted for 1000 samples, what is expected is that samples from 0 to 999 have nan value, while sample 1000 should have value form index 0 on non-shifted signal. For 10 microsecond step it works like expected, but with 1 microsecond step index 1000 still has nan value, as can be seen on the output:
Version 0.24.2 is used
#### Expected Output
Output if 10 microsecond step is used
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.0
pip: 10.0.1
setuptools: 38.5.1
Cython: 0.28.2
numpy: 1.14.3
scipy: 1.0.1
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.6
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 2.1.1
openpyxl: 1.7.0
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/26121 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26121/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26121/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26121/events | https://github.com/pandas-dev/pandas/issues/26121 | 434,288,542 | MDU6SXNzdWU0MzQyODg1NDI= | 26,121 | to_parquet and read_parquet are not symmetric if a column contains python lists | {
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"default": false,
"description": "parquet, feather",
"... | open | false | null | [] | null | 1 | 2019-04-17T13:38:00Z | 2020-04-19T19:24:44Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> df = pd.DataFrame({'A': ["this is a list".split(), "so is this one".split()]})
>>> df.to_parquet('tmp.parquet', 'pyarrow')
>>> df2 = pd.read_parquet('tmp.parquet', 'pyarrow')
>>> print("Type:", type(df2.A[0]), "Value:", repr(df2.A[0]))
Type: <class 'numpy.ndarray'> Value: array(['this', 'is', 'a', 'list'], dtype=object)
```
#### Problem description
If a pandas DataFrame has one or more columns where the cells are python objects containing a list of strings, then converting the DataFrame to parquet and back using pyarrow as the back end will result in the columns being converted to numpy arrays of strings. This can cause unexpected problems later in the code. For example trying to write the dataframe to a SQL database with `DataFrame.to_sql` will blow up since it does not know how to handle numpy arrays.
While investigating this I found that using fastparquet as the back end for both saving and loading worked OK, but saving with fastparquet and loading with pyarrow changed the lists into byte strings containing the repr of the list, i.e.
```
>>> df.to_parquet('tmp.parquet', 'fastparquet')
>>> df2 = pd.read_parquet('tmp.parquet', 'pyarrow')
>>> print("Type:", type(df2.A[0]), "Value:", repr(df2.A[0]))
Type: <class 'bytes'> Value: b'["this", "is", "a", "list"]'
```
Also saving with pyarrow and loading with fastparquet throws an exception:
```
>>> df.to_parquet('tmp.parquet', 'pyarrow')
>>> df2 = pd.read_parquet('tmp.parquet', 'fastparquet')
Traceback (most recent call last):
File "<ipython-input-22-b30bd30928a4>", line 1, in <module>
df2 = pd.read_parquet('tmp.parquet', 'fastparquet')
File "/home/dave/anaconda3/envs/tca/lib/python3.6/site-packages/pandas/io/parquet.py", line 282, in read_parquet
return impl.read(path, columns=columns, **kwargs)
File "/home/dave/anaconda3/envs/tca/lib/python3.6/site-packages/pandas/io/parquet.py", line 211, in read
return parquet_file.to_pandas(columns=columns, **kwargs)
File "/home/dave/anaconda3/envs/tca/lib/python3.6/site-packages/fastparquet/api.py", line 421, in to_pandas
check_column_names(self.columns + list(self.cats), columns, categories)
File "/home/dave/anaconda3/envs/tca/lib/python3.6/site-packages/fastparquet/util.py", line 92, in check_column_names
if set(arg) - set(columns):
TypeError: unhashable type: 'dict'
```
N.B. I am not sure which of these are bugs in pandas and which are bugs in pyarrow & fastparquet. If this is not the right place for this then let me know and I will raise tickets on the respective projects.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 3.8.2
pip: 18.1
setuptools: 40.4.3
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.2.1
pyarrow: 0.13.0
xarray: None
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: 3.8.0
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.8.1
s3fs: None
fastparquet: 0.3.0
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-04-17T17:16:50Z | 2019-05-07T01:59:20Z | 2019-05-07T01:59:20Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
arr = ['March 1, 2018 12:00:00+0400', 'March 1, 2018 12:00:00+0500', '20100240']
result = pd.to_datetime(arr, errors='coerce')
```
`>>> result`:
`Index(['March 1, 2018 12:00:00+0400', 'March 1, 2018 12:00:00+0500', '20100240'], dtype='object')`
#### Problem description
In `to_datetime` docstring:
`- If 'coerce', then invalid parsing will be set as NaT`
So.. it seems to me, that this is unexpected behaviour
#### Expected Output
`>>> result`:
`Index([2018-03-01 12:00:00+04:00, 2018-03-01 12:00:00+05:00, NaT], dtype='object')`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.0
pip: 19.0.3
setuptools: 39.0.1
Cython: 0.29.3
numpy: 1.16.0
scipy: 1.2.1
pyarrow: None
xarray: 0.11.3
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: 0.9.3
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.2.0
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 2 | 2019-04-17T18:21:20Z | 2020-04-25T20:44:16Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
for dtype in (np.float32, np.int32, np.bool):
s = pd.SparseSeries([1, 0], dtype=dtype)
print('original:', s.dtype)
print('reindexed:', s.reindex([0, 1, 2]).dtype)
```
**Output**
```
original: Sparse[float32, nan]
reindexed: Sparse[float64, nan]
original: Sparse[int32, 0]
reindexed: Sparse[float64, 0]
original: Sparse[bool, False]
reindexed: Sparse[float64, False]
```
#### Problem description
The output sparse series is always of type ```Sparse[float64]``` instead of the dtype passed in.
This looks to be a regression from v0.23.4. Perhaps it has to do with the new SparseArray rework in v0.24.x.
#### Expected Output
Ideally the dtype would not be lost. Although that wasn't exactly true in v0.23.4, the sparse float dtypes were not upcasted. My use case for casting to a sparse dtype is to save space, so the conversion to float64 breaks things.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-16-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: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.12.0
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-04-17T18:29:51Z | 2020-09-03T03:06:19Z | 2020-09-03T03:06:19Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df.to_csv(path_or_buf='gs://mybucket/my_file', encoding='utf8')
```
#### Problem description
Instead of expected UTF-8 file is stored in latin-1 on Windows. I can also see that current implementation of method ```pandas.io.gcs.get_filepath_or_buffer``` ignores encoding argument.
I can submit PR if are open to it.
#### Expected Output
Uploaded file encoded in UTF8 even on Windows.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-17-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: None
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: 0.9.0
pandas_datareader: None
gcsfs: 0.2.1
</details>
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} | 4 | 2019-04-17T19:09:01Z | 2019-05-09T12:36:35Z | 2019-05-09T12:36:35Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import numpy as np; import pandas as pd
In [2]: np.__version__, pd.__version__
Out[2]: ('1.16.2', '0.25.0.dev0+421.g8b60ebb359')
In [3]: idx = pd.Index([10, 20, 11]); idx
Out[3]: Int64Index([10, 20, 11], dtype='int64')
In [4]: np.min(idx)
---------------------------------------------------------------------------
TypeError: min() got an unexpected keyword argument 'out'
In [5]: np.max(idx)
---------------------------------------------------------------------------
TypeError: max() got an unexpected keyword argument 'out'
```
Note that other numpy functions work fine:
```python
In [6]: np.sum(idx), np.prod(idx), np.argmax(idx), np.argmin(idx)
Out[6]: (41, 2200, 1, 0)
```
And `np.min` and `np.max` work on other pandas data structures:
```python
In [7]: s = pd.Series([10, 20, 11])
In [8]: np.min(s), np.max(s)
Out[8]: (10, 20)
In [9]: df = pd.DataFrame({'A': [10, 20, 11], 'B': [3, 1, 4]})
In [10]: np.min(df)
Out[10]:
A 10
B 1
dtype: int64
In [11]: np.max(df)
Out[11]:
A 20
B 4
dtype: int64
```
#### Problem description
Both `np.min` and `np.max` raise a `TypeError` when applied to a `pd.Index`. Using `pd.Index.min` and `pd.Index.max` is more idiomatic for pandas, but using the numpy equivalent functions should work, as they do for other pandas data structures.
#### Expected Output
I'd expect `np.min(pd.Index)` and `np.max(pd.Index)` to be equivalent to `pd.Index.min` and `pd.Index.max`, as is the case with other pandas data structures.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 8b60ebb3598881571b390276d2c75ec7c7a95212
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.25.0.dev0+421.g8b60ebb359
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.6.3
Cython: 0.28.2
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.6.0
xarray: 0.9.6
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml.etree: 3.8.0
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 1 | 2019-04-17T22:30:02Z | 2019-04-18T04:31:35Z | 2019-04-18T04:31:35Z | NONE | null | ```
df = pd.DataFrame({'A': [1,2],
'B': [3,4]})
df.columns.name = 'Test'
df.to_html(index = False)
#Output
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>Test</th>
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<th></th>
<td>1</td>
<td>3</td>
</tr>
<tr>
<th></th>
<td>2</td>
<td>4</td>
</tr>
</tbody>
</table>
```
#### Problem description
I recently updated one of my projects from 0.23.4 to 0.24.1 and the html output of ```df.to_html(index = False)``` changed when there is a column name (ie. one generated from when you create a pivot table).
Previously, ```df.to_html(index = False)``` would drop the column name, however now it creates an empty column with the column name.
I've also confirmed reverting to 0.23.4 fixes the issue.
#### Expected Output
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>A</th>
<th>B</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>3</td>
</tr>
<tr>
<td>2</td>
<td>4</td>
</tr>
</tbody>
</table>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 3.8.0
pip: 10.0.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.16.0
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.9
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml.etree: 4.3.0
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"color": "d93f0b... | closed | false | null | [] | null | 10 | 2019-04-17T23:27:05Z | 2019-11-21T20:55:56Z | 2019-11-21T20:55:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from pandas._libs.algos import backfill
getattr(backfill, "__kwdefaults__", None)
```
Run with `-X faulthandler` to get more info.
#### Problem description
Trying to access `__kwdefaults__`, either by `getattr` or `hasattr`, segfaults the Python process. This occurs at least on macOS when pandas is installed from PyPI using `pip install pandas`. If it is instead installed via `pip install pandas --no-binary :all:`, then it does not segfault. There may be other functions which do this, but this is the first we encounter.
This impacts our Python language server as it uses the `inspect` library to examine libraries without Python source. The code sample above is a minimal repro, but in reality it's being called by `inspect.getfullargspec()` (which eventually does [this](https://github.com/python/cpython/blob/3.7/Lib/inspect.py#L1837)). When it segfaults, our process crashes (and on some OSs like macOS produces a visible popup as the OS is tracking these sorts of crashes). See: Microsoft/python-language-server#740
cython/cython#1470 looks to be related, and would be fixed in Cython 0.29.6, so maybe a version bump is all that would be required. Given I can do `--no-binary` to do the compilation locally, it may be that what is building for PyPI is older than what happens on my machine.
#### Expected Output
Anything, just not crash. Here's what macOS's crash reporter says:
<details>
Process: Python [80749]
Path: /usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/Resources/Python.app/Contents/MacOS/Python
Identifier: Python
Version: 3.7.3 (3.7.3)
Code Type: X86-64 (Native)
Parent Process: zsh [72384]
Responsible: Python [80749]
User ID: 501
Date/Time: 2019-04-17 16:18:22.448 -0700
OS Version: Mac OS X 10.14.4 (18E226)
Report Version: 12
Bridge OS Version: 3.0 (14Y674)
Anonymous UUID: 5A957B3E-4E8F-3DE2-C606-5B11FE48E6DD
Sleep/Wake UUID: 02FDA72B-8D53-471B-80AE-6514E0B386FB
Time Awake Since Boot: 23000 seconds
Time Since Wake: 2200 seconds
System Integrity Protection: disabled
Crashed Thread: 0 Dispatch queue: com.apple.main-thread
Exception Type: EXC_BAD_ACCESS (SIGSEGV)
Exception Codes: KERN_INVALID_ADDRESS at 0x0000000000000000
Exception Note: EXC_CORPSE_NOTIFY
Termination Signal: Segmentation fault: 11
Termination Reason: Namespace SIGNAL, Code 0xb
Terminating Process: exc handler [80749]
VM Regions Near 0:
-->
__TEXT 000000010a32a000-000000010a32c000 [ 8K] r-x/rwx SM=COW /usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/Resources/Python.app/Contents/MacOS/Python
Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 algos.cpython-37m-darwin.so 0x0000000116045520 __pyx_pf_6pandas_5_libs_5algos_600__defaults__ + 32
1 algos.cpython-37m-darwin.so 0x0000000116195ed0 __Pyx_CyFunction_get_kwdefaults + 48
2 org.python.python 0x000000010a352587 getset_get + 58
3 org.python.python 0x000000010a379c2f _PyObject_GenericGetAttrWithDict + 180
4 org.python.python 0x000000010a379b20 _PyObject_LookupAttr + 166
5 org.python.python 0x000000010a3d6d66 builtin_getattr + 141
6 org.python.python 0x000000010a34d6f2 _PyMethodDef_RawFastCallKeywords + 495
7 org.python.python 0x000000010a34cc8e _PyCFunction_FastCallKeywords + 44
8 org.python.python 0x000000010a3e1db2 call_function + 636
9 org.python.python 0x000000010a3dac35 _PyEval_EvalFrameDefault + 6594
10 org.python.python 0x000000010a3e26d3 _PyEval_EvalCodeWithName + 1867
11 org.python.python 0x000000010a3d91d0 PyEval_EvalCode + 51
12 org.python.python 0x000000010a40779b run_mod + 54
13 org.python.python 0x000000010a4067c5 PyRun_FileExFlags + 163
14 org.python.python 0x000000010a405e6b PyRun_SimpleFileExFlags + 263
15 org.python.python 0x000000010a41e9b0 pymain_main + 5367
16 org.python.python 0x000000010a41f088 _Py_UnixMain + 56
17 libdyld.dylib 0x00007fff5dead3d5 start + 1
Thread 1:
0 libsystem_kernel.dylib 0x00007fff5dfe586a __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff5e09e56e _pthread_cond_wait + 722
2 libopenblasp-r0.3.5.dev.dylib 0x000000010af14a3b blas_thread_server + 619
3 libsystem_pthread.dylib 0x00007fff5e09b2eb _pthread_body + 126
4 libsystem_pthread.dylib 0x00007fff5e09e249 _pthread_start + 66
5 libsystem_pthread.dylib 0x00007fff5e09a40d thread_start + 13
Thread 2:
0 libsystem_kernel.dylib 0x00007fff5dfe586a __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff5e09e56e _pthread_cond_wait + 722
2 libopenblasp-r0.3.5.dev.dylib 0x000000010af14a3b blas_thread_server + 619
3 libsystem_pthread.dylib 0x00007fff5e09b2eb _pthread_body + 126
4 libsystem_pthread.dylib 0x00007fff5e09e249 _pthread_start + 66
5 libsystem_pthread.dylib 0x00007fff5e09a40d thread_start + 13
Thread 3:
0 libsystem_kernel.dylib 0x00007fff5dfe586a __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff5e09e56e _pthread_cond_wait + 722
2 libopenblasp-r0.3.5.dev.dylib 0x000000010af14a3b blas_thread_server + 619
3 libsystem_pthread.dylib 0x00007fff5e09b2eb _pthread_body + 126
4 libsystem_pthread.dylib 0x00007fff5e09e249 _pthread_start + 66
5 libsystem_pthread.dylib 0x00007fff5e09a40d thread_start + 13
Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000000000000 rbx: 0x000000010a75fd68 rcx: 0x000000010a75fd50 rdx: 0x00000001177329c0
rdi: 0x000000010a75fd80 rsi: 0x0000000000000000 rbp: 0x00007ffee58d49e0 rsp: 0x00007ffee58d49d0
r8: 0x1678a558da7df69e r9: 0x00007ffee58d4a90 r10: 0x00007fe0bb081a68 r11: 0x00007ffee58d4ae8
r12: 0x0000000116209a08 r13: 0x00000001161a8698 r14: 0x0000000116209a08 r15: 0x0000000000000000
rip: 0x0000000116045520 rfl: 0x0000000000010202 cr2: 0x0000000000000000
Logical CPU: 6
Error Code: 0x00000004
Trap Number: 14
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-04-17T23:48:09Z | 2019-04-19T17:13:24Z | 2019-04-18T21:52:15Z | CONTRIBUTOR | null | - [x] xref #25725
Bit of a tedious whopper to review (sorry), but this PR removes all instances of inheriting from the base ``object`` from the code base, as dropping Python2 support means that this particular idiom is no longer needed.
Additionally, as part of the above, I've removed the check in code_checks.sh that classes must inherit, as that is no longer a requirement after dropping Python2. I don't think a different check is possible to do now, but I can change instead of delete the check if anyone thinks that would make sense.
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} | 3 | 2019-04-18T00:25:15Z | 2019-04-19T17:13:36Z | 2019-04-18T03:48:23Z | CONTRIBUTOR | null | As part of #26128, I saw that in three test methods, their first parameter has been named ``object`` rather than ``self``. This rectifies that. | {
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Additionally, the register_index_accessor, besides the init, what else is needed to properly work?
My goal is to use an rtree index to find my content. | {
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} | 15 | 2019-04-18T07:56:24Z | 2021-11-14T03:19:08Z | 2021-11-14T03:19:08Z | NONE | null | ## Code Sample
**[IN]**
```python
import pandas as pd
now = pd.Timestamp.utcnow()
help(now.isoformat)
```
**[OUT]**
Help on method isoformat in module pandas._libs.tslibs.timestamps:
isoformat(sep='T') method of pandas._libs.tslibs.timestamps.Timestamp instance
[sep] -> string in ISO 8601 format, YYYY-MM-DDT[HH[:MM[:SS[.mmm[uuu]]]]][+HH:MM].
sep is used to separate the year from the time, and defaults to 'T'.
timespec specifies what components of the time to include (allowed values are 'auto', 'hours', 'minutes', 'seconds', 'milliseconds', and 'microseconds').
**[IN]**
```python
now.isoformat(timespec="seconds")
```
**[OUT]**
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-3626478eeb62> in <module>
----> 1 now.isoformat(timespec="seconds")
pandas\_libs\tslibs\timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.isoformat()
TypeError: isoformat() got an unexpected keyword argument 'timespec'
**[IN]**
```python
now.to_pydatetime().isoformat(timespec="seconds")
```
**[OUT]**
'2019-04-18T07:38:50+00:00'
## Problem description
The help method has given me the feeling that I can use timespec just like I can with standard datetime objects (since python 3.6).
## Expected Output
We could mimic standard datetime.isoformat behavior, or at least remove this false information from the help message.
## Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.1
pip: 19.0.3
setuptools: 41.0.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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#### reproduce:
```python
r = pd.Series( ['0','1', None, np.nan])
s = pd.to_numeric( r, downcast='integer').astype('Int64')
```
```python
s.to_hdf('file', 'df', mode='w', complevel=9)
```
#### `AttributeError:` 'NoneType' object has no attribute 'names'
```python
s.to_hdf('file', 'df', mode='w')
```
#### TypeError: objects of type ``IntegerArray`` are not supported in this context, sorry; supported objects are: NumPy array, record or scalar; homogeneous list or tuple, integer, float, complex or bytes
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.2
pytest: 3.8.0
pip: 19.0.3
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.10.0
xarray: None
IPython: 6.5.0
sphinx: 1.8.5
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: 0.4.0
matplotlib: 2.2.3
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: 0.10.0
pandas_datareader: None
gcsfs: None
</details>
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} | 10 | 2019-04-18T13:02:18Z | 2019-04-20T16:45:06Z | 2019-04-20T16:45:02Z | CONTRIBUTOR | null | - [x] closes #16771
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | closed | false | null | [] | null | 15 | 2019-04-18T15:28:30Z | 2019-09-20T14:41:04Z | 2019-09-20T14:41:04Z | CONTRIBUTOR | null | based on #11788
closes #6245
This PR allows to save wide dataframes to hdf. It will break forward compatibility, old versions of pandas will not be able to read hdfs from new versions.
The column is saved to a vlarray with object atomic. This is rather slow for very wide dfs but does not require any type checks.
One test fails because the saved column is not compressed but the compression level is checked for all nodes. I think this is fine but i wanted to consult with you before rewriting the test.
Two tests were added to check if a wide df can be saved and if it is appendable.
Please let me know what you think and if there is additional need for test coverage. | {
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} | 3 | 2019-04-18T18:14:32Z | 2019-05-01T21:48:01Z | 2019-05-01T21:48:01Z | CONTRIBUTOR | null | I'm using `pd.merges_asof()` to merge two dataframes together. The `by` keys are categoricals, but not equal, apparently (one has a few values more than the other). It took me a little bit to figure that out, however, because the error message for this was a bit confusing: "incompatible merge keys [1] category and category, must be the same type". It'd be nice if it was clearer. I can do the pull request, once we figure out how to make this better.
I see the following possible solutions:
1. Special-case the error message for dtypes that take parameters and so are not necessarily all equal. Something a bit like: "incompatible merge keys: both are category, but not equal ones" (Easiest solution.)
2. Make a nicer error message for categories, by digging a little bit into what makes them not equal. Something like "incompatible merge keys: both categories, but the left one has 3 levels more", or "but they have different levels: ..."
3. Change the `__str__` method for CategoricalDType to print something a bit more informative than "category". (No guarantee though that what we would print would always allow people to distinguish two not-equal categoricals as not equal.. unless we were to print out all the levels.)
Anything that sounds good here? | {
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} | 9 | 2019-04-18T18:32:25Z | 2019-05-29T14:06:24Z | 2019-05-29T02:15:24Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/19239
This currently includes the changes from https://github.com/pandas-dev/pandas/pull/25682, which I think is mergeable.
I think this would be good to have for 0.25.0. I think it's close, but I may not have time to push this across the finish line. Anyone interested in finishing it off? | {
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} | 2 | 2019-04-18T21:16:32Z | 2019-04-21T10:52:32Z | 2019-04-21T10:52:32Z | CONTRIBUTOR | null | The doc for the `HDFStore` class mentions:
```
complevel : int, 0-9, default None
Specifies a compression level for data.
A value of 0 disables compression.
```
That doesn't actually answer the question of what compression level is used when the default (None) is used, though. Is None translated further down to 0? it turns out yes, but you have to dig in the code to actually figure that out. And it could as well have been translated eventually to any other value.
Two options:
1. Actually change the default in the `complevel` argument to be "0". (It's an immutable object, so it's fine as a default value for a function argument.)
2. Just adjust the doc in some way.
When the right solution is decided, I can do a pull request with it. Thanks! | {
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"color": "d93f0b... | closed | false | null | [] | null | 7 | 2019-04-18T23:19:39Z | 2019-05-03T05:22:19Z | 2019-05-03T05:22:18Z | NONE | null | - [x] closes #26127
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This ups the cython minimum version to fix bugs as seen in #26127. I've added a test which goes through the members of `_libs` to check that they don't segfault when accessed. If you manually install 0.28.2 and run the test, you'll see it segfault.
I'm not sure how to place this change in the "whatsnow", so I'd be happy if I could get some advice there (and anywhere else I've gone wrong, of course). | {
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"description": "read_hdf... | open | false | null | [] | null | 8 | 2019-04-19T02:50:11Z | 2021-07-02T05:29:31Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# init dataframe with newly supported IntegerArray for missing values
d = {'col1': [1, 2, None]}
df = pd.DataFrame(data=d, dtype=pd.Int32Dtype())
df.dtypes
# Error: ValueError: cannot convert float NaN to integer
df.to_hdf("foo.h5", key='df', mode='w', format='table')
# Another error: TypeError: objects of type ``IntegerArray`` are not supported in this context, sorry; supported objects are: NumPy array, record or scalar; homogeneous list or tuple, integer, float, complex or bytes
df.to_hdf("foo.h5", key='df', mode='w') # , format='table'
# same error: TypeError: objects of type ``IntegerArray`` are not supported in this context, sorry; supported objects are: NumPy array, record or scalar; homogeneous list or tuple, integer, float, complex or bytes
with pd.HDFStore("bar.h5", 'a') as h5_pd:
h5_pd["spam"] = df
```
#### Problem description
Starting from version 0.24.0, pandas supports [`pandas.arrays.IntegerArray`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.arrays.IntegerArray.html) to help deal with missing values (I'm aware it is still experimental).
However, when trying to store this dataframe in a HDF5 file, errors are thrown (2 different errors, depending on whether `format='table'` is set or not). See the code example comments.
#### Expected Output
An errorless creation of an HDF5 file containing a pandas DataFrame.
#### Observed Output
Either:
```
ValueError: cannot convert float NaN to integer
```
or
```
TypeError: objects of type ``IntegerArray`` are not supported in this context, sorry; supported objects are: NumPy array, record or scalar; homogeneous list or tuple, integer, float, complex or bytes
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-47-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: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
#### Output of ``conda list``
<details><summary>Conda environment (CLICK ME)</summary>
```
# packages in conda environment:
#
# Name Version Build Channel
asn1crypto 0.24.0 py36_0
attrs 19.1.0 py_0 conda-forge
audioread 2.1.6 py36_0 conda-forge
backcall 0.1.0 py_0 conda-forge
blas 2.4 openblas conda-forge
bleach 3.1.0 py_0 conda-forge
blosc 1.15.0 hd408876_0
bokeh 1.1.0 py_0 bokeh
bzip2 1.0.6 h14c3975_1002 conda-forge
ca-certificates 2019.3.9 hecc5488_0 conda-forge
certifi 2019.3.9 py36_0 conda-forge
cffi 1.12.2 py36h2e261b9_1
chardet 3.0.4 py36_1
cryptography 2.6.1 py36h1ba5d50_0
cycler 0.10.0 py_1 conda-forge
dbus 1.13.2 h714fa37_1
decorator 4.4.0 py_0 conda-forge
defusedxml 0.5.0 py_1 conda-forge
entrypoints 0.3 py36_1000 conda-forge
expat 2.2.5 hf484d3e_1002 conda-forge
ffmpeg 4.1.1 h167e202_0 conda-forge
fontconfig 2.13.1 he4413a7_1000 conda-forge
freetype 2.10.0 he983fc9_0 conda-forge
futures-compat 1.0 py3_0
gettext 0.19.8.1 hc5be6a0_1002 conda-forge
glib 2.56.2 had28632_1001 conda-forge
gmp 6.1.2 hf484d3e_1000 conda-forge
gnutls 3.6.5 hd3a4fd2_1002 conda-forge
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.9.0 py36h7918eee_0
hdf5 1.10.4 hb1b8bf9_0
holoviews 1.12.1 py_0 pyviz
icu 58.2 hf484d3e_1000 conda-forge
idna 2.8 py36_0
ipykernel 5.1.0 py36h24bf2e0_1002 conda-forge
ipython 7.1.1 py36h24bf2e0_1000 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
ipywidgets 7.4.2 py_0 conda-forge
jaconv 0.2.4 pypi_0 pypi
jedi 0.13.3 py36_0 conda-forge
jinja2 2.10 py_1 conda-forge
joblib 0.13.2 py_0 conda-forge
jpeg 9c h14c3975_1001 conda-forge
js2py 0.60 pypi_0 pypi
jsonschema 3.0.1 py36_0 conda-forge
jupyter 1.0.0 py_2 conda-forge
jupyter_client 5.2.4 py_3 conda-forge
jupyter_console 6.0.0 py_0 conda-forge
jupyter_core 4.4.0 py_0 conda-forge
kiwisolver 1.0.1 py36h6bb024c_1002 conda-forge
lame 3.100 h14c3975_1001 conda-forge
libblas 3.8.0 4_openblas conda-forge
libcblas 3.8.0 4_openblas conda-forge
libedit 3.1.20181209 hc058e9b_0
libffi 3.2.1 hd88cf55_4
libflac 1.3.1 hf484d3e_1002 conda-forge
libgcc-ng 8.2.0 hdf63c60_1
libgfortran 3.0.0 1 conda-forge
libgfortran-ng 7.3.0 hdf63c60_0
libiconv 1.15 h516909a_1005 conda-forge
liblapack 3.8.0 4_openblas conda-forge
liblapacke 3.8.0 4_openblas conda-forge
libogg 1.3.2 h14c3975_1001 conda-forge
libopenblas 0.3.3 h5a2b251_3
libpng 1.6.36 h84994c4_1000 conda-forge
librosa 0.6.3 py_0 conda-forge
libsndfile 1.0.28 hf484d3e_1000 conda-forge
libsodium 1.0.16 h14c3975_1001 conda-forge
libstdcxx-ng 8.2.0 hdf63c60_1
libtiff 4.0.10 h648cc4a_1001 conda-forge
libuuid 2.32.1 h14c3975_1000 conda-forge
libvorbis 1.3.5 h14c3975_1001 conda-forge
libxcb 1.13 h14c3975_1002 conda-forge
libxml2 2.9.8 h143f9aa_1005 conda-forge
llvmlite 0.26.0 py36hdbcaa40_1000 conda-forge
lzo 2.10 h49e0be7_2
markdown 3.0.1 py36_0
markupsafe 1.1.1 py36h14c3975_0 conda-forge
matplotlib 3.0.2 py36h8a2030e_1001 conda-forge
matplotlib-base 3.0.2 py36h167e16e_1001 conda-forge
mistune 0.8.4 py36h14c3975_1000 conda-forge
nbconvert 5.4.1 py_2 conda-forge
nbformat 4.4.0 py_1 conda-forge
ncurses 6.1 he6710b0_1
nettle 3.4.1 h1bed415_1002 conda-forge
nodejs 10.13.0 he6710b0_0
notebook 5.7.6 py36_0 conda-forge
numba 0.41.0 py36h637b7d7_1000 conda-forge
numexpr 2.6.9 py36h2ffa06c_0
numpy 1.16.2 py36h8b7e671_1 conda-forge
olefile 0.46 py_0 conda-forge
openblas 0.3.5 ha44fe06_0 conda-forge
openh264 1.8.0 hdbcaa40_1000 conda-forge
openssl 1.1.1b h14c3975_1 conda-forge
packaging 19.0 py_0 conda-forge
pandas 0.24.2 py36he6710b0_0
pandoc 2.7.1 0 conda-forge
pandocfilters 1.4.2 py_1 conda-forge
panel 0.4.0 py_0 pyviz
param 1.8.2 py_0 conda-forge
parso 0.3.4 py_0 conda-forge
pcre 8.43 he6710b0_0
pexpect 4.6.0 py36_1000 conda-forge
pickleshare 0.7.5 py36_1000 conda-forge
pillow 5.3.0 py36h00a061d_1000 conda-forge
pip 19.0.3 py36_0
plotly 3.7.0 py_0
prometheus_client 0.6.0 py_0 conda-forge
prompt_toolkit 2.0.9 py_0 conda-forge
pthread-stubs 0.4 h14c3975_1001 conda-forge
ptyprocess 0.6.0 py36_1000 conda-forge
pyasn1 0.4.5 pypi_0 pypi
pycparser 2.19 py36_0
pyct 0.4.6 py_0 pyviz
pyct-core 0.4.6 py_0 pyviz
pygments 2.3.1 py_0 conda-forge
pyjsparser 2.5.2 pypi_0 pypi
pyopenssl 19.0.0 py36_0
pyparsing 2.3.1 py_0 conda-forge
pyqt 5.6.0 py36h13b7fb3_1008 conda-forge
pyrsistent 0.14.11 py36h14c3975_0 conda-forge
pysmb 1.1.27 pypi_0 pypi
pysocks 1.6.8 py36_0
pysoundfile 0.10.2 pyh14c3975_1000 conda-forge
pytables 3.5.1 py36h71ec239_0
python 3.6.8 h0371630_0
python-dateutil 2.8.0 py_0 conda-forge
pytz 2018.9 py36_0
pyviz_comms 0.7.2 py_0 conda-forge
pyyaml 5.1 py36h14c3975_0 conda-forge
pyzmq 18.0.1 py36h0e1adb2_0 conda-forge
qt 5.6.3 h8bf5577_3
qtconsole 4.4.3 py_0 conda-forge
readline 7.0 h7b6447c_5
requests 2.21.0 py36_0
resampy 0.2.1 py_1 conda-forge
retrying 1.3.3 py36_2
scikit-learn 0.20.3 py36h22eb022_0
scipy 1.2.1 py36he2b7bc3_0
send2trash 1.5.0 py_0 conda-forge
setuptools 40.8.0 py36_0
sip 4.18.1 py36hf484d3e_1000 conda-forge
six 1.12.0 py36_1000 conda-forge
snappy 1.1.7 hbae5bb6_3
sqlite 3.27.2 h7b6447c_0
terminado 0.8.1 py36_1001 conda-forge
testpath 0.3.1 py36_0
tk 8.6.8 hbc83047_0
tornado 6.0.2 py36h516909a_0 conda-forge
traitlets 4.3.2 py36_1000 conda-forge
tzlocal 1.5.1 pypi_0 pypi
urllib3 1.24.1 py36_0
wcwidth 0.1.7 py_1 conda-forge
webencodings 0.5.1 py_1 conda-forge
wheel 0.33.1 py36_0
widgetsnbextension 3.4.2 py36_1000 conda-forge
x264 1!152.20180806 h14c3975_0 conda-forge
xlrd 1.2.0 py36_0
xorg-libxau 1.0.9 h14c3975_0 conda-forge
xorg-libxdmcp 1.1.3 h516909a_0 conda-forge
xz 5.2.4 h14c3975_4
yaml 0.1.7 h14c3975_1001 conda-forge
zeromq 4.2.5 hf484d3e_1006 conda-forge
zlib 1.2.11 h7b6447c_3
```
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} | 2 | 2019-04-19T03:34:32Z | 2021-07-02T05:30:17Z | null | NONE | null | # DataFrame
# DataFrame.to_excel
# Output Excel File
### how output '大区'？
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} | 2 | 2019-04-19T07:38:23Z | 2019-04-21T16:04:47Z | 2019-04-21T16:04:47Z | CONTRIBUTOR | null | Part of #25882
Current errors are:
pandas\core\accessor.py:14: error: Need type annotation for '_accessors'
PR coming soon | {
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} | 8 | 2019-04-19T07:59:17Z | 2019-04-21T16:56:39Z | 2019-04-21T16:04:47Z | CONTRIBUTOR | null | - [x] closes #26146
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26148 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26148/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26148/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26148/events | https://github.com/pandas-dev/pandas/issues/26148 | 435,095,450 | MDU6SXNzdWU0MzUwOTU0NTA= | 26,148 | Fix Type Annotation in pandas/core/api.py | {
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} | 9 | 2019-04-19T08:27:37Z | 2019-05-12T14:52:12Z | 2019-05-12T14:52:12Z | CONTRIBUTOR | null | Part of #25882
Current errors are:
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'Int8Dtype'
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'Int16Dtype'
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'Int32Dtype'
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'Int64Dtype'
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'UInt8Dtype'
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'UInt16Dtype'
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'UInt32Dtype'
pandas\core\api.py:5: error: Module 'pandas.core.arrays.integer' has no attribute 'UInt64Dtype'
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} | 3 | 2019-04-19T10:07:11Z | 2019-04-19T15:53:00Z | 2019-04-19T15:53:00Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Parameters like project, token should be taken and added if provided in case of GCP buckets.
```
def get_filepath_or_buffer(filepath_or_buffer, encoding=None,
compression=None, mode=None, project=None, token=None):
if mode is None:
mode = 'rb'
fs = gcsfs.GCSFileSystem(project=project, token=token)
filepath_or_buffer = fs.open(
filepath_or_buffer, mode) # type: gcsfs.GCSFile
return filepath_or_buffer, None, compression, True
```
#### Problem description
Currently if you do a read_csv in pandas with a google bucket URL like 'pd.read_csv('gs://abc/def.csv')', there is no way you can provide service account or token details for authorization.
The library that supports reading from gcp buckets [here](https://github.com/pandas-dev/pandas/blob/master/pandas/io/gcs.py) should take **'token'** parameter as well, as shown above
#### Expected Output
#### Output of ``pd.show_versions()``
[paste the output of ``pd.show_versions()`` here below this line]
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.11.6.el7.x86_64
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: 3.3.1
pip: 19.0.3
setuptools: 41.0.0
Cython: 0.27.3
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.6.5
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.0
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: 0.2.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26150 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26150/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26150/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26150/events | https://github.com/pandas-dev/pandas/issues/26150 | 435,167,375 | MDU6SXNzdWU0MzUxNjczNzU= | 26,150 | Weird performance characteristics of resampled quantile() function (100 times slower) | {
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"default"... | open | false | null | [] | null | 6 | 2019-04-19T12:51:14Z | 2019-10-22T15:48:21Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from time import time
print("Pandas version:", pd.__version__)
print("Numpy version:", np.__version__)
index = pd.date_range("2016-01-01", periods=int(1e7), freq="ms")
df = pd.DataFrame(np.random.rand(len(index)), columns=["column"], index=index)
print()
print("Median")
start = time()
df.resample("1min").median()
print("df.resample.median", time() - start)
start = time()
df.column.resample("1min").median()
print("df.column.resample.median", time() - start)
start = time()
df.resample("1min").column.median()
print("df.resample.column.median", time() - start)
start = time()
df.resample("1min").apply(lambda x: np.median(x))
print("df.resample.apply np.median", time() - start)
start = time()
df.column.resample("1min").apply(lambda x: np.median(x))
print("df.column.resample.apply np.median", time() - start)
start = time()
df.column.resample("1min").apply(lambda x: x.median())
print("df.column.resample.apply pd.median", time() - start)
start = time()
df.resample("1min").column.apply(lambda x: np.median(x))
print("df.resample.column.apply np.median", time() - start)
start = time()
df.resample("1min").column.apply(lambda x: x.median())
print("df.resample.column.apply pd.median", time() - start)
print()
print("Quantile")
start = time()
df.resample("1min").quantile(0.25)
print("df.resample.quantile", time() - start)
start = time()
df.column.resample("1min").quantile(0.25)
print("df.column.resample.quantile", time() - start)
start = time()
df.resample("1min").column.quantile(0.25)
print("df.resample.column.quantile", time() - start)
start = time()
df.resample("1min").apply(lambda x: np.quantile(x, 0.25))
print("df.resample.apply np.quantile", time() - start)
start = time()
df.resample("1min").apply(lambda x: x.quantile(0.25))
print("df.resample.apply pd.quantile", time() - start)
start = time()
df.column.resample("1min").apply(lambda x: np.quantile(x, 0.25))
print("df.column.resample.apply np.quantile", time() - start)
start = time()
df.resample("1min").column.apply(lambda x: x.quantile(0.25))
print("df.resample.column.apply pd.quantile", time() - start)
```
Output:
```
Pandas version: 0.24.2
Numpy version: 1.16.2
Median
df.resample.median 0.5927023887634277
df.column.resample.median 0.5536832809448242
df.resample.column.median 0.5364699363708496
df.resample.apply np.median 0.1465294361114502
df.column.resample.apply np.median 0.14081549644470215
df.column.resample.apply pd.median 0.19739317893981934
df.resample.column.apply np.median 0.704085111618042
df.resample.column.apply pd.median 0.7553591728210449
Quantile
df.resample.quantile 0.8943967819213867
df.column.resample.quantile 16.76218605041504
df.resample.column.quantile 16.512025117874146
df.resample.apply np.quantile 0.15454792976379395
df.resample.apply pd.quantile 0.2752718925476074
df.column.resample.apply np.quantile 0.1515665054321289
df.resample.column.apply pd.quantile 0.8622317314147949
```
#### Problem description
I have noticed that `quantile()` is sometimes extremely slow even though `median()` (which should have a similar run time) is not. While debugging the behaviour, I have found the following two problems:
1) apply + numpy is significantly faster than the corresponding pandas functions. This is surprising since I would have expected that the pandas operations avoid the overhead of apply().
2) If you perform `quantile()` on a series instead of a dataframe, then the operation is much slower. In the example above, it is 100 times slower than the fastest equivalent.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-17134-Microsoft
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.2
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
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sqlalchemy: 1.3.1
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psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | [] | closed | false | null | [] | null | 1 | 2019-04-19T12:54:39Z | 2019-04-19T12:57:56Z | 2019-04-19T12:57:56Z | NONE | null | Trying to erase the first collum in my table, the index collum.
```python
df = pd.read_csv(DBPath, index=False)
db_table = df.head(891).to_html(escape=False)
fig, ax = plt.subplots()
df.groupby('Pclass')['Survived'].aggregate(lambda x: x.sum() / len(x)).plot(kind='bar')
encoded = fig_to_base64(fig)
plot_img = '<img src="data:image/png;base64, {}">'.format(encoded.decode('utf-8'))
```
DBPath is predefiend so there is no need to worry about that.
see the first collum, how do I get rid of that?
Thanks in advance. | {
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} | 4 | 2019-04-19T16:54:38Z | 2020-01-16T00:34:07Z | 2019-05-05T22:20:01Z | MEMBER | null | - [X] closes #26011
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
Would ideally like to combine first, nth and last implementations. Consider this a precursor
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} | 3 | 2019-04-19T17:19:29Z | 2019-04-19T20:33:27Z | 2019-04-19T19:48:59Z | MEMBER | null | More GroupBy cleanup post Panel removal
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} | 6 | 2019-04-19T17:23:14Z | 2019-04-23T12:12:28Z | 2019-04-23T12:12:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.Series([True, False]).nlargest(1)
```
> 1 False
> dtype: bool
#### Problem description
If you cast this to any other type, you will have the opposite and expected order.
More generally in python `True > False`. You still have the same order everywhere in pandas.
```python
df = pd.DataFrame({'True': True, 'False': [False] * 2})
df['True'] > df['False']
```
The result is misleading and can easily lead to a debugging process.
- [X] Check for duplicate
- [X] Pandas 0.24.2
#### Expected Output
> 1 True
> dtype: bool
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-17763-Microsoft
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 9.0.1
setuptools: 40.6.3
Cython: 0.26.1
numpy: 1.16.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.8
xlrd: None
xlwt: None
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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- [x] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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... | closed | false | null | [] | null | 11 | 2019-04-20T02:05:06Z | 2019-04-21T00:06:32Z | 2019-04-20T14:48:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.read_json('{"Month":{"0":"2018-01-01","1":"2018-02-01","2":"2018-03-01","3":"2018-04-01","4":"2018-05-01","5":"2018-01-01","6":"2018-02-01","7":"2018-03-01","8":"2018-04-01","9":"2018-05-01","10":"2018-01-01","11":"2018-02-01","12":"2018-03-01","13":"2018-04-01","14":"2018-05-01"},"Person":{"0":"A","1":"A","2":"A","3":"A","4":"A","5":"B","6":"B","7":"B","8":"B","9":"B","10":"C","11":"C","12":"C","13":"C","14":"C"},"Foo":{"0":2,"1":3,"2":4,"3":4,"4":3,"5":10,"6":8,"7":6,"8":4,"9":8,"10":5,"11":6,"12":5,"13":6,"14":5},"Bar":{"0":10,"1":30,"2":5,"3":40,"4":20,"5":80,"6":70,"7":60,"8":50,"9":40,"10":50,"11":50,"12":50,"13":50,"14":50}}'
, convert_dates = ['Month'])
df_rolls = (df
.sort_values(by=['Month', 'Person'], ascending=True)
.set_index(['Month'])
.groupby(['Person'])
.rolling(3, min_periods=3)
)
```
this works without bottleneck installed but throws this error: `# AttributeError: 'float' object has no attribute 'round'`
```python
df_rolls.agg([lambda x: x.mean().round(4])
```
#### Problem description
my intention is to compute rolling a rolling mean and sum on multiple columns at once (see below). I was using agg because it allows for multiple functions at once.
```python
df_rolls.agg([MeanRound, 'sum'])
```
#### Expected Output
```python
df_rolls.agg([MeanRound])
```
```python
df_rolls.agg([MeanRound, 'sum'])
```
I was able get a workaround with apply (even though `.transform()` isn't implemented for `RollingGroupby`?)
```
df_rolls.apply(MeanRound, raw = True)
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
before bottleneck
```
INSTALLED VERSIONS
------------------
commit: d04fe2a3f27f84b91e4df800cd8b0836bd8b0dfc
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: 4.4.1
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.14.6
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
after bottleneck
```
------------------
commit: d04fe2a3f27f84b91e4df800cd8b0836bd8b0dfc
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: 4.4.1
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
numpy: 1.14.6
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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- [x] closes #26154
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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- [x] closes #26139
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- [x] tests added / passed
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Removes the metaclass ``_WritableDoc`` from the code base. | {
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} | 2 | 2019-04-20T21:00:25Z | 2019-04-21T16:31:42Z | 2019-04-21T15:50:51Z | CONTRIBUTOR | null | - [x] xref #25725
This removes the ``compat.add_metaclass`` function from the code base and replaces it with a metaclass parameter in class instation.
I don't understand why ``ExcelWriter`` needs to use a abc.ABCMeta metaclass, as the class is being used directly in the code base and I thought abstract classes couldn't be instantiated, but needed subclassing. I've kept abc.ABCMeta metaclass in ``ExcelWriter``, but would appreciate advice. | {
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} | 3 | 2019-04-20T21:15:28Z | 2019-04-21T16:31:32Z | 2019-04-21T15:54:27Z | CONTRIBUTOR | null | - [x] xref #25725
- [x] xref #26165
While doing #26165 I discovered some uses of ``abc.abstractproperty`` in the codebase. This propery was deprecated in Python 3.3 and superseeded by using ``abc.abstractmethod`` and ``property`` together instead (see [python abc.abstractproperty docs](https://docs.python.org/3.7/library/abc.html#abc.abstractproperty)). I've therefore removed the uses of ``abc.abstractproperty`` from the code base.
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} | 3 | 2019-04-20T22:12:20Z | 2019-04-21T17:51:36Z | 2019-04-21T16:37:05Z | MEMBER | null | - [x] closes #25843
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-04-20T23:07:16Z | 2019-04-21T15:50:18Z | 2019-04-21T15:49:19Z | MEMBER | null | Removed unused imports in tslibs found via PyCharm. | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26170 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26170/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26170/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26170/events | https://github.com/pandas-dev/pandas/pull/26170 | 435,476,888 | MDExOlB1bGxSZXF1ZXN0MjcyMTk0ODk4 | 26,170 | BUG: read_json converted date strings with Z to UTC | {
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} | 4 | 2019-04-21T05:23:24Z | 2019-05-07T15:19:45Z | 2019-05-07T04:19:24Z | MEMBER | null | - [x] closes #25546
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26171 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26171/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26171/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26171/events | https://github.com/pandas-dev/pandas/issues/26171 | 435,482,633 | MDU6SXNzdWU0MzU0ODI2MzM= | 26,171 | TypeError: __init__() got an unexpected keyword argument 'max_rows' | {
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} | [] | closed | false | null | [] | null | 3 | 2019-04-21T07:11:56Z | 2020-06-03T12:00:16Z | 2019-04-21T11:31:15Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pystan
from fbprophet import Prophet
plt.style.use('fivethirtyeight')
y = [t1_tmax['date'], t1_tmax['value']]
import pandas as pd
df = pd.DataFrame({'date': y[0], 'value': y[1]})
df.dtypes
df = df.rename(columns={'date': 'ds','value': 'y'})
df
my_model = Prophet(interval_width=0.95)
my_model.fit(df)
```
#### Problem description
I have installed the latest Pystan and fbprophet versions:
pystan 2.17.1.0
fbprophet 0.4.post2
Currently, I'm using conda 4.6.14 with Python 3.6.7 version as suggested after uninstalling 3.7 in other threads!
#### Expected Output
#### Output of ``
my_model.fit(df)
``
<details>
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-134-2eba425e3c88> in <module>
2 #with weekly_seasonality=True
3 #instantiate Prophet
----> 4 my_model.fit(df) #fit the model with your dataframe
~\Anaconda3\Anaconda\lib\site-packages\fbprophet\forecaster.py in fit(self, df, **kwargs)
1010 """
1011 if self.history is not None:
-> 1012 raise Exception('Prophet object can only be fit once. '
1013 'Instantiate a new object.')
1014 if ('ds' not in df) or ('y' not in df):
Exception: Prophet object can only be fit once. Instantiate a new object.
</details> | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26172 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26172/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26172/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26172/events | https://github.com/pandas-dev/pandas/pull/26172 | 435,486,324 | MDExOlB1bGxSZXF1ZXN0MjcyMjAwODE4 | 26,172 | ENH: adding functionality to support 'skipdatarows' flag in read_csv to … | {
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} | 4 | 2019-04-21T08:05:35Z | 2019-04-22T02:52:04Z | 2019-04-21T18:29:56Z | NONE | null | - [x] closes #23067
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adding feature for issue https://github.com/pandas-dev/pandas/issues/23067#issue-368463820. The newly added 'skipdatarows' flag allows the first n rows excluding comments to be skipped when read_csv is called. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26173/events | https://github.com/pandas-dev/pandas/issues/26173 | 435,488,707 | MDU6SXNzdWU0MzU0ODg3MDc= | 26,173 | Plotting ExtensionArrays | {
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... | open | false | null | [] | null | 5 | 2019-04-21T08:39:41Z | 2021-07-02T05:32:08Z | null | NONE | null | I'd like to plot data from an ExtensionArray. This currently results in an error `TypeError: Empty 'DataFrame': no numeric data to plot`
This is because the EA data is filtered out. Adding the ExtensionDtype to the filter criteria makes the plot work.
https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_core.py#L367
```python3
numeric_data = data.select_dtypes(include=[np.number,
"datetime",
"datetimetz",
"timedelta",
pint.PintType])
```
Could we get a property of the ExtensionDtype that flags the dtype as holding plotable numeric data? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26174 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26174/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26174/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26174/events | https://github.com/pandas-dev/pandas/issues/26174 | 435,511,282 | MDU6SXNzdWU0MzU1MTEyODI= | 26,174 | Exception: Prophet object can only be fit once. Instantiate a new object. | {
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} | [] | closed | false | null | [] | null | 2 | 2019-04-21T13:44:55Z | 2019-04-21T14:12:21Z | 2019-04-21T14:05:11Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pystan
from fbprophet import Prophet
plt.style.use('fivethirtyeight')
y = [t1_tmax['date'], t1_tmax['value']]
import pandas as pd
df = pd.DataFrame({'date': y[0], 'value': y[1]})
df.dtypes
df = df.rename(columns={'date': 'ds','value': 'y'})
df
my_model = Prophet(interval_width=0.95)
my_model.fit(df)
```
#### Problem description
I have installed the latest Pystan and fbprophet versions:
pystan 2.17.1.0
fbprophet 0.4.post2
Currently, I'm using conda 4.6.14 with Python 3.6.7 version as suggested after uninstalling 3.7 in other threads!
#### Expected Output
#### Output of ``my_model.fit(df) ``
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-134-2eba425e3c88> in <module>
2 #with weekly_seasonality=True
3 #instantiate Prophet
----> 4 my_model.fit(df) #fit the model with your dataframe
~\Anaconda3\Anaconda\lib\site-packages\fbprophet\forecaster.py in fit(self, df, **kwargs)
1010 """
1011 if self.history is not None:
-> 1012 raise Exception('Prophet object can only be fit once. '
1013 'Instantiate a new object.')
1014 if ('ds' not in df) or ('y' not in df):
Exception: Prophet object can only be fit once. Instantiate a new object.
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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] | closed | false | null | [] | null | 11 | 2019-04-21T17:42:33Z | 2019-04-24T15:10:38Z | 2019-04-24T15:10:38Z | CONTRIBUTOR | null | Since we are not merging #25975 `Type` and `Final` needs to be added to `pandas._typing` module.
`Final` is required for PR #25961
`Type` is required to stay compatible with python 3.5.1 as `Type` was added in 3.5.2 [python docs](https://docs.python.org/3/library/typing.html#typing.Type)
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Issue #20824 found that a fallback was occuring when df.Groupby().sum() was called with the skipna flag. This was occurring because the _cython_agg_general function was not accepting the argument, which has now been fixed. The fallback still occurs with strings in the df, however this is a deeper issue stemming from the _aggregate() call in groupby/ops.py (line 572).
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} | 9 | 2019-04-21T18:55:05Z | 2019-04-25T04:28:05Z | 2019-04-24T21:02:12Z | CONTRIBUTOR | null | - [x] xref #25725
Simplifies the use of simple uses of ``super`` by converting to use python3 idioms. This is a quite big PR, but only deals with converting ``super(MyClass, self)`` to ``super()`` so it's very simple.
In addition to the uses of ``super`` dealt with in this PR, there are some more advanced uses of ``super`` in the code base, e.g. in classmethods and using ``super`` to jump to a specific point in the MRO-chain. I'd like to tackle those in a seperate PR in order to keep the the current large PR very simple. | {
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} | 2 | 2019-04-21T19:05:26Z | 2019-05-05T06:58:55Z | 2019-04-23T04:45:02Z | CONTRIBUTOR | null | - [x] xref #25725 & #26165
Removes uses of ``__metaclass__`` from the code base. After #26165 there was actually only one left.
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"updated_at": "2020-01-02T15:10:40Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/61"
} | 0 | 2019-04-21T19:42:50Z | 2019-04-21T23:28:49Z | 2019-04-21T23:28:49Z | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26181 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26181/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26181/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26181/events | https://github.com/pandas-dev/pandas/issues/26181 | 435,546,924 | MDU6SXNzdWU0MzU1NDY5MjQ= | 26,181 | pandas.rolling.apply skips calling function if window contains any NaN | {
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"description": "Requires... | closed | false | null | [] | null | 14 | 2019-04-21T20:53:16Z | 2021-01-14T18:15:23Z | 2021-01-14T18:15:22Z | NONE | null | This issue has been raised several times, but I do not think has been adequately addressed.
The solution given is always min_periods, but that only includes the obvious issue where the NaN is at the beginning of the series. If there is any NaN then .apply will not call the function.
There should be a skipna=True option that works. Despite this question being asked in a dozen places, it has not been fixed.
I see that @jreback has closed this issue multiple times with a reference to min_periods. Again... this is NOT an adequate answer. (See the followup here https://github.com/pandas-dev/pandas/issues/19218 that was ignored once the issue was marked closed.)
There should simply be a skipna=True flag in the rolling window function. As-is, it will not call the function in .apply() if there are ANY NaNs at any point in the window. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26182/events | https://github.com/pandas-dev/pandas/issues/26182 | 435,618,702 | MDU6SXNzdWU0MzU2MTg3MDI= | 26,182 | groupby apply failed on dataframe with DatetimeIndex | {
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} | 11 | 2019-04-22T06:40:17Z | 2020-10-02T22:59:01Z | 2020-10-02T22:59:01Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# -*- coding: utf-8 -*-
import pandas as pd
def _do_calc_(_df):
df2 = _df.copy()
df2["vol_ma20"] = df2["volume"].rolling(20, min_periods=1).mean()
return df2
dbars = pd.read_csv("2019.dbar_ftridx.csv.gz",
index_col=False,
encoding="utf-8-sig",
parse_dates=["trade_day"])
dbars = dbars.set_index("trade_day", drop=False) # everything works fine if this line is commented
df = dbars.groupby("exchange").apply(_do_calc_)
print(len(df))
```
#### Problem description
here is the input data file:
[2019.dbar_ftridx.csv.gz](https://github.com/pandas-dev/pandas/files/3102450/2019.dbar_ftridx.csv.gz)
this piece of code runs well with pandas 0.23, when upgraded to 0.24.2, it reports error:
```
Traceback (most recent call last):
File "D:/test/groupby_bug.py", line 16, in <module>
df = dbars.groupby("exchange").apply(_do_calc_)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 701, in apply
return self._python_apply_general(f)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 712, in _python_apply_general
not_indexed_same=mutated or self.mutated)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby\generic.py", line 318, in _wrap_applied_output
not_indexed_same=not_indexed_same)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 918, in _concat_objects
sort=False)
File "C:\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py", line 228, in concat
copy=copy, sort=sort)
File "C:\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py", line 292, in __init__
obj._consolidate(inplace=True)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 5156, in _consolidate
self._consolidate_inplace()
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 5138, in _consolidate_inplace
self._protect_consolidate(f)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 5127, in _protect_consolidate
result = f()
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 5136, in f
self._data = self._data.consolidate()
File "C:\Anaconda3\lib\site-packages\pandas\core\internals\managers.py", line 922, in consolidate
bm = self.__class__(self.blocks, self.axes)
File "C:\Anaconda3\lib\site-packages\pandas\core\internals\managers.py", line 114, in __init__
self._verify_integrity()
File "C:\Anaconda3\lib\site-packages\pandas\core\internals\managers.py", line 311, in _verify_integrity
construction_error(tot_items, block.shape[1:], self.axes)
File "C:\Anaconda3\lib\site-packages\pandas\core\internals\managers.py", line 1691, in construction_error
passed, implied))
ValueError: Shape of passed values is (432, 27), indices imply (1080, 27)
```
If I do not call set_index() on the dataframe, it works fine. Seems there is something wrong with the DatetimeIndex?
I don't know if this error can be re-produced on your machine, I can re-produce the same error on all my machines.
#### Expected Output
4104
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
C:\Anaconda3\python.exe D:/test/groupby_bug.py
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.0
pip: 19.0.3
setuptools: 41.0.0
Cython: 0.29.7
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.6
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26183 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26183/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26183/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26183/events | https://github.com/pandas-dev/pandas/pull/26183 | 435,710,486 | MDExOlB1bGxSZXF1ZXN0MjcyMzYwODI4 | 26,183 | BUG: Preserve sparse dtype when reindexing | {
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"color": ... | closed | false | null | [] | null | 2 | 2019-04-22T12:31:10Z | 2019-06-08T20:26:54Z | 2019-06-08T20:26:54Z | NONE | null | - [ ] closes #26123.
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Hi, I've been taking a look at this but I need to verify some of the surrounding functionality so that I prevent introducing more breakages.
In this test, https://github.com/alex-hutton/pandas/blob/preserve-sparse-dtype-26123/pandas/tests/sparse/test_combine_concat.py#L99-L110 , my interpretation is that its purpose is to verify that if two `SparseSeries` are concatenated, a `fill_value` set on either of those `SparseSeries` should be ignored in favour of the default `fill_value` of `NaN`.
Is my interpretation correct, and is this test correct? It seems odd that the the `fill_value` of the `SparseSeries` should be disregarded, but I am probably missing something. | {
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"color": "37... | closed | false | null | [] | null | 2 | 2019-04-22T15:42:28Z | 2019-04-22T16:00:31Z | 2019-04-22T16:00:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
data = []
pd.DataFrame(data, columns=['col'])
```
#### Problem description
The expected behaviour should be the 3.6 one on both cases. Failing that, it should be consistent for 3.6 and 3.7.
np version 1.15.2 ; pandas version 0.23.4
#### Expected Output
Expected: Empty dataframe with one col named 'col' in both cases.
Python 3.6: above
Python 3.7: `TypeError: int() argument must be a string, a bytes-like object or a number, not '_NoValueType'`
#### 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: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.4.0
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26185 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26185/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26185/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26185/events | https://github.com/pandas-dev/pandas/pull/26185 | 435,816,111 | MDExOlB1bGxSZXF1ZXN0MjcyNDQyODg5 | 26,185 | BUG: Plotting use FixedLocator | {
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} | 11 | 2019-04-22T17:46:51Z | 2019-05-01T14:00:04Z | 2019-04-30T12:59:54Z | CONTRIBUTOR | null | For string x values or multiindex, the axis locator is now set to a
FixedLocator for Line and Area plot. The ticklabel stay consistent
when scaling and zooming.
closes #7612
closes #15912
closes #22334
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-04-22T21:07:46Z | 2021-07-02T05:32:32Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import matplotlib.pyplot as plt
data = {"A":0, "B":3, "C":-4}
df = pd.DataFrame.from_dict(data, orient = "index", columns = ["Value"])
ax = df.plot.barh()
df = df.sort_values("Value") * - 2
df.plot.barh(ax = ax, color = "red")
plt.show()
```
#### Problem description
The second plot overwrites the first index even if it is not in the same order, producing incoherent figure where the ylabel does not match with the data displayed.
The current output (pandas 0.24.1) creates
instead of
I don't know if this is related to the way pandas handles index or matplotlib.
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} | 1 | 2019-04-22T22:29:34Z | 2019-04-23T12:10:53Z | 2019-04-23T12:10:36Z | MEMBER | null | This just started showing up in the CI failures today:
```sh
Traceback (most recent call last):
File "ci/../scripts/validate_docstrings.py", line 991, in <module>
args.ignore_deprecated))
File "ci/../scripts/validate_docstrings.py", line 891, in main
result = validate_all(prefix, ignore_deprecated)
File "ci/../scripts/validate_docstrings.py", line 845, in validate_all
doc_info = validate_one(func_name)
File "ci/../scripts/validate_docstrings.py", line 801, in validate_one
errs, wrns, examples_errs = get_validation_data(doc)
File "ci/../scripts/validate_docstrings.py", line 749, in get_validation_data
if not doc.see_also:
File "ci/../scripts/validate_docstrings.py", line 477, in see_also
in self.doc['See Also'])
File "ci/../scripts/validate_docstrings.py", line 476, in <genexpr>
for name, desc, _
ValueError: not enough values to unpack (expected 3, got 2)
```
I think it's an issue with upgrading to numpydoc 0.9 but will look in more detail and confirm | {
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} | 5 | 2019-04-22T22:36:45Z | 2020-01-16T00:34:11Z | 2019-04-23T12:10:36Z | MEMBER | null | - [X] closes #26187
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-04-22T23:53:24Z | 2019-04-28T16:11:15Z | 2019-04-28T16:11:15Z | CONTRIBUTOR | null | This just started breaking on scipy master:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=10858
xfailing in #26190
```
> /Users/jreback/pandas/pandas/tests/series/test_missing.py(1446)test_interpolate_timedelta_index()
-> result = df[0].interpolate(method=method, **kwargs)
(Pdb) n
> /Users/jreback/pandas/pandas/tests/series/test_missing.py(1447)test_interpolate_timedelta_index()
-> expected = pd.Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
(Pdb) p result
0 days 00:00:00.000000 0.0
1 days 00:00:00.000000 1.0
2 days 00:00:00.000000 2.0
3 days 00:00:00.000000 3.0
Freq: D, Name: 0, dtype: float64
(Pdb) p df[0]
0 days 00:00:00.000000 0.0
1 days 00:00:00.000000 1.0
2 days 00:00:00.000000 NaN
3 days 00:00:00.000000 3.0
Freq: D, Name: 0, dtype: float64
```
I think we are passing invalid inputs
```
> /Users/jreback/pandas/pandas/core/missing.py(311)_interpolate_scipy_wrapper()
-> new_y = method(x, y, new_x, **kwargs)
(Pdb) p method
<function pchip_interpolate at 0x81bb9e9d8>
(Pdb) p x
array([ 1, 86400000000001, 259200000000001],
dtype='timedelta64[ns]')
(Pdb) p y
array([0., 1., 3.])
``` | {
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] | closed | false | null | [] | null | 2 | 2019-04-23T03:04:39Z | 2019-04-23T16:11:52Z | 2019-04-23T04:52:47Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'a': [1, 2]})
df.drop(4)
```
#### Problem description
Dropping non-existing entries results in error, like
```KeyError: '[4] not found in axis'```
#### Expected Output
An empty output or no action if inplace. Possibly a warning, but surely not an error.
What is wrong with trying to drop the entries that are already not present? Ghost-busting should be legal.
#### Output of ``pd.show_versions()``
<details>
pandas: 0.24.2
</details> | {
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} | 3 | 2019-04-23T11:18:30Z | 2019-04-23T12:16:32Z | 2019-04-23T12:16:28Z | MEMBER | null | This reverts commit 695e89345a46c0cab06c479c83007f5adbb70ad5.
Closes https://github.com/pandas-dev/pandas/issues/18628 (reverts https://github.com/pandas-dev/pandas/pull/18661)
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} | 4 | 2019-04-23T14:10:53Z | 2021-07-02T05:33:22Z | null | NONE | null | In the [What's New - Other enhancements](https://pandas.pydata.org/pandas-docs/version/0.21/whatsnew.html#whatsnew-0180-enhancements-other) section from the 18.0 release it says that you can define the environment variable `AWS_S3_HOST`.
Therefore, when setting this environment variable, together with `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` I expected the following code to work.
```python
import pandas as pd
df = pd.DataFrame(dict(a=[1, 2, 3], b=[4, 5, 6]))
df.to_csv(fs.open('s3://bucket/key', mode='w'))
```
The script crashes with:
```
Exception ignored in: <bound method S3File.__del__ of <S3File bucket/key>>
Traceback (most recent call last):
File "/home/xxx/.local/share/virtualenvs/xxx/lib/python3.6/site-packages/s3fs/core.py", line 1518, in __del__
self.close()
File "/home/xxx/.local/share/virtualenvs/xxx/lib/python3.6/site-packages/s3fs/core.py", line 1496, in close
raise_from(IOError('Write failed: %s' % self.path), e)
File "<string>", line 3, in raise_from
OSError: Write failed: bucket/key
```
This is the exact same error as when I don't specify the variable `AWS_S3_HOST` at all.
I managed to get a working solution by directly interacting directly with `s3fs`:
```python
import os
import pandas as pd
import s3fs
fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': os.environ["AWS_S3_HOST"]})
df = pd.DataFrame(dict(a=[1, 2, 3], b=[4, 5, 6]))
df.to_csv(fs.open('s3://bucket/key', mode='w'))
```
Looks like this feature got (accidentally?) removed in `https://github.com/pandas-dev/pandas/commit/dc4b0708f36b971f71890bfdf830d9a5dc019c7b#diff-a37b395bed03f0404dec864a4529c97dL94`, when they switched from `boto` to `s3fs`.
Is it planned to support this environment variable again?
I'm sure that many companies, e.g. like the one I work for, hosts their own s3 server, e.g. with Minio, and don't use Amazon. | {
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} | 3 | 2019-04-23T17:39:25Z | 2019-05-05T06:59:22Z | 2019-04-24T12:23:57Z | CONTRIBUTOR | null | - [x] xref #25725
Remove usage of ``# coding=utf-8`` pattern from code base. | {
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} | 7 | 2019-04-23T18:08:47Z | 2019-05-01T20:03:21Z | 2019-04-28T15:51:08Z | CONTRIBUTOR | null | - [x] xref #25725 & #26196
Remove usage of ``-# -*- coding: utf-8 -*-`` pattern from code base.
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} | 6 | 2019-04-23T22:17:31Z | 2019-04-25T15:57:15Z | 2019-04-25T14:53:25Z | MEMBER | null | dump and load don't appear anywhere in the code base except for UltraJSON tests. Since they are part of the exposed API I figure makes just as much sense to remove | {
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} | 3 | 2019-04-23T22:52:59Z | 2019-04-25T15:57:29Z | 2019-04-24T21:00:57Z | MEMBER | null | There was a header file in the ujson source specifically meant to bridge compatibility between Py2 and Py3. With the Py2 support drop this could be removed and the source code simplified.
Most of these changes are simply verbatim, though there are probably a few follow ups on context to simplify further that could be made | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26200/events | https://github.com/pandas-dev/pandas/issues/26200 | 436,425,802 | MDU6SXNzdWU0MzY0MjU4MDI= | 26,200 | CI Failures for test_string_na_nat_conversion | {
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] | closed | false | null | [] | null | 2 | 2019-04-23T23:01:58Z | 2019-04-24T19:54:42Z | 2019-04-24T19:54:42Z | MEMBER | null | These just started showing up in the build today:
```python
___________ TestToDatetimeMisc.test_string_na_nat_conversion[False] ____________
[gw0] linux -- Python 3.7.3 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.datetimes.test_tools.TestToDatetimeMisc object at 0x7f38c92b73c8>
cache = False
@pytest.mark.parametrize('cache', [True, False])
def test_string_na_nat_conversion(self, cache):
# GH #999, #858
strings = np.array(['1/1/2000', '1/2/2000', np.nan,
'1/4/2000, 12:34:56'], dtype=object)
expected = np.empty(4, dtype='M8[ns]')
for i, val in enumerate(strings):
if isna(val):
expected[i] = iNaT
else:
expected[i] = parse(val)
result = tslib.array_to_datetime(strings)[0]
tm.assert_almost_equal(result, expected)
result2 = to_datetime(strings, cache=cache)
assert isinstance(result2, DatetimeIndex)
tm.assert_numpy_array_equal(result, result2.values)
malformed = np.array(['1/100/2000', np.nan], dtype=object)
# GH 10636, default is now 'raise'
msg = (r"\(u?'Unknown string format:', '1/100/2000'\)|"
"day is out of range for month")
with pytest.raises(ValueError, match=msg):
> to_datetime(malformed, errors='raise', cache=cache)
E AssertionError: Pattern '\(u?'Unknown string format:', '1/100/2000'\)|day is out of range for month' not found in 'Unknown string format: 1/100/2000'
```
Not sure what the root cause is. I figured should be good enough to relax the error message for the time being and not block CI for others.
@simonjayhawkins looks like you might have changed this test to add a more restrictive message so if you want to dive into further detail for a more comprehensive fix would certainly be welcome | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26202/events | https://github.com/pandas-dev/pandas/issues/26202 | 436,554,635 | MDU6SXNzdWU0MzY1NTQ2MzU= | 26,202 | how to merge two data frames with different dataframe | {
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} | [] | closed | false | null | [] | null | 1 | 2019-04-24T08:07:37Z | 2019-04-24T10:20:27Z | 2019-04-24T10:20:27Z | NONE | null | i have two data frames whose structure as per below. in need to fill value from one data frame to another on the basis of verification.
Data Frame1 =
id a b c c1 d d1 e e1 f
0 al al1 - 1 K 10 _ _ File1
1 al al2 - 1 _ _ _ _ File2
2 al al3 ma 1 P 1 L 100 File3
dataframe2 =
Column Column1 Column2
c cal ca
c1 cal1 ca1
d dal da
d1 dal1 da1
e eal ea
e1 eal1 ea1
Excepted output of dataframe 2 = it should be run on the basis of column F and create csv one by one with updated data like File1.csv and fill value only for file1 in csv
Column Column1 Column2 Value
c cal ca _
c1 cal1 ca1 1
d dal da K
d1 dal1 da1 10
e eal ea _
e1 eal1 ea1 _
dataframe3 =
Column1 Column2 Column Value Column1 Value
excepted output dataframe 3= for file1.csv , here file1.csv should updated in file1df3.csv with below output
Column1 Column2 Column Value Column1 Value
cal ca 1
dal da k 10
in dataframe 3, in we need to fill data from data frame 1 & data frame2. only those data should be filled in column1 column2 in which contains value in X1 column greater then 0.
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} | 3 | 2019-04-24T09:38:57Z | 2019-05-10T04:22:16Z | null | NONE | null | ```python
### Not working
pd.read_excel(
"test.xlsx",
na_values={"Test": ['#', 0]},
parse_dates=["Test"],
date_parser=lambda x: pd.to_datetime(x, format="%Y-%m-%d"),
)
### Working
pd.read_csv(
"test.txt",
na_values={"Test": ['#', 0]},
parse_dates=["Test"],
date_parser=lambda x: pd.to_datetime(x, format="%Y-%m-%d"),
)
```
read_excel behaves different from read_csv in replacing NA values and parsing dates.
Test case:
The test.txt and test.xlsx contain the same data, just one column with header "Test" and 5 entries where 0 and # both represent NA values.
```
Test
2012-10-01
0
2015-05-15
#
2017-09-09
```
The first one crashes while trying to parse a "#" character as date. | {
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} | 8 | 2019-04-24T09:43:45Z | 2019-04-24T15:05:25Z | 2019-04-24T15:05:20Z | CONTRIBUTOR | null | - [x] closes #N/A
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
`asv continuous -f 1.05 origin/master HEAD -b ^io.csv.ReadCSVParseSpecialDate -a warmup_time=2 -a sample_time=2`:
master | patch | ratio | test_name
-|-|-|-
276±1ms| 262±2ms| 0.95| io.csv.ReadCSVParseSpecialDate.time_read_special_date('hm')
16.6±0.2ms| 14.2±0.08ms| 0.85| io.csv.ReadCSVParseSpecialDate.time_read_special_date('mdY')
42.7±0.9ms| 35.0±0.2ms | 0.82| io.csv.ReadCSVParseSpecialDate.time_read_special_date('mY')
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https://api.github.com/repos/pandas-dev/pandas/issues/26205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26205/events | https://github.com/pandas-dev/pandas/issues/26205 | 436,671,073 | MDU6SXNzdWU0MzY2NzEwNzM= | 26,205 | `read_html` crash when table is empty | {
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} | 3 | 2019-04-24T12:35:16Z | 2019-04-25T15:29:36Z | 2019-04-25T15:29:36Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
foo = pd.read_html('<table><tbody><tr></tr></tbody></table>') # Crashed
print(foo)
```
#### Problem description
`pd.read_html` would crash if the input table is an empty table, as the sample code indicates. The crash output is as follows:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-441-efc751c3c2c9> in <module>
----> 1 pd.read_html('<table><tbody><tr></tr></tbody></table>')
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/io/html.py in read_html(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, tupleize_cols, thousands, encoding, decimal, converters, na_values, keep_default_na, displayed_only)
1092 decimal=decimal, converters=converters, na_values=na_values,
1093 keep_default_na=keep_default_na,
-> 1094 displayed_only=displayed_only)
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/io/html.py in _parse(flavor, io, match, attrs, encoding, displayed_only, **kwargs)
914 break
915 else:
--> 916 raise_with_traceback(retained)
917
918 ret = []
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/compat/__init__.py in raise_with_traceback(exc, traceback)
418 if traceback == Ellipsis:
419 _, _, traceback = sys.exc_info()
--> 420 raise exc.with_traceback(traceback)
421 else:
422 # this version of raise is a syntax error in Python 3
ValueError: No tables found matching pattern '.+'
```
From instinct, even if the table might be empty, it is not supposed to crash `pd.read_html`, returning an empty data frame might be a better way.
#### Expected Output
```python
foo = pd.read_html('<table><tbody><tr></tr></tbody></table>')
print(foo) # Empty dataframe
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.2
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.1
openpyxl: None
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/26206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26206/events | https://github.com/pandas-dev/pandas/issues/26206 | 436,849,646 | MDU6SXNzdWU0MzY4NDk2NDY= | 26,206 | REGR? no error anymore when converting out of bounds datetime64[non-ns] data | {
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} | 7 | 2019-04-24T18:41:36Z | 2020-01-06T20:53:21Z | 2019-12-30T17:52:01Z | MEMBER | null | Didn't directly find a related issue, but on master / 0.24 / 0.23, we see:
```
In [1]: pd.Series(np.array(['2262-04-12'], dtype='datetime64[D]'))
Out[1]:
0 1677-09-21 00:25:26.290448384
dtype: datetime64[ns]
```
while on pandas 0.22.0:
```
In [1]: pd.Series(np.array(['2262-04-12'], dtype='datetime64[D]'))
---------------------------------------------------------------------------
OutOfBoundsDatetime Traceback (most recent call last)
<ipython-input-1-b3f7cbbf1054> in <module>()
----> 1 pd.Series(np.array(['2262-04-12'], dtype='datetime64[D]'))
~/miniconda3/envs/pandas022/lib/python3.6/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
264 raise_cast_failure=True)
265
--> 266 data = SingleBlockManager(data, index, fastpath=True)
267
268 generic.NDFrame.__init__(self, data, fastpath=True)
~/miniconda3/envs/pandas022/lib/python3.6/site-packages/pandas/core/internals.py in __init__(self, block, axis, do_integrity_check, fastpath)
4400 if not isinstance(block, Block):
4401 block = make_block(block, placement=slice(0, len(axis)), ndim=1,
-> 4402 fastpath=True)
4403
4404 self.blocks = [block]
~/miniconda3/envs/pandas022/lib/python3.6/site-packages/pandas/core/internals.py in make_block(values, placement, klass, ndim, dtype, fastpath)
2955 placement=placement, dtype=dtype)
2956
-> 2957 return klass(values, ndim=ndim, fastpath=fastpath, placement=placement)
2958
2959 # TODO: flexible with index=None and/or items=None
~/miniconda3/envs/pandas022/lib/python3.6/site-packages/pandas/core/internals.py in __init__(self, values, placement, fastpath, **kwargs)
2468 def __init__(self, values, placement, fastpath=False, **kwargs):
2469 if values.dtype != _NS_DTYPE:
-> 2470 values = tslib.cast_to_nanoseconds(values)
2471
2472 super(DatetimeBlock, self).__init__(values, fastpath=True,
pandas/_libs/tslib.pyx in pandas._libs.tslib.cast_to_nanoseconds()
pandas/_libs/tslib.pyx in pandas._libs.tslib._check_dts_bounds()
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-04-12 00:00:00
In [2]: pd.__version__
Out[2]: '0.22.0'
```
cc @jbrockmendel any idea if this was changed on purpose or to what refactoring could have been the cause of this change?
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} | 3 | 2019-04-24T21:44:03Z | 2019-04-24T22:27:22Z | 2019-04-24T22:27:14Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry | {
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} | 4 | 2019-04-25T09:27:53Z | 2019-05-15T13:01:28Z | 2019-05-15T13:01:28Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
d = pd.DataFrame({1: [], 2: []})
g = d.groupby(1)
g[2].transform(lambda x: x)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\python37\lib\site-packages\pandas\core\groupby\generic.py", line 945, in transform
result = concat(results).sort_index()
File "C:\python37\lib\site-packages\pandas\core\reshape\concat.py", line 228, in concat
copy=copy, sort=sort)
File "C:\python37\lib\site-packages\pandas\core\reshape\concat.py", line 262, in __init__
raise ValueError('No objects to concatenate')
```
#### Problem description
Crashes on SeriesGroupby obj with zero length, which came from an empty dataframe. Would be nicer if pandas can handle this case without raising errors, by for example, just return an empty series. Thanks.
#### Expected Output
```
Series([], Name: 2, dtype: float64)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.1
pip: 19.0.3
setuptools: 41.0.1
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-04-25T09:38:57Z | 2020-04-25T22:05:11Z | 2020-04-25T22:05:11Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
# generate a simple df with weekly DateTimeIndex, group and a value
df = pd.DataFrame({
'group':['Group1','Group2','Group3']*3,
'value':np.random.randint(100, 1000, size=9)},
index=pd.date_range('1991-10-2',periods=3, freq='W-MON').repeat(3)
)
# this works as expected
new_value = df.groupby('group')[['value']].apply(
lambda x: x.reindex(pd.date_range(x.index.min(), x.index.max(), freq='W-MON'), fill_value=0)
)
# this fails
new_value = df.groupby('group').value.apply(
lambda x: x.reindex(pd.date_range(x.index.min(), x.index.max(), freq='W-MON'), fill_value=0)
)
```
#### Problem description
We have a DF with a group and DateTimeIndex, we want to `reindex` the values per group to make sure all groups have all the appropriate weekly DateTimeIndex. This works as expected when there is a gap to be resolved by the `reindex`ing... However, it fails when there's no gap to be filled by `reindex`.
`ValueError: cannot reindex from a duplicate axis`
This issue is resolved by turning the corresponding Series into a DF by `[[]]` syntax.
#### Expected Output
The original Series.
#### Output of ``pd.show_versions()``
<details>
installed versions
------------------
commit: none
python: 3.6.8.final.0
python-bits: 64
os: linux
os-release: 4.4.0-43-microsoft
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: 3.5.1
pip: 19.0.3
setuptools: 41.0.0
cython: 0.28.2
numpy: 1.14.3
scipy: 1.1.0
pyarrow: none
xarray: none
ipython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2019.1
blosc: none
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: none
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: none
psycopg2: none
jinja2: 2.10
s3fs: none
fastparquet: none
pandas_gbq: none
pandas_datareader: none
gcsfs: None
</details>
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 5 | 2019-04-25T14:13:41Z | 2021-07-02T05:34:50Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> ix = pd.MultiIndex(levels=[['B', 'A'], ['x']], codes=[[-1, 0, 1], [0, 0, 0]],
... names=['symbol', 'date'])
>>> ix._sort_levels_monotonic()
MultiIndex(levels=[['A', 'B'], ['x']],
codes=[[0, 1, 0], [0, 0, 0]],
names=['symbol', 'date'])
# MultiIndex silently accepts Nan in codes when inside a NumPy array and cast to 0:
>>> pd.MultiIndex(levels=[['B', 'A'], ['x']], codes=[np.array([np.nan, 0, 1]), [0, 0, 0]],
... names=['symbol', 'date'])
MultiIndex(levels=[['B', 'A'], ['x']],
codes=[[0, 0, 1], [0, 0, 0]],
names=['symbol', 'date'])
```
#### Problem description
When sorting a `MultiIndex`, NaNs in any level which is not already sorted are replaced with the first non-null value in the sorted orders. Effectively, the `-1` values in the `codes` get replaced with `0`. I've tracked this to two separate parts. The first part is in this line in `MultiIndex._sort_levels_monotonic`:
level_codes = algos.take_1d(ri, level_codes)
The output has `np.nan` where the input had `-1`. This could be fixed by:
level_codes = algos.take_1d(ri, level_codes, fill_value=-1)
The second part, which also seems unexpected to me, is that `MultiIndex` silently accepts NaN in codes when inside a NumPy array and casts to 0 (see second part of example above).
In particular, this is inconsistent with the behaviour when we pass NaNs inside a `list` which throws an error:
```python
>>> pd.MultiIndex(levels=[['B', 'A'], ['x']], codes=[[np.nan, 0, 1], [0, 0, 0]])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/dtc/conda_envs/condron/envs/pd24/lib/python3.6/site-packages/pandas/util/_decorators.py", line 188, in wrapper
return func(*args, **kwargs)
File "/opt/dtc/conda_envs/condron/envs/pd24/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 235, in __new__
result._set_codes(codes, copy=copy, validate=False)
File "/opt/dtc/conda_envs/condron/envs/pd24/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 683, in _set_codes
for lev, level_codes in zip(self.levels, codes))
File "/opt/dtc/conda_envs/condron/envs/pd24/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 683, in <genexpr>
for lev, level_codes in zip(self.levels, codes))
File "/opt/dtc/conda_envs/condron/envs/pd24/lib/python3.6/site-packages/pandas/core/indexes/frozen.py", line 192, in _ensure_frozen
array_like = coerce_indexer_dtype(array_like, categories)
File "/opt/dtc/conda_envs/condron/envs/pd24/lib/python3.6/site-packages/pandas/core/dtypes/cast.py", line 556, in coerce_indexer_dtype
return ensure_int8(indexer)
File "pandas/_libs/algos_common_helper.pxi", line 333, in pandas._libs.algos.ensure_int8
ValueError: cannot convert float NaN to integer
```
#### Expected Output
I think the expected output of `_sort_levels_monotonic` in this case would preserve the `-1` codes. Patching `_sort_levels_monotonic` with this fix I get:
```python
>>> s = pd.Series([1, 2, 3], ix)
>>> s.sort_index(na_position='first')
symbol date
NaN x 1
A x 3
B x 2
>>> s.sort_index(na_position='last')
symbol date
A x 3
B x 2
NaN x 1
```
Also, I would expect the following to throw the same `ValueError`
```python
>>> pd.MultiIndex(levels=[['B', 'A'], ['x']], codes=[np.array([np.nan, 0, 1]), [0, 0, 0]])
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.162-94.72-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 9.0.1
setuptools: 36.4.0
Cython: None
numpy: 1.13.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-04-25T17:09:15Z | 2020-01-16T00:34:11Z | 2019-04-26T00:45:12Z | MEMBER | null | Continuing cleanup of ujson source there are some preprocessor directives in there which are no longer valid given minimum Python and NumPy versions | {
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] | closed | false | null | [] | null | 7 | 2019-04-25T18:31:14Z | 2019-09-05T14:52:58Z | 2019-09-05T14:52:58Z | MEMBER | null | I've been spending some time cleaning up the integration with ujson in our source code. After enabling some debugging directives and running the test suite I noticed that there were quite a few places in objToJSON.c which were not being hit. This can point to either dead code or a lack of test coverage, so these can be whittled down.
In my own repo I have a branch of this where each of the items are commented with `// not hit in tests` which you can see here:
https://github.com/WillAyd/pandas/blob/dead-json/pandas/_libs/src/ujson/python/objToJSON.c
Planning to go through these individually and see what we can do. Extra set of eyes would certainly be welcome | {
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