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Because DatetimeIndex and TimedeltaIndex intersections are similar, I have moved the intersection function of both classes up into Datetimelike after discussions with @reidy-p @jorisvandenbossche. At the same time for PeriodIndex I have used Index.intersection instead. Additionally I have added some tests to TimedeltaIndex recycling some tests from DatetimeIndex.
Re-open from #25121 | {
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- [X] tests added / passed
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When printing column labels, check if they are floats and if they are, then round according to display.precision preferences | {
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} | 3 | 2019-03-29T00:58:32Z | 2019-03-30T01:23:10Z | 2019-03-29T12:13:31Z | MEMBER | null | Remaining is_terminal function is used in `core/config_init.py`, and as this function is not meant to be public, we can move it there.
This will help with dependency organization of pandas' modules.
Follow-up to #25886.
cc @jbrockmendel | {
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} | 0 | 2019-03-29T01:20:12Z | 2019-03-29T02:49:30Z | null | MEMBER | null | see also #17280
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
float_val = 0.55555555
index=pd.MultiIndex.from_tuples([(float_val,float_val)], names=[float_val,float_val])
df = pd.DataFrame([float_val], index=index, columns=index)
pd.set_option('display.precision', 3)
df
```
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th>0.55555555</th>
<th>0.556</th>
</tr>
<tr>
<th></th>
<th>0.55555555</th>
<th>0.556</th>
</tr>
<tr>
<th>0.55555555</th>
<th>0.55555555</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>0.556</th>
<th>0.556</th>
<td>0.556</td>
</tr>
</tbody>
</table>
</div>
#### Problem description
the float valued index names are not displayed with a precision of 3.
#### Expected Output
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th>0.556</th>
<th>0.556</th>
</tr>
<tr>
<th></th>
<th>0.556</th>
<th>0.556</th>
</tr>
<tr>
<th>0.556</th>
<th>0.556</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>0.556</th>
<th>0.556</th>
<td>0.556</td>
</tr>
</tbody>
</table>
</div>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.25.0.dev0+332.g96a128eaa
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.6.3
Cython: 0.29.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 9 | 2019-03-29T05:30:30Z | 2019-04-01T16:42:07Z | 2019-04-01T11:51:04Z | CONTRIBUTOR | null | also remove this module from mypy.ini
- [x] closes #25899
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
float_val = 0.55555555
df = pd.DataFrame([float_val, 'foo'], index=[float_val, 'foo'])
pd.set_option('display.precision', 3)
df
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.55555555</th>
<td>0.556</td>
</tr>
<tr>
<th>foo</th>
<td>foo</td>
</tr>
</tbody>
</table>
</div>
#### Problem description
the float valued index values are not displayed with a precision of 3. The expected behavior is observed with a `Float64Index` when all index values are floats.
#### Expected Output
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.556</th>
<td>0.556</td>
</tr>
<tr>
<th>foo</th>
<td>foo</td>
</tr>
</tbody>
</table>
</div>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.25.0.dev0+332.g96a128eaa
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.6.3
Cython: 0.29.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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Note: complex numbers are covered by #25514 and #25745 for `to_string()`
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
float_val = 0.55555555
complex_val = complex(float_val,float_val)
tuple_val= float_val,float_val
df = pd.DataFrame([float_val, complex_val, tuple_val])
pd.set_option('display.precision', 3)
df
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0.556</td>
</tr>
<tr>
<th>1</th>
<td>(0.55555555+0.55555555j)</td>
</tr>
<tr>
<th>2</th>
<td>(0.55555555, 0.55555555)</td>
</tr>
</tbody>
</table>
#### Problem description
not all the float values are displayed with a precision of 3.
#### Expected Output
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0.556</td>
</tr>
<tr>
<th>1</th>
<td>(0.556+0.556j)</td>
</tr>
<tr>
<th>2</th>
<td>(0.556, 0.556)</td>
</tr>
</tbody>
</table>
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 4 | 2019-03-29T15:34:03Z | 2019-04-23T11:23:46Z | 2019-04-23T11:23:39Z | NONE | null | `pandas==0.24.2`
#### Code
```python
#!/usr/bin/env python
import pandas as pd
df = pd.DataFrame({'col0': []})
s3_filepath = 's3://some-bogus-bucket/df.parquet'
try:
print('>> TEST: pd.DataFrame.to_parquet using PyArrow')
df.copy().to_parquet(s3_filepath, engine='pyarrow')
except OSError:
print('>>>> TEST: OSError raised!')
else:
print('>>>> TEST: OSError **NOT** raised!')
```
#### Output
```
>> TEST: pd.DataFrame.to_parquet using PyArrow
Exception ignored in: <bound method S3File.__del__ of <S3File some-bogus-bucket/df.parquet>>
Traceback (most recent call last):
File "<redacted>/lib/python3.6/site-packages/s3fs/core.py", line 1476, in __del__
self.close()
File "<redacted>/lib/python3.6/site-packages/s3fs/core.py", line 1454, in close
raise_from(IOError('Write failed: %s' % self.path), e)
File "<string>", line 3, in raise_from
OSError: Write failed: some-bogus-bucket/df.parquet
>>>> TEST: OSError **NOT** raised!
```
#### Problem description
The PyArrowImpl Parquet writer fails silently when it encounters errors while writing to S3. In the above example, I try writing a parquet file using `engine='pyarrow'` to a phony bucket `some-bogus-bucket` which is causing `boto3` is raising a `NoSuchBucket` exception "under-the-hood." While the write clearly does fail, it does *not* raise an `OSError`, as is expected and as is the behavior when using the `engine='fastparquet'`.
I dug a little more into this and tried writing to S3 using the `pyarrow` lib directly in two different ways:
```
from s3fs import S3File, S3FileSystem
import pyarrow as pa
import pyarrow.parquet as pq
# use S3File --> fails silently
pq.write_table(pa.Table.from_pandas(df.copy()),
S3File(S3FileSystem(), s3_filepath, mode='wb'))
# use filepath and S3FileSystem --> raises OSError, as expected
pq.write_table(pa.Table.from_pandas(df.copy()),
s3_filepath,
filesystem=S3FileSystem())
```
The first, which is similar to how `pandas.io.parquet.PyArrowImpl` calls `pyarrow.write_table`, fails while the second, which explicitly uses an S3 filepath and an `S3FileSystem()`, does not.
#### Expected Output
```
>> TEST: pd.DataFrame.to_parquet using PyArrow
>>>> TEST: OSError raised!
Exception ignored in: <bound method S3File.__del__ of <S3File some-bogus-bucket/df.parquet>>
Traceback (most recent call last):
File "<redacted>/lib/python3.6/site-packages/s3fs/core.py", line 1476, in __del__
self.close()
File "<redacted>/lib/python3.6/site-packages/s3fs/core.py", line 1454, in close
raise_from(IOError('Write failed: %s' % self.path), e)
File "<string>", line 3, in raise_from
OSError: Write failed: some-bogus-bucket/df.parquet
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
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: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: 0.12.1
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.4
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: 2.7.4 (dt dec pq3 ext lo64)
jinja2: None
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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} | 30 | 2019-03-29T15:38:45Z | 2019-04-20T16:52:55Z | 2019-04-20T16:52:25Z | CONTRIBUTOR | null | This tremendously speeds up parsing of certain date patterns: `MM/YYYY`, `MM/DD/YYYY` and `DD/MM/YYYY` (where separator is one of ` `, `/`, `-`, and `\`), as previously they were parsed using `dateutil.parse` which is implemented in Python.
- [x] closes N/A
- [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 | 4 | 2019-03-29T16:54:38Z | 2019-04-10T05:56:13Z | 2019-04-10T01:40:05Z | MEMBER | null | Mostly driven by MyPy (which has it's own import mechanism) we get 67 failures as of now on master for imports coming from the top level pandas namespace:
<details>
```sh
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'DatetimeIndex'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'concat'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'isna'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'to_datetime'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'to_timedelta'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'DatetimeIndex'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'Index'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'Int64Index'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'PeriodIndex'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'Series'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'SparseDataFrame'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'SparseSeries'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'TimedeltaIndex'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'concat'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'isna'
pandas/io/pytables.py:30: error: Module 'pandas' has no attribute 'to_datetime'
pandas/io/parquet.py:8: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Categorical'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'CategoricalIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'DatetimeIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Float64Index'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Index'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Int64Index'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Interval'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'IntervalIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'NaT'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Panel'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Period'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'PeriodIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'RangeIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Series'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'TimedeltaIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Timestamp'
pandas/io/html.py:17: error: Module 'pandas' has no attribute 'Series'
pandas/io/feather_format.py:7: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/feather_format.py:7: error: Module 'pandas' has no attribute 'Int64Index'
pandas/io/feather_format.py:7: error: Module 'pandas' has no attribute 'RangeIndex'
pandas/io/json/table_schema.py:15: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/json/normalize.py:11: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'Series'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'isna'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'to_datetime'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Categorical'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Index'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Interval'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'IntervalIndex'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Series'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Timedelta'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Timestamp'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'to_datetime'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'to_timedelta'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Categorical'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'DataFrame'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Index'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Series'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Timedelta'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'DataFrame'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'Index'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'Series'
```
</details>
Specifically with MyPy doing explicit imports of these versus current wildcard helps, as does importing directly from the modules where these are defined.
With MyPy if we wanted to we could always ignore these imports, but at the same time I was wondering if we really should be importing from the top level within the code base as it theoretically makes our import machinery more convoluted than it needs to be.
I spoke with @jorisvandenbossche on Gitter yesterday about this and there were two things we discussed:
- Option 1: Always import from where the object is defined, so you would always do `from pandas.core.frame import DataFrame`
- Option 2: Leverage the API of particular subpackages when outside of them, import directly within. So if you are in `pandas.core` you would still do `from pandas.core.frame import DataFrame`, but outside of core you would do `from pandas.core.api import DataFrame`
FWIW I think option 2 is kind of nice, because it abstracts where within sub packages we choose to place particular items and allows things like `from pandas.core.api import DataFrame, Series` instead of having two separate imports
Curious if anyone has any thoughts or objections to either of the options presented above versus current allowance of imports from top level | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25924/events | https://github.com/pandas-dev/pandas/pull/25924 | 427,118,495 | MDExOlB1bGxSZXF1ZXN0MjY1ODE4NTU1 | 25,924 | day_of_year | {
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... | closed | false | null | [] | null | 8 | 2019-03-29T18:16:23Z | 2019-06-02T19:49:03Z | 2019-06-02T19:48:52Z | NONE | null | - [ ] closes #9606
- [ ] 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/25925 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25925/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25925/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25925/events | https://github.com/pandas-dev/pandas/pull/25925 | 427,199,278 | MDExOlB1bGxSZXF1ZXN0MjY1ODgyMTEy | 25,925 | CLN: Removed second version of xstrtod in parser_helper.h | {
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} | 8 | 2019-03-29T22:20:57Z | 2019-04-04T08:12:11Z | 2019-04-03T20:28:19Z | CONTRIBUTOR | null | - [x] closes #19361
- [x] tests added / 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/25926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25926/events | https://github.com/pandas-dev/pandas/pull/25926 | 427,203,536 | MDExOlB1bGxSZXF1ZXN0MjY1ODg1NDYw | 25,926 | BUG: Fix memory leak in Rolling.min and Rolling.max (#25893) | {
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} | 10 | 2019-03-29T22:40:28Z | 2019-04-01T23:01:24Z | 2019-04-01T22:27:46Z | CONTRIBUTOR | null | - [x] closes #25893
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This fixes an issue that caused a memory leak in `window._roll_min_max`. Apparently, when you pass an ndarray as an argument that you `cdef`ed in the calling function, that memory never gets released. This is not an issue unique to pandas, as evidenced by [this stackflow question](https://stackoverflow.com/questions/29247373/memory-leak-calling-cython-function-with-large-numpy-array-parameters). It may be a numpy bug.
There are a few potential fixes for this:
- Moving everything into one function
- Pass pointers to the functions
- Use [memoryviews](https://cython.readthedocs.io/en/latest/src/userguide/memoryviews.html#memoryviews)
As memoryviews is the least disruptive change and is just as performant as what was already being used, that's what I went with for this fix.
I did not add a test with this PR, as determining memory usage is platform specific (and rather wonky) unless you use a library like `psutils`, and even with that may have problems. | {
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} | 16 | 2019-03-29T23:25:23Z | 2020-11-07T23:47:12Z | 2020-11-07T23:47:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
For:
```
result = a2b.loc[vals] # pd.Series()[ np.array ]
```
If a2b is a series that maps `{int64:int64}` and vals is an `int64` array, the result should be a series that maps `{int64:int64}`, or a KeyError should be thrown
Pasteable repo:
```
import pandas as pd
import numpy as np
a2b = pd.Series(
index = np.array([ 9724501000001103, 9724701000001109, 9725101000001107,
9725301000001109, 9725601000001103, 9725801000001104,
9730701000001104, 10049011000001109, 10328511000001105]),
data = np.array([999000011000001104, 999000011000001104, 999000011000001104,
999000011000001104, 999000011000001104, 999000011000001104,
999000011000001104, 999000011000001104, 999000011000001104])
)
assert a2b.dtype==np.int64
assert a2b.index.dtype==np.int64
key = np.array([ 9724501000001103, 9724701000001109, 9725101000001107,
9725301000001109, 9725601000001103, 9725801000001104,
9730701000001104,
10047311000001102, # Misin in a2b.index
10049011000001109,
10328511000001105])
result = a2b.loc[key]
result
assert result.dtype==np.int64
assert result.index.dtype==np.int64
```
What happens:
```python
In [2]: import pandas as pd
...: import numpy as np
...: a2b = pd.Series(
...: index = np.array([ 9724501000001103, 9724701000001109, 9725101000001107,
...: 9725301000001109, 9725601000001103, 9725801000001104,
...: 9730701000001104, 10049011000001109, 10328511000001105]),
...: data = np.array([999000011000001104, 999000011000001104, 999000011000001104,
...: 999000011000001104, 999000011000001104, 999000011000001104,
...: 999000011000001104, 999000011000001104, 999000011000001104])
...: )
...: assert a2b.dtype==np.int64
...: assert a2b.index.dtype==np.int64
...: key = np.array([ 9724501000001103, 9724701000001109, 9725101000001107,
...: 9725301000001109, 9725601000001103, 9725801000001104,
...: 9730701000001104,
...: 10047311000001102, # Misin in a2b.index
...: 10049011000001109,
...: 10328511000001105])
...: result = a2b.loc[key]
...: result
...:
Out[2]:
9.990000e+17 NaN
9.990000e+17 NaN
9.990000e+17 NaN
9.990000e+17 NaN
9.990000e+17 NaN
9.990000e+17 NaN
9.990000e+17 NaN
NaN NaN
9.990000e+17 NaN
9.990000e+17 NaN
dtype: float64
In [3]: assert result.dtype==np.int64
...: assert result.index.dtype==np.int64
...:
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-3-be86ec17a393> in <module>()
----> 1 assert result.dtype==np.int64
2 assert result.index.dtype==np.int64
AssertionError:
```
#### Problem description
I don't like this behavior because:
- I have quietly lost all my data due to cast to float64
- in other calls to __getitem__ a KeyError is raised if a value is not found in the index.
#### Expected Output
Asserts should not fail.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
In [4]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.candidate.1
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.22.0
pytest: None
pip: 18.1
setuptools: 40.6.2
Cython: 0.29.1
numpy: 1.16.1
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.5.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 2.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.9999999
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: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25928/events | https://github.com/pandas-dev/pandas/pull/25928 | 427,241,512 | MDExOlB1bGxSZXF1ZXN0MjY1OTEzNzky | 25,928 | BUG: Fix #16363 - Prevent visit_BinOp from accessing value on UnaryOp | {
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} | 8 | 2019-03-30T04:31:22Z | 2019-04-12T20:18:52Z | 2019-04-12T11:52:17Z | CONTRIBUTOR | null | - [x] closes #16363
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Checking `hasattr(side, 'value')` resolves the bug and is consistent with the way that other methods of the `BaseExprVisitor` class work (e.g., both `visit_Call_35` and `visit_Call_legacy` check for 'value'). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25929/events | https://github.com/pandas-dev/pandas/issues/25929 | 427,261,757 | MDU6SXNzdWU0MjcyNjE3NTc= | 25,929 | import pickle | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25930 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25930/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25930/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25930/events | https://github.com/pandas-dev/pandas/issues/25930 | 427,318,025 | MDU6SXNzdWU0MjczMTgwMjU= | 25,930 | Add Test to Explicitly Check Private Modules Exposed via API | {
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https://github.com/pandas-dev/pandas/pull/25884#discussion_r270638199
Want to update test_api to explicitly check private modules that may also be in the namespace | {
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} | 3 | 2019-03-30T22:57:03Z | 2019-03-31T18:01:55Z | 2019-03-31T18:00:35Z | CONTRIBUTOR | null | - [x] closes #23986 by adding regression test
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-03-31T00:04:35Z | 2019-05-01T10:08:51Z | 2019-05-01T10:08:51Z | MEMBER | null | Here's the list of current errors:
```sh
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'DatetimeIndex'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'concat'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'isna'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'to_datetime'
pandas/io/stata.py:32: error: Module 'pandas' has no attribute 'to_timedelta'
pandas/io/stata.py:1730: error: Name 'data_label' already defined on line 1052
```
For the former we should just use absolute imports instead of pulling from top level pandas object (per discussion in #25923). The last issue I think is just a method shadowing a instance variable which I think can safely be removed | {
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} | 1 | 2019-03-31T00:07:43Z | 2019-04-11T00:47:47Z | 2019-04-11T00:47:47Z | MEMBER | null | The parquet, packers, html, feather_format, json.table_schema, json.normalize, and json.json could all be removed from the mypy.ini blacklist if we switch them over to absolute imports per the discussion in #25923
Here's the complete list of errors:
```sh
pandas/io/parquet.py:8: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Categorical'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'CategoricalIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'DatetimeIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Float64Index'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Index'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Int64Index'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Interval'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'IntervalIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'NaT'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Panel'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Period'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'PeriodIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'RangeIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Series'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'TimedeltaIndex'
pandas/io/packers.py:58: error: Module 'pandas' has no attribute 'Timestamp'
pandas/io/html.py:17: error: Module 'pandas' has no attribute 'Series'
pandas/io/feather_format.py:7: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/feather_format.py:7: error: Module 'pandas' has no attribute 'Int64Index'
pandas/io/feather_format.py:7: error: Module 'pandas' has no attribute 'RangeIndex'
pandas/io/json/table_schema.py:15: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/json/normalize.py:11: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'DataFrame'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'Series'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'isna'
pandas/io/json/json.py:14: error: Module 'pandas' has no attribute 'to_datetime'
``` | {
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} | 1 | 2019-03-31T00:11:40Z | 2019-04-15T20:45:20Z | 2019-04-15T20:45:20Z | MEMBER | null | The tile, merge and concat modules could all be removed from the mypy.ini blacklist if we switch them over to absolute imports per the discussion in #25923
Here's the complete list of errors:
```sh
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Categorical'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Index'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Interval'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'IntervalIndex'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Series'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Timedelta'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'Timestamp'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'to_datetime'
pandas/core/reshape/tile.py:16: error: Module 'pandas' has no attribute 'to_timedelta'
pandas/core/reshape/merge.py:11: error: Module 'pandas._libs' has no attribute 'join'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Categorical'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'DataFrame'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Index'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Series'
pandas/core/reshape/merge.py:25: error: Module 'pandas' has no attribute 'Timedelta'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'DataFrame'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'Index'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'MultiIndex'
pandas/core/reshape/concat.py:9: error: Module 'pandas' has no attribute 'Series'
```
I'm not 100% sure about the import from pandas._libs so that may be an exception here, but would certainly take a PR for the other two modules at the very least | {
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} | 3 | 2019-03-31T02:12:55Z | 2019-04-02T00:17:31Z | 2019-04-01T11:39:22Z | CONTRIBUTOR | null | - [x] closes #25930
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 13 | 2019-03-31T03:55:25Z | 2020-01-06T02:51:06Z | 2019-04-15T20:45:20Z | CONTRIBUTOR | null | - [x] closes #25934
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 4 | 2019-03-31T04:05:38Z | 2019-04-01T18:06:01Z | 2019-04-01T11:38:47Z | MEMBER | null | now that we're py3-only, `is_string_object(obj)` reduces to just `isinstance(obj, str)` which cython already optimizes. | {
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} | 5 | 2019-03-31T04:15:38Z | 2019-04-05T02:36:00Z | 2019-04-05T02:28:15Z | MEMBER | null | Two motivations:
1) #25853 re-implements some tz-conversion code in order to avoid circular imports. By separating out this tightly-scoped, low-dependency chunk of code, we can avoid both duplication and circularity.
2) there has been scope-creep in `tslibs.conversion` which was originally intended for functions converting between datetime-like objects, but has picked up a bunch of timezone-conversion functions (separated by this PR) and normalization functions (not affected by this PR) | {
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} | 16 | 2019-03-31T05:13:52Z | 2020-01-06T02:49:39Z | 2019-04-11T00:47:48Z | CONTRIBUTOR | null | - [x] closes #25933
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 26 | 2019-03-31T05:53:19Z | 2020-01-06T02:50:21Z | 2019-04-10T12:59:43Z | CONTRIBUTOR | null | - [x] closes ##25932
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} | 4 | 2019-03-31T14:22:08Z | 2019-12-05T13:44:57Z | 2019-04-29T00:47:46Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pandas.util.testing as tm
df = pd.DataFrame(index = tm.makeMultiIndex())
print(df.to_html(col_space=100))
```
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th style="min-width: 100;"></th>
<th style="min-width: 100;"></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="2" valign="top"style="min-width: 100;">foo</th>
<th style="min-width: 100;">1</th>
</tr>
<tr>
<th style="min-width: 100;">2</th>
</tr>
<tr>
<th rowspan="2" valign="top"style="min-width: 100;">bar</th>
<th style="min-width: 100;">1</th>
</tr>
<tr>
<th style="min-width: 100;">2</th>
</tr>
</tbody>
</table>
```
#### Problem description
The `col_space` parameter expects an int..
https://github.com/pandas-dev/pandas/blob/cb89bc0c012a3154fae394a7299e72ec75956c9b/pandas/io/formats/format.py#L47-L48
it should be either px of % for HTML but this raises an error if you try.
```
print(df.to_html(col_space='100px'))
```
gives `TypeError: '>' not supported between instances of 'str' and 'int'`
The `<th style="min-width: 100;">` is also applied needlessly to the `<th>` tags within `<tbody>`
#### Expected Output
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th style="min-width: 100px;"></th>
<th style="min-width: 100px;"></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="2" valign="top">foo</th>
<th>1</th>
</tr>
<tr>
<th>2</th>
</tr>
<tr>
<th rowspan="2" valign="top">bar</th>
<th>1</th>
</tr>
<tr>
<th>2</th>
</tr>
</tbody>
</table>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25942 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25942/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25942/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25942/events | https://github.com/pandas-dev/pandas/issues/25942 | 427,408,336 | MDU6SXNzdWU0Mjc0MDgzMzY= | 25,942 | Add ArrayLike an Dtype definitions to pandas._typing | {
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} | 0 | 2019-03-31T15:23:29Z | 2019-04-10T13:00:42Z | 2019-04-10T13:00:42Z | CONTRIBUTOR | null | The existing type hints in the codebase suggest a couple of easy additions to the pandas._typing module, including
- [ ] ArrayLike = `Union[ABCExtensionArray, np.ndarray]` and
- [ ] Dtype = `Union[str, np.dtype, ExtensionDtype, Type]`.
These should be added to the typing module and the matching types replaced with the newly defined ones where they occur.
Crossreferences: #25829 and #25791. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25943 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25943/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25943/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25943/events | https://github.com/pandas-dev/pandas/pull/25943 | 427,408,685 | MDExOlB1bGxSZXF1ZXN0MjY2MDIxOTMz | 25,943 | #25942 Added ArrayLike and Dtype to pandas._typing | {
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} | 4 | 2019-03-31T15:26:57Z | 2019-04-10T13:26:36Z | 2019-04-10T13:00:42Z | CONTRIBUTOR | null | - [x] closes #25942
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/25944 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25944/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25944/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25944/events | https://github.com/pandas-dev/pandas/issues/25944 | 427,423,557 | MDU6SXNzdWU0Mjc0MjM1NTc= | 25,944 | Feature suggestion: nullif method | {
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} | [] | closed | false | null | [] | null | 1 | 2019-03-31T17:54:06Z | 2019-03-31T18:03:18Z | 2019-03-31T18:03:18Z | MEMBER | null | I know that the `pandas` API is already fairly saturated with methods, but I think one that many users might like would be `nullif` (analogous to the Postgres `nullif` function) as a sort of inverse to `fillna`. Idea being that if you have a `DataFrame` or `Series` and there are certain values that should be treated as `np.nan` that aren't (e.g., sentinel values, impossible values, etc.), you could call `Series.nullif(value)` to return a new `Series` with any instance of `value` set to `np.nan`. Optionally you could pass in an array of values to set all elements of the array to null, or a boolean-valued function that sets to null any elements that evaluate to `True` (e.g., all negative numbers of a column that should be non-negative, non-numeric values in a numeric column).
Of course this could be done with existing methods like `map` or `applymap` so this would more or less be a syntactic / conceptual shortcut (as in the case of `fillna`). | {
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} | 2 | 2019-04-01T14:49:00Z | 2019-04-09T17:28:20Z | 2019-04-09T17:28:20Z | CONTRIBUTOR | null | ```python
import pandas as pd
df = pd.read_csv('s3://bucket/key#1.csv')
df = pd.read_csv('s3://bucket/key%231.csv')
```
#### Problem description
Pandas can't open an object from S3 if it has a # sign in the URL, both in the case where the URL path is percent encoded and not. The reason is that urllib.parse.urlparse(), which is used in io/s3.py to parse the URL, treats the # sign as the beginning of the URL fragment, and thus it is lost (in the case of not percent encoded).
I see two possible solutions to the problem, but I'm not sure which one is best, since there does not seem to be a 'specification' for the S3 URL scheme (at least that I can find):
1. Use `allow_fragments=False` when calling `urllib.parse.urlparse()`. This would allow the non-percent-encoded case to work, but seems slightly wrong.
2. Call `urllib.parse.unquote()` on S3 paths before passing to s3fs. s3fs *seems* to want just a bucket/key as input, so pandas would have to remove the percent encoding. This would allow the percent-encoded case to work. It seems a bit more correct, but it might change some existing behavior where users could be loading URLs with % characters in them. | {
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} | 11 | 2019-04-01T16:42:07Z | 2019-04-04T14:40:55Z | 2019-04-04T14:01:44Z | CONTRIBUTOR | null | - [x] closes N/A
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This isn't giving much speedup because this is simple change, but for some certain inputs like empty datetime fields in csv it gives some speed (because empty fields are parsed as float `NaN`-s). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25947 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25947/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25947/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25947/events | https://github.com/pandas-dev/pandas/pull/25947 | 427,812,045 | MDExOlB1bGxSZXF1ZXN0MjY2MzI0MTM1 | 25,947 | Remove references to T-Series | {
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} | [] | closed | false | null | [] | null | 0 | 2019-04-01T16:55:24Z | 2019-04-01T18:38:33Z | 2019-04-01T18:38:33Z | NONE | null | - [×] closes #xxxx
- [✓] tests added / passed
- [△] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [👊] whatsnew entry
This pull request removes any references to T-Series from the source code. It is a breaking change for any old code that relies on the old module name, so it will probably require a new major version release, but that is a small price to pay. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25948 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25948/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25948/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25948/events | https://github.com/pandas-dev/pandas/issues/25948 | 427,833,181 | MDU6SXNzdWU0Mjc4MzMxODE= | 25,948 | Fix Type Annotations in pandas/_version.py | {
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} | 1 | 2019-04-01T17:49:32Z | 2019-04-04T12:56:57Z | 2019-04-04T12:56:29Z | CONTRIBUTOR | null | Part of #25882
Current errors are as follows:
pandas\_version.py:51: error: Need type annotation for 'LONG_VERSION_PY'
pandas\_version.py:52: error: Need type annotation for 'HANDLERS'
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https://api.github.com/repos/pandas-dev/pandas/issues/25949 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25949/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25949/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25949/events | https://github.com/pandas-dev/pandas/pull/25949 | 427,848,395 | MDExOlB1bGxSZXF1ZXN0MjY2MzUyNDg4 | 25,949 | Fix type annotation in pandas. version | {
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} | 6 | 2019-04-01T18:27:47Z | 2019-04-04T13:11:13Z | 2019-04-04T12:56:29Z | CONTRIBUTOR | null | Also remove this module from `mypy.ini`
- [ ] closes #25948
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-04-01T18:36:16Z | 2019-04-26T00:53:27Z | 2019-04-26T00:53:27Z | CONTRIBUTOR | null | Part of #25882
Current errors are as follows:
> mypy pandas/compat/numpy/function.py
pandas\compat\numpy\function.py:104: error: Need type annotation for 'ARGSORT_DEFAULTS'
pandas\compat\numpy\function.py:105: error: Cannot determine type of 'ARGSORT_DEFAULTS'
pandas\compat\numpy\function.py:106: error: Cannot determine type of 'ARGSORT_DEFAULTS'
pandas\compat\numpy\function.py:107: error: Cannot determine type of 'ARGSORT_DEFAULTS'
pandas\compat\numpy\function.py:108: error: Cannot determine type of 'ARGSORT_DEFAULTS'
pandas\compat\numpy\function.py:113: error: Need type annotation for 'ARGSORT_DEFAULTS_KIND'
pandas\compat\numpy\function.py:114: error: Cannot determine type of 'ARGSORT_DEFAULTS_KIND'
pandas\compat\numpy\function.py:115: error: Cannot determine type of 'ARGSORT_DEFAULTS_KIND'
pandas\compat\numpy\function.py:116: error: Cannot determine type of 'ARGSORT_DEFAULTS_KIND'
pandas\compat\numpy\function.py:158: error: Need type annotation for 'COMPRESS_DEFAULTS'
pandas\compat\numpy\function.py:159: error: Cannot determine type of 'COMPRESS_DEFAULTS'
pandas\compat\numpy\function.py:160: error: Cannot determine type of 'COMPRESS_DEFAULTS'
pandas\compat\numpy\function.py:161: error: Cannot determine type of 'COMPRESS_DEFAULTS'
pandas\compat\numpy\function.py:164: error: Need type annotation for 'CUM_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:165: error: Cannot determine type of 'CUM_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:166: error: Cannot determine type of 'CUM_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:167: error: Cannot determine type of 'CUM_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:169: error: Cannot determine type of 'CUM_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:188: error: Need type annotation for 'ALLANY_DEFAULTS'
pandas\compat\numpy\function.py:189: error: Cannot determine type of 'ALLANY_DEFAULTS'
pandas\compat\numpy\function.py:190: error: Cannot determine type of 'ALLANY_DEFAULTS'
pandas\compat\numpy\function.py:191: error: Cannot determine type of 'ALLANY_DEFAULTS'
pandas\compat\numpy\function.py:192: error: Cannot determine type of 'ALLANY_DEFAULTS'
pandas\compat\numpy\function.py:194: error: Cannot determine type of 'ALLANY_DEFAULTS'
pandas\compat\numpy\function.py:218: error: Need type annotation for 'SORT_DEFAULTS'
pandas\compat\numpy\function.py:219: error: Cannot determine type of 'SORT_DEFAULTS'
pandas\compat\numpy\function.py:220: error: Cannot determine type of 'SORT_DEFAULTS'
pandas\compat\numpy\function.py:221: error: Cannot determine type of 'SORT_DEFAULTS'
pandas\compat\numpy\function.py:222: error: Cannot determine type of 'SORT_DEFAULTS'
pandas\compat\numpy\function.py:225: error: Need type annotation for 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:226: error: Cannot determine type of 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:227: error: Cannot determine type of 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:229: error: Cannot determine type of 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:233: error: Cannot determine type of 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:237: error: Cannot determine type of 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:239: error: Cannot determine type of 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:245: error: Cannot determine type of 'STAT_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:250: error: Need type annotation for 'STAT_DDOF_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:251: error: Cannot determine type of 'STAT_DDOF_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:252: error: Cannot determine type of 'STAT_DDOF_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:253: error: Cannot determine type of 'STAT_DDOF_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:254: error: Cannot determine type of 'STAT_DDOF_FUNC_DEFAULTS'
pandas\compat\numpy\function.py:257: error: Need type annotation for 'TAKE_DEFAULTS'
pandas\compat\numpy\function.py:258: error: Cannot determine type of 'TAKE_DEFAULTS'
pandas\compat\numpy\function.py:259: error: Cannot determine type of 'TAKE_DEFAULTS'
pandas\compat\numpy\function.py:260: error: Cannot determine type of 'TAKE_DEFAULTS'
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] | closed | false | null | [] | null | 13 | 2019-04-01T23:39:28Z | 2019-04-02T21:35:25Z | 2019-04-02T16:25:51Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
n = 10001
df = pd.DataFrame({'a' : [0] * n,
'b' : [1] * n})
df['c'] = df.a + df.b
print(type(df.a.values[0]))
print(type(df.b.values[0]))
print(type(df.c.values[0]))
print(isinstance(df.a.values[0], np.int64))
print(isinstance(df.b.values[0], np.int64))
print(isinstance(df.c.values[0], np.int64))
```
#### Problem description
The last print statement displays "False, " but it should be "True." You get the correct output as long as n in the first line is set to a number <= 10000, but the wrong output if n > 10000
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
<class 'numpy.int64'>
<class 'numpy.int64'>
<class 'numpy.int64'>
True
True
True
#### 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: 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: 0.24.2
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-04-02T01:12:49Z | 2019-04-28T20:57:24Z | 2019-04-28T20:57:24Z | CONTRIBUTOR | null | #25743 caused a performance regression with Series operations (mean, sum, etc). This can be avoided by moving some of the `if`s around so that the additional check is not performed for those operations. | {
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} | 10 | 2019-04-02T01:22:45Z | 2019-04-28T20:57:27Z | 2019-04-28T20:57:24Z | CONTRIBUTOR | null | - [x] closes #25952
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
#25743 introduced a performance regression in `Series` `MultiIndex` statistical operations.
This happened due to the additional required check for Series groupby operations that already existed for `DataFrame`. By ensuring that the check needs to be run, this regression can be avoided also potentially slightly speeding up similar `DataFrame` operations. | {
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} | 7 | 2019-04-02T01:26:18Z | 2019-04-03T20:27:06Z | 2019-04-03T20:25:35Z | MEMBER | null | - [x] xref #25725
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Imports `StringIO` and `BytesIO` from `io`. Removes `cStringIO` | {
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"description": "read_html, to_html, Styl... | closed | false | null | [] | null | 1 | 2019-04-02T01:47:00Z | 2019-09-16T20:23:24Z | 2019-09-16T20:23:24Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pandas.util.testing as tm
df = tm.makeDataFrame()[:5]
fmt = lambda x: str(x)+'_mod'
formatters=[fmt,fmt,None,None]
print(df.to_html(formatters=formatters))
print(df.to_html(formatters=formatters, max_cols=3))
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>B</th>
<th>C</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>auVRHDVXKo</th>
<td>0.8480611318869997_mod</td>
<td>2.4785562034246165_mod</td>
<td>-1.258639</td>
<td>1.134326</td>
</tr>
<tr>
<th>4IL8N5I4tk</th>
<td>-0.4349431395826644_mod</td>
<td>1.491615558543841_mod</td>
<td>-1.392170</td>
<td>-1.119832</td>
</tr>
<tr>
<th>PACjyGDGOG</th>
<td>-0.8963777799744853_mod</td>
<td>0.5011278874888623_mod</td>
<td>-0.475899</td>
<td>1.041362</td>
</tr>
<tr>
<th>dGFzef3Sbf</th>
<td>0.2615536705949168_mod</td>
<td>1.4994356591304039_mod</td>
<td>0.713050</td>
<td>-0.613034</td>
</tr>
<tr>
<th>U2mGzTnuUi</th>
<td>-0.8803411110313392_mod</td>
<td>-0.5522186462569404_mod</td>
<td>0.309005</td>
<td>-1.715071</td>
</tr>
</tbody>
</table>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>...</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>auVRHDVXKo</th>
<td>0.8480611318869997_mod</td>
<td>...</td>
<td>1.134326092661258_mod</td>
</tr>
<tr>
<th>4IL8N5I4tk</th>
<td>-0.4349431395826644_mod</td>
<td>...</td>
<td>-1.1198318989339953_mod</td>
</tr>
<tr>
<th>PACjyGDGOG</th>
<td>-0.8963777799744853_mod</td>
<td>...</td>
<td>1.0413616487242832_mod</td>
</tr>
<tr>
<th>dGFzef3Sbf</th>
<td>0.2615536705949168_mod</td>
<td>...</td>
<td>-0.6130344240242487_mod</td>
</tr>
<tr>
<th>U2mGzTnuUi</th>
<td>-0.8803411110313392_mod</td>
<td>...</td>
<td>-1.7150707649504604_mod</td>
</tr>
</tbody>
</table>
#### Problem description
When a list of formatters is given, the integer position of the truncated dataframe is used to lookup the formatter.
the same problem also occurs for `to_string()`
#### Expected Output
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>A</th>
<th>...</th>
<th>D</th>
</tr>
</thead>
<tbody>
<tr>
<th>auVRHDVXKo</th>
<td>0.8480611318869997_mod</td>
<td>...</td>
<td>1.134326</td>
</tr>
<tr>
<th>4IL8N5I4tk</th>
<td>-0.4349431395826644_mod</td>
<td>...</td>
<td>-1.119832</td>
</tr>
<tr>
<th>PACjyGDGOG</th>
<td>-0.8963777799744853_mod</td>
<td>...</td>
<td>1.041362</td>
</tr>
<tr>
<th>dGFzef3Sbf</th>
<td>0.2615536705949168_mod</td>
<td>...</td>
<td>-0.613034</td>
</tr>
<tr>
<th>U2mGzTnuUi</th>
<td>-0.8803411110313392_mod</td>
<td>...</td>
<td>-1.715071</td>
</tr>
</tbody>
</table>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.25.0.dev0+348.gb727c6b32
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.6.3
Cython: 0.29.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Remove `cPickle`. Removes some `cPickle` tests. | {
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} | 5 | 2019-04-02T04:37:13Z | 2019-04-02T23:41:26Z | 2019-04-02T15:03:13Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Removes the following from `pandas.compat` in favor of direct imports from `collections.abc`:
- `Hashable`
- `Iterable`
- `Iterator`
- `Mapping`
- `MutableMapping`
- `Sequence`
- `Sized`
- `Set` | {
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] | closed | false | null | [] | null | 6 | 2019-04-02T04:47:14Z | 2020-01-16T00:34:13Z | 2019-04-02T12:34:21Z | MEMBER | null | Progress towards #25632
Prefacing this PR with the statement that this is pretty wonky, but I would consider that largely an artifact of cleaning up the existing code base.
Right now `transpose` is defined in `NDFrame` but is only used by `DataFrame` and `Panel`, as `Series` objects end up using a separate `transpose` defined in `IndexOpsMixin`.
There are duplicate calls to `validate_transpose*` with various signatures that aren't well defined, and everything is passed rather ambiguously via args and kwargs which makes validation rather difficult.
Existing validation is arguably rough. Here's a sample call and response on master:
```python-traceback
>>> df = pd.DataFrame(np.ones((2, 2)))
>>> df.transpose('foo')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/core/frame.py", line 2643, in transpose
nv.validate_transpose(args, dict())
File "/Users/williamayd/clones/pandas/pandas/compat/numpy/function.py", line 55, in __call__
self.defaults)
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 218, in validate_args_and_kwargs
validate_kwargs(fname, kwargs, compat_args)
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 157, in validate_kwargs
_check_for_default_values(fname, kwds, compat_args)
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 69, in _check_for_default_values
format(fname=fname, arg=key)))
ValueError: the 'axes' parameter is not supported in the pandas implementation of transpose()
```
As you can see the error message mentions the 'axes' parameter, but that isn't part of the signature of `transpose` and is instead ambiguously defined based off of args.
What I've done here is removed the Panel implementation and tried to make the `NDFrame` implementation more explicit. Because it's only used by `DataFrame` however, an alternate and arguably preferable implementation would be to raise `NotImplementedError` in `NDFrame` and handle everything in the `DataFrame` implementation once Panel is gone | {
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} | 4 | 2019-04-02T06:44:00Z | 2019-05-19T17:09:40Z | 2019-05-19T17:09:40Z | NONE | null | To reproduce the problem:
```python
import pandas as pd
s = pd.util.testing.makeTimeSeries()
s = s.tz_localize('UTC')
pd.Series(s.index).apply(lambda x: pd.Series([1,2]))
AttributeError: 'PandasArray' object has no attribute 'tolist'
```
It works fine if the function to apply returns a scalar instead of a Series:
```python
import pandas as pd
s = pd.util.testing.makeTimeSeries()
s = s.tz_localize('UTC')
pd.Series(s.index).apply(lambda x: 1)
```
or it there is no timezone information in the series.
```python
import pandas as pd
s = pd.util.testing.makeTimeSeries()
pd.Series(s.index).apply(lambda x: pd.Series([1,2]))
```
The issue is present with pandas 0.24.2, it used to work fine with 0.23.4. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25960 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25960/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25960/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25960/events | https://github.com/pandas-dev/pandas/issues/25960 | 428,240,472 | MDU6SXNzdWU0MjgyNDA0NzI= | 25,960 | UnicodeDecodeError for Stata file | {
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} | 20 | 2019-04-02T13:51:55Z | 2019-04-04T12:51:42Z | 2019-04-04T12:51:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.read_stata('mwe.dta')
```
`mwe.dta` available here: [mwe.zip](https://github.com/pandas-dev/pandas/files/3034141/mwe.zip)
This file is a derivative of [The Supreme Court Database](http://scdb.wustl.edu/_brickFiles/2018_02/SCDB_2018_02_caseCentered_Citation.dta.zip)
#### Problem description
The command raises
```python
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa4 in position 20: invalid start byte
```
I traced the error to a value label containing that byte.
This is a follow-up for #21244 and #23736
Changing line **1334** of `pandas.io.stata` from
```python
return s.decode('utf-8')
```
to
```python
return s.decode('latin-1')
```
allows me to read in the file.
#### Expected Output
The file should be correctly read and parsed.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.6.2
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
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: 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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} | 29 | 2019-04-02T14:26:13Z | 2019-04-26T09:45:44Z | 2019-04-26T00:53:27Z | CONTRIBUTOR | null | - [x] closes #25950
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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... | open | false | null | [] | null | 0 | 2019-04-02T20:20:02Z | 2020-05-16T20:37:18Z | null | NONE | null | I have a HDFStore that is being generated by pandas0.16.3 and is attempting to be read by current pandas. it works in 0.23.4 but in 0.24. it says no module named tslib. what causes this?
for 0.24.2,
I get this error:
/anaconda/4.5.12/lib/python2.7/site-packages/tables/vlarray.pyc in read(self, start, stop, step)
823 atom = self.atom
824 if not hasattr(atom, 'size'): # it is a pseudo-atom
--> 825 outlistarr = [atom.fromarray(arr) for arr in listarr]
826 else:
827 # Convert the list to the right flavor
/anaconda/4.5.12/lib/python2.7/site-packages/tables/atom.pyc in fromarray(self, array)
1225 if array.size == 0:
1226 return None
-> 1227 return six.moves.cPickle.loads(array.tostring())
ImportError: No module named tslib
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} | 22 | 2019-04-02T20:47:15Z | 2019-05-19T17:09:44Z | 2019-05-19T17:09:40Z | CONTRIBUTOR | null | - [x] closes #25959
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
In Series.apply we call .array on returning the result of DatetimeLikeArrayMixin map, which causes an error.
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https://api.github.com/repos/pandas-dev/pandas/issues/25965 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25965/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25965/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25965/events | https://github.com/pandas-dev/pandas/issues/25965 | 428,439,975 | MDU6SXNzdWU0Mjg0Mzk5NzU= | 25,965 | drop_duplicates throws error when series stores numpy array | {
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] | closed | false | null | [] | null | 3 | 2019-04-02T20:56:00Z | 2019-04-10T18:22:06Z | 2019-04-09T20:08:50Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A' : []})
df.loc[0] = [np.array(['hello'])]
df.A.drop_duplicates()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
TypeError: unhashable type: 'numpy.ndarray'
During handling of the above exception, another exception occurred:
SystemError Traceback (most recent call last)
<ipython-input-7-311de327a529> in <module>
----> 1 df.A.drop_duplicates()
/usr/local/lib/python3.5/dist-packages/pandas/core/series.py in drop_duplicates(self, keep, inplace)
1739 Name: animal, dtype: object
1740 """
-> 1741 return super(Series, self).drop_duplicates(keep=keep, inplace=inplace)
1742
1743 def duplicated(self, keep='first'):
/usr/local/lib/python3.5/dist-packages/pandas/core/base.py in drop_duplicates(self, keep, inplace)
1507 return self._shallow_copy()
1508
-> 1509 duplicated = self.duplicated(keep=keep)
1510 result = self[np.logical_not(duplicated)]
1511 if inplace:
/usr/local/lib/python3.5/dist-packages/pandas/core/series.py in duplicated(self, keep)
1813 dtype: bool
1814 """
-> 1815 return super(Series, self).duplicated(keep=keep)
1816
1817 def idxmin(self, axis=0, skipna=True, *args, **kwargs):
/usr/local/lib/python3.5/dist-packages/pandas/core/base.py in duplicated(self, keep)
1521 return duplicated(self, keep=keep)
1522 else:
-> 1523 return self._constructor(duplicated(self, keep=keep),
1524 index=self.index).__finalize__(self)
1525
/usr/local/lib/python3.5/dist-packages/pandas/core/algorithms.py in duplicated(values, keep)
785 values, dtype, ndtype = _ensure_data(values)
786 f = getattr(htable, "duplicated_{dtype}".format(dtype=ndtype))
--> 787 return f(values, keep=keep)
788
789
SystemError: <built-in function duplicated_object> returned a result with an error set
```
#### Problem description
If I add another row with the same value drop_duplicates doesn't throw an exception but fails to remove the duplicate. If I add a third row with the same value it removes only one of the three duplicates.
```python
df.loc[1] = [np.array(['hello'])]
df.A.drop_duplicates()
0 [hello]
1 [hello]
Name: A, dtype: object
df.loc[2] = [np.array(['hello'])]
df.A.drop_duplicates()
0 [hello]
1 [hello]
Name: A, dtype: object
```
#### Expected Output
```python
0 [hello]
Name: A, dtype: object
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.4.2
pip: 19.0.3
setuptools: 38.5.1
Cython: None
numpy: 1.16.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: None
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml.etree: 4.1.1
bs4: 4.4.1
html5lib: 1.0b8
sqlalchemy: 1.2.4
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25966 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25966/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25966/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25966/events | https://github.com/pandas-dev/pandas/issues/25966 | 428,472,642 | MDU6SXNzdWU0Mjg0NzI2NDI= | 25,966 | str.contain does not parse "?" | {
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} | 1 | 2019-04-02T22:24:19Z | 2019-04-03T00:30:11Z | 2019-04-03T00:29:01Z | NONE | null | ```python
# Your code here
df[df.name.str.contains('?')]
Traceback (most recent call last)
df[df.name.str.contains('?')]
~/.pyenv/versions/3.6.1/envs/data_disambiguation/lib/python3.6/site-packages/pandas/core/strings.py in contains(self, pat, case, flags, na, regex)
2521 def contains(self, pat, case=True, flags=0, na=np.nan, regex=True):
2522 result = str_contains(self._parent, pat, case=case, flags=flags, na=na,
2523 regex=regex)
2524 return self._wrap_result(result, fill_value=na)
2525
~/.pyenv/versions/3.6.1/envs/data_disambiguation/lib/python3.6/site-packages/pandas/core/strings.py in str_contains(arr, pat, case, flags, na, regex)
297 flags |= re.IGNORECASE
298
299 regex = re.compile(pat, flags=flags)
300
301 if regex.groups > 0:
~/.pyenv/versions/3.6.1/lib/python3.6/re.py in compile(pattern, flags)
231 def compile(pattern, flags=0):
232 "Compile a regular expression pattern, returning a pattern object."
233 return _compile(pattern, flags)
234
235 def purge():
~/.pyenv/versions/3.6.1/lib/python3.6/re.py in _compile(pattern, flags)
299 if not sre_compile.isstring(pattern):
300 raise TypeError("first argument must be string or compiled pattern")
301 p = sre_compile.compile(pattern, flags)
302 if not (flags & DEBUG):
303 if len(_cache) >= _MAXCACHE:
~/.pyenv/versions/3.6.1/lib/python3.6/sre_compile.py in compile(p, flags)
560 if isstring(p):
561 pattern = p
562 p = sre_parse.parse(p, flags)
563 else:
564 pattern = None
~/.pyenv/versions/3.6.1/lib/python3.6/sre_parse.py in parse(str, flags, pattern)
854
855 try:
856 p = _parse_sub(source, pattern, flags & SRE_FLAG_VERBOSE, False)
857 except Verbose:
858 # the VERBOSE flag was switched on inside the pattern. to be
~/.pyenv/versions/3.6.1/lib/python3.6/sre_parse.py in _parse_sub(source, state, verbose, nested)
413 start = source.tell()
414 while True:
415 itemsappend(_parse(source, state, verbose))
416 if not sourcematch("|"):
417 break
~/.pyenv/versions/3.6.1/lib/python3.6/sre_parse.py in _parse(source, state, verbose)
613 if not item or (_len(item) == 1 and item[0][0] is AT):
614 raise source.error("nothing to repeat",
615 source.tell() - here + len(this))
616 if item[0][0] in _REPEATCODES:
617 raise source.error("multiple repeat",
error: nothing to repeat at position 0
```
#### Problem description
The function should return a pandas.DataFrame which has `?` in the column `name`.
This function works well with other punctuations such as `!`, `&` etc.
#### Expected Output
name | input_domain
-- | --
somethingwith? | something.com/
?Iamhere | https://www.url.com
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} | 5 | 2019-04-02T22:51:31Z | 2019-12-19T22:29:39Z | 2019-04-04T12:51:42Z | CONTRIBUTOR | null | Add a fall back decode path that allows improperly formatted Stata
files written in 118 format but using latin-1 encoded strings to be
read
closes #25960
- [x] closes #25960
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 7 | 2019-04-02T23:03:34Z | 2019-12-19T22:29:38Z | 2019-04-05T00:04:30Z | CONTRIBUTOR | null | Improve the explanation when value labels are repeated in Stata dta files.
Add suggested methods to work around the issue using the low level
interface.
closes #25772
- [x] closes #25772
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2019-04-03T02:44:32Z | 2021-06-11T13:27:05Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
s1 = pd.Series({"a": np.int64(64), "b": 10})
for v in s1.to_dict().values():
print(type(v)) # prints <class 'int'> 2x
s2 = pd.Series({"a": np.int64(64), "b": 10, "c": "ABC"})
for v in s2.to_dict().values():
print(type(v)) # prints <class 'numpy.int64'> for first variable "a"
for k, v in s1.items():
print(k, type(v)) # prints <class 'int'> 2x
for k, v in s2.items():
print(k, type(v)) # prints <class 'numpy.int64'> again for the first variable "a"
```
#### Problem description
`pd.Series.to_dict` can return different types for objects depending on the composition of the series. This also affects iteration, e.g., `for k, v in series: ...`. This is inconsistent and, critically, leads to really weird and hard to debug issues downstream with types, especially around JSON conversion (the built-in `json` module and many others will blow up when it encounters numpy dtypes).
I cannot find this exact issue open in the issue tracker, though there are a number of related issues including:
* An issue related to `DataFrame.to_dict` and inconsistent types (closed in `0.24`): https://github.com/pandas-dev/pandas/issues/24908
* This issue also related to scalar coercion on `DataFrame.to_dict` calls (also closed recently): https://github.com/pandas-dev/pandas/issues/23753
* This PR fixes the issue deriving from iteration, but it looks like the above case is either an untested edge case or a regression: https://github.com/pandas-dev/pandas/pull/17491
#### Expected Output
Expected output is for type coercion to Python ints to occur regardless of the exact column composition in the Series. https://github.com/pandas-dev/pandas/issues/24908 is a related issue for `DataFrame` coercions with irregular behavior happening as a result.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
```
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
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: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.4.0
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: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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} | 5 | 2019-04-03T08:44:43Z | 2020-05-13T02:49:45Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
#### 1st example:
Input:
```python
series = pd.Series([1, 1, 2, 0, 1, np.nan, 4, 4, np.nan, 3])
series.value_counts(True, bins=2)
```
Output:
```python
(-0.005, 2.0] 0.5
(2.0, 4.0] 0.3
dtype: float64
```
Expected output:
```python
(-0.005, 2.0] 0.625
(2.0, 4.0] 0.375
dtype: float64
```
#### 2nd example:
Input:
```python
series = pd.Series([1, 1, 2, 0, 1, np.nan, 4, 4, np.nan, 3])
series.value_counts(True, bins=2, dropna=False)
```
Output:
```python
(-0.005, 2.0] 0.5
(2.0, 4.0] 0.3
dtype: float64
```
Expected Output:
```python
(-0.004, 2.0] 0.5
(2.0, 4.0] 0.3
NaN 0.2
dtype: float64
```
#### Problem description
dropna argument in value_counts() seems to have no effect when bins is not None. Expected behaviour is better, because it sums up shares up to 1 when dropna=True and shows NaNs when dropna=False.
#### 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 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: 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: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25971 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25971/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25971/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25971/events | https://github.com/pandas-dev/pandas/issues/25971 | 428,653,248 | MDU6SXNzdWU0Mjg2NTMyNDg= | 25,971 | Groupby on single key should be accessible by tuple of length 1 | {
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... | open | false | null | [] | null | 2 | 2019-04-03T09:12:44Z | 2021-07-02T05:13:02Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]})
def select_by_dict(dataframe, d):
return dataframe.groupby(list(d.keys())).get_group(tuple(d.values()))
select_by_dict(df, {'col1': 1, 'col2': 3})
select_by_dict(df, {'col1': 1}) # this one fails
```
#### Problem description
I would like to use a dictionary of arbitrary length to select certain groups from a DataFrame, but if I group by a single key, it won't accept a tuple of length 1 as the key value.
#### Expected Output
I expect the second call to the function to return a DataFrame containing those values for which col1 == 1. (I.e. as df[df['col1'] == 1]). Perhaps my approach is ill-advised, but I have come across this problem before in situations where it was more obvious to use groupby (in the current case it might well be better to use boolean indexing).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.3
pytest: 3.3.1
pip: 18.1
setuptools: 36.6.0
Cython: 0.29.5
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.4.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.0.2
openpyxl: 2.5.4
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.7.1
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
I hope you find this a useful issue. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25972 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25972/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25972/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25972/events | https://github.com/pandas-dev/pandas/issues/25972 | 428,684,006 | MDU6SXNzdWU0Mjg2ODQwMDY= | 25,972 | Unexpected behaviour of numpy.ufuncs for all NaN DataFrame | {
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} | [] | closed | false | null | [] | null | 3 | 2019-04-03T10:17:39Z | 2019-04-12T10:19:04Z | 2019-04-12T10:19:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
x = pd.DataFrame(index=[0, 1, 2], columns=[0, 1])
np.log(x)
>>> AttributeError: 'float' object has no attribute 'log'
np.sum(df)
>>> 0 0.0
>>> 1 0.0
dtype: float64
y = pd.DataFrame(index=[0, 1, 2], columns=[0, 1, dtype=float])
np.log(y)
>>> 0 1
>>> 0 NaN NaN
>>> 1 NaN NaN
>>> 2 NaN NaN
```
#### Problem description
The AttributeError raised by calling a numpy.ufunc on a DataFrame with object columns containing NaN values is completely uninformative about the problem.
The error suggest that the namespace `np` is assigned to a float during execution of that numpy.ufunc. This shouldn't happen in any case.
The error can be observed for ufuncs: `exp`, `log`, `sin`, `cos`, etc.
It is also confusing that the ufunc `sum` can be applied to `x` resulting in the expected output for a DataFrame of dtype=float.
The second example, where the dtype is explicitly set to float, passes as expected.
#### Expected Output
An appropriate exception stating that the numpy.ufunc cannot be applied to a frame with (all) object columns.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.23.4
pytest: None
pip: 10.0.1
setuptools: 39.2.0
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
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/25973 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25973/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25973/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25973/events | https://github.com/pandas-dev/pandas/issues/25973 | 428,736,798 | MDU6SXNzdWU0Mjg3MzY3OTg= | 25,973 | [featureRequest] pd.DataFrame.iterdicts() | {
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} | 8 | 2019-04-03T12:23:11Z | 2021-10-29T02:08:07Z | 2021-10-29T02:08:07Z | NONE | null | ### Code
Can we have a method that returns an iterator of dictionary instead of tuples? Something like
```python
def iterdicts(self, **kwargs):
it = (x.__dict__ for x in self.itertuples())
return it
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-143-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: 3.2.3
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.25.2
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.7.0
sphinx: 1.7.4
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 3.5.0
bs4: None
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
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25974 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25974/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25974/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25974/events | https://github.com/pandas-dev/pandas/issues/25974 | 428,752,671 | MDU6SXNzdWU0Mjg3NTI2NzE= | 25,974 | Add dependency typing_extensions for type hints | {
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"def... | closed | false | null | [] | null | 1 | 2019-04-03T12:57:57Z | 2019-04-21T16:55:16Z | 2019-04-21T16:55:16Z | CONTRIBUTOR | null | [Typing Extensions](https://pypi.org/project/typing-extensions/) – Backported and Experimental Type Hints for Python.
To make use of Type Hints only available in python 3.7 and some other Experimental Type Hints (like `Final`) this package is required. Discussion in #25961 | {
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"def... | closed | false | null | [] | null | 21 | 2019-04-03T13:22:47Z | 2019-04-19T07:23:04Z | 2019-04-17T05:05:26Z | CONTRIBUTOR | null | - [ ] closes #25974
- [ ] 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/25976 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25976/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25976/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25976/events | https://github.com/pandas-dev/pandas/pull/25976 | 428,777,474 | MDExOlB1bGxSZXF1ZXN0MjY3MDU0OTcw | 25,976 | Change fname to path in to_parquet | {
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} | [] | closed | false | null | [] | null | 4 | 2019-04-03T13:45:59Z | 2019-04-04T11:08:23Z | 2019-04-04T01:33:15Z | CONTRIBUTOR | null |
- [X] closes #23574
- [N/A ] tests added / passed
- [X ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ N/A?] whatsnew entry
https://github.com/pandas-dev/pandas/issues/23574 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25977 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25977/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25977/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25977/events | https://github.com/pandas-dev/pandas/pull/25977 | 428,878,950 | MDExOlB1bGxSZXF1ZXN0MjY3MTM1Njgz | 25,977 | REF: handling of max_colwidth parameter | {
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] | closed | false | null | [] | null | 6 | 2019-04-03T17:07:25Z | 2019-08-28T16:45:16Z | 2019-08-28T16:45:15Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
this would be a precursor to fixes for #7059, #9784 and #6491.
getting the display.max_colwidth value is moved from `_make_fixed_width` to `format_array` to be consistent with the handling of the `column_space`, `float_format` and `precision` display options.
the PR also includes refactoring of the prior fix to `to_html` #24841
`_format_col` is moved from `DataFrameFormatter` to `TableFormatter` to make it available to `HTMLFormatter` and `NotebookFormatter`.
at the moment `_make_fixed_width` is called by `format_array`, but also by `_to_str_columns` and `_get_formatted_index`.
`_to_str_columns` is not an issue because the calls are preceded to calls to `format_array`
IMO `_get_formatted_index` needs to be refactored to call `format_array` directly and probably some additional tests as a precursor to this PR.
thoughts?
cc @TomAugspurger | {
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} | 3 | 2019-04-03T19:20:52Z | 2019-04-04T20:13:51Z | 2019-04-04T20:13:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
s = pd.Series([
'nan',
pd.Timestamp("1990-01-01"),
"2015-03-14T16:15:14.123-08:00",
"2019-03-04T21:56:32.620-07:00", # Note different TimeZone
None,
])
print(s)
#0 nan
#1 1990-01-01 00:00:00
#2 2015-03-14T16:15:14.123-08:00
#3 2019-03-04T21:56:32.620-07:00
#4 None
#dtype: object
pd.to_datetime(s)
#Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/tools/datetimes.py", line 592, in to_datetime
# values = convert_listlike(arg._values, True, format)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/tools/datetimes.py", line 302, in _convert_listlike_datetimes
# allow_object=True)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py", line 1857, in objects_to_datetime64ns
# require_iso8601=require_iso8601
# File "pandas/_libs/tslib.pyx", line 460, in pandas._libs.tslib.array_to_datetime
# File "pandas/_libs/tslib.pyx", line 712, in pandas._libs.tslib.array_to_datetime
# File "pandas/_libs/tslib.pyx", line 813, in pandas._libs.tslib.array_to_datetime_object
#RuntimeError: No active exception to reraise
```
#### Problem description
Throwing `RuntimeError` does not give the user any feedback on how to fix things. The correct way to fix this is to add `utc=True`:
```python
pd.to_datetime(s, utc=True)
#0 NaT
#1 1990-01-01 00:00:00+00:00
#2 2015-03-15 00:15:14.123000+00:00
#3 2019-03-05 04:56:32.620000+00:00
#4 NaT
#dtype: datetime64[ns, UTC]
```
#### Expected Output
The error message should be more descriptive, perhaps something like:
```
ValueError: array must be all same time zone
```
The above is the same error provided by `pandas._libs.tslibs.conversion.datetime_to_datetime64` and can be seen using this example:
```python
import numpy as np
import pandas as pd
df = pd.DataFrame([['1', "2019-04-03T16:13:27.123-08:00"], ['2', "2019-01-01T13:45:46.424-07:00"]])dd
print(df.types)
#0 object
#1 object
#dtype: object
df[1].astype(np.datetime64)
#Traceback (most recent call last):
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py", line 1861, in objects_to_datetime64ns
# values, tz_parsed = conversion.datetime_to_datetime64(data)
# File "pandas/_libs/tslibs/conversion.pyx", line 185, in pandas._libs.tslibs.conversion.datetime_to_datetime64
#ValueError: Array must be all same time zone
#
#During handling of the above exception, another exception occurred:
#
#Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/generic.py", line 5691, in astype
# **kwargs)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 531, in astype
# return self.apply('astype', dtype=dtype, **kwargs)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 395, in apply
# applied = getattr(b, f)(**kwargs)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 534, in astype
# **kwargs)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 633, in _astype
# values = astype_nansafe(values.ravel(), dtype, copy=True)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/dtypes/cast.py", line 690, in astype_nansafe
# return astype_nansafe(to_datetime(arr).values, dtype, copy=copy)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/dtypes/cast.py", line 690, in astype_nansafe
# return astype_nansafe(to_datetime(arr).values, dtype, copy=copy)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/tools/datetimes.py", line 609, in to_datetime
# result = convert_listlike(arg, box, format)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/tools/datetimes.py", line 302, in _convert_listlike_datetimes
# allow_object=True)
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py", line 1866, in objects_to_datetime64ns
# raise e
# File "/c/gitlab/tph-admin/gitlab-activity-monitor/.venv-gam/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py", line 1857, in objects_to_datetime64ns
# require_iso8601=require_iso8601
# File "pandas/_libs/tslib.pyx", line 460, in pandas._libs.tslib.array_to_datetime
# File "pandas/_libs/tslib.pyx", line 537, in pandas._libs.tslib.array_to_datetime
#ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
#### Output of ``pd.show_versions()``
<details>
```
>>> import pandas as pd
>>> pd.show_versions()
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: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.9.0
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: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25979 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25979/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25979/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25979/events | https://github.com/pandas-dev/pandas/issues/25979 | 428,955,654 | MDU6SXNzdWU0Mjg5NTU2NTQ= | 25,979 | pip install error on arm7l (beaglebone) | {
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] | closed | false | null | [] | null | 2 | 2019-04-03T20:18:05Z | 2019-04-03T22:46:33Z | 2019-04-03T20:21:24Z | NONE | null | Hi,
Pandas install fails on a beaglebone (I know, I know, use miniconda, but I would like to not do that). It dies with some kind of utter gcc failure building pandas/_libs/join.c which... does not exist.
$ sudo pip install pandas
[see pastebin for full log](https://pastebin.com/Pb331dLu)
building 'pandas._libs.join' extension
arm-linux-gnueabihf-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fdebug-prefix-map=/build/python3.5-QMv3zq/python3.5-3.5.3=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/_libs/src/klib -Ipandas/_libs/src -I/usr/local/lib/python3.5/dist-packages/numpy/core/include -I/usr/include/python3.5m -c pandas/_libs/join.c -o build/temp.linux-armv7l-3.5/pandas/_libs/join.o -Wno-unused-function
In file included from /usr/local/lib/python3.5/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0,
from /usr/local/lib/python3.5/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,
from /usr/local/lib/python3.5/dist-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/_libs/join.c:619:
/usr/local/lib/python3.5/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it with " \
^~~~~~~
arm-linux-gnueabihf-gcc: internal compiler error: Killed (program cc1)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-6/README.Bugs> for instructions.
error: command 'arm-linux-gnueabihf-gcc' failed with exit status 4
----------------------------------------
Command "/usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-install-y1n9_kq7/pandas/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-record-j3wf4k_9/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-install-y1n9_kq7/pandas/
For reference:
$ python3 --version
Python 3.5.3
$ pip freeze
Cython==0.29.6
dateutils==0.6.6
future==0.17.1
iso8601==0.1.12
numpy==1.16.2
python-dateutil==2.8.0
pytz==2018.9
PyYAML==5.1
serial==0.0.97
six==1.12.0
I'm going to try to find the pip flag that doesn't delete its cache or partial installation so I can see a little more. Presumably this is something bad with gcc, maybe running out of memory, I haven't gotten further. This install/compile takes a long time.
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] | closed | false | null | [] | null | 3 | 2019-04-03T21:39:47Z | 2019-08-28T16:17:52Z | 2019-08-28T16:17:52Z | CONTRIBUTOR | null | - [ ] progress towards #24471
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a WIP pull request because I wanted to make sure I'm going in the right direction before continuing.
In #24959 it was agreed that the default for some of the set operations should be ``sort=None`` for backwards compatibility in 0.24.1. ``sort=None`` generally tries to sort in all cases _except_ when the two indices are equal or one of the indices is empty. This meant that in a future release we could introduce a proper ``sort=True`` which would try to sort in all cases and change the default to ``sort=False`` in all cases.
Are there any potential problems with this plan?
In this PR I have started to deprecate ``sort=None`` for ``Index.union`` and to introduce ``sort=True``. ``sort=None`` is currently the default for this set operation so a ``FutureWarning`` will be given by default. I will make similar changes to the ``union`` method in all the index classes if this is the approach we want to take. | {
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} | 3 | 2019-04-03T21:44:37Z | 2019-04-04T01:41:45Z | 2019-04-04T01:31:03Z | MEMBER | null | - [x] xref #25725
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Also
- Removed `pandas.compat.itertools.product` for `itertools.product`
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} | 4 | 2019-04-03T23:04:29Z | 2019-04-04T20:19:52Z | 2019-04-04T20:13:51Z | MEMBER | null | - [x] closes #25978
- [x] tests added / passed
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} | 10 | 2019-04-03T23:12:36Z | 2019-04-05T10:52:43Z | 2019-04-05T02:26:57Z | MEMBER | null | - [x] closes #13021
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
can't shortcut completely without changing existing tested behaviour in a couple of tests.
for instance, the expected output for `test_to_string_with_formatters` in `pandas\tests\io\formats\test_format.py` has two spaces between float and object column, whereas shortcuting would only have one (the same as between the int and float columns)
```
int float object
0 0x1 [ 1.0] -(1, 2)-
1 0x2 [ 2.0] -True-
2 0x3 [ 3.0] -False-
```
if it's OK to change a couple of tests, we can remove this subtle leading_space bug in this PR.
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} | 1 | 2019-04-04T03:43:11Z | 2019-04-05T12:13:04Z | 2019-04-05T02:25:58Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
The index in `Out[3]` is does not display `NaN`:
```python
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '0.25.0.dev0+359.gcaad3b5e5'
In [2]: ii = pd.IntervalIndex.from_tuples([(0, 1), np.nan, (2, 3)])
In [3]: pd.Series(list('abc'), index=ii)
Out[3]:
(0.0, 1.0] a
b
(2.0, 3.0] c
dtype: object
```
Note that this isn't an issue when the `Series` has `Interval` values, or when printing the index itself:
```python
In [4]: pd.Series(ii)
Out[4]:
0 (0.0, 1.0]
1 NaN
2 (2.0, 3.0]
dtype: interval
In [5]: ii
Out[5]:
IntervalIndex([(0.0, 1.0], nan, (2.0, 3.0]],
closed='right',
dtype='interval[float64]')
```
This does not appear to be an issue with other types of index and was working in 0.23.4.
#### Problem description
The output index in `Out[3]` is missing `NaN` for the second entry and is instead blank.
#### Expected Output
I'd expect `NaN` to be displayed in the index.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: caad3b5e566d74d5cf0a018378d71625c30bf145
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.14-041914-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+359.gcaad3b5e5
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: 0.11.1
xarray: 0.11.3
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: 3.0.3
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: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 2 | 2019-04-04T08:48:59Z | 2019-04-24T16:24:07Z | 2019-04-24T16:24:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 6 | 2019-04-04T10:41:53Z | 2019-04-04T20:13:38Z | 2019-04-04T13:27:34Z | NONE | null | #### Code Sample
```python
import pandas as pd
# Where example.csv is:
# column1,column2
# 1, 2
pd.read_csv('example.csv', usecols=['column1', 'column2', ' column3'])
```
#### Problem description
When specifying usecols to reduce the amount of data loaded, read_csv fails if the columns do not exist. This is not always desired, especially when reading a large number of files that may have varying columns.
There should be an option to suppress this and allow usecols to cut-down columns without enforcing their presence.
#### Current Output
`ValueError: Usecols do not match columns in file, columns expected but not found: ['column3']`
#### Expected Output
No error thrown where only some of the usecols exist.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.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: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | [] | closed | false | null | [] | null | 2 | 2019-04-04T11:31:25Z | 2019-04-04T13:19:59Z | 2019-04-04T12:49:34Z | NONE | null | Hi, I have been trying to set-up terabee multiflex sensors and had encountered this error.
Hope to get some help in solving this. Thank you!
** Just some background information, I have been trying to modify from the kobuki_bringup launch file and adapting the use of the multiflex to the application of my robot.
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} | 4 | 2019-04-04T13:30:05Z | 2019-04-04T20:12:52Z | 2019-04-04T20:12:52Z | CONTRIBUTOR | null | Part of #25882
Current errors are as follows:
>mypy pandas/compat/pickle_compat.py
pandas\compat\pickle_compat.py:141: error: Name 'pkl._Unpickler' is not defined | {
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} | 2 | 2019-04-05T01:07:14Z | 2019-04-05T02:26:07Z | 2019-04-05T02:25:19Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas-gbq/issues/271
appears latest google-cloud-bigquery is causing issues. | {
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} | 1 | 2019-04-05T02:32:37Z | 2019-07-16T23:54:41Z | 2019-07-16T23:54:40Z | CONTRIBUTOR | null | some warnings I am seeing; I think these are somewhat recently introduced.
```
/include/python3.7m -c pandas/_libs/src/parser/tokenizer.c -o build/temp.macosx-10.7-x86_64-3.7/pandas/_libs/src/parser/tokenizer.o -Wno-unused-function
pandas/_libs/src/parser/tokenizer.c:302:34: warning: comparison of integers of different signs: 'unsigned long long' and 'int64_t' (aka 'long long') [-Wsign-compare]
if (self->words_len + nbytes < self->max_words_cap) {
~~~~~~~~~~~~~~~~~~~~~~~~ ^ ~~~~~~~~~~~~~~~~~~~
pandas/_libs/src/parser/tokenizer.c:1121:41: warning: comparison of integers of different signs: 'int64_t' (aka 'long long') and 'unsigned long long' [-Wsign-compare]
self->lines == start_lines + line_limit) {
~~~~~~~~~~~ ^ ~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/src/parser/tokenizer.c:1210:15: warning: comparison of integers of different signs: 'size_t' (aka 'unsigned long') and 'int64_t' (aka 'long long') [-Wsign-compare]
if (nrows > self->lines) {
~~~~~ ^ ~~~~~~~~~~~
pandas/_libs/src/parser/tokenizer.c:1247:19: warning: comparison of integers of different signs: 'int64_t' (aka 'long long') and 'unsigned long long' [-Wsign-compare]
for (i = 0; i < self->lines - nrows + 1; ++i) {
~ ^ ~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/src/parser/tokenizer.c:1287:17: warning: comparison of integers of different signs: 'size_t' (aka 'unsigned long') and 'int64_t' (aka 'long long') [-Wsign-compare]
if (new_cap < self->words_cap) {
~~~~~~~ ^ ~~~~~~~~~~~~~~~
pandas/_libs/src/parser/tokenizer.c:1311:17: warning: comparison of integers of different signs: 'size_t' (aka 'unsigned long') and 'int64_t' (aka 'long long') [-Wsign-compare]
if (new_cap < self->stream_cap) {
~~~~~~~ ^ ~~~~~~~~~~~~~~~~
pandas/_libs/src/parser/tokenizer.c:1339:17: warning: comparison of integers of different signs: 'size_t' (aka 'unsigned long') and 'int64_t' (aka 'long long') [-Wsign-compare]
if (new_cap < self->lines_cap) {
~~~~~~~ ^ ~~~~~~~~~~~~~~~
pandas/_libs/src/parser/tokenizer.c:1379:47: warning: comparison of integers of different signs: 'long long' and 'size_t' (aka 'unsigned long') [-Wsign-compare]
if (!all && self->lines - start_lines >= nrows) break;
~~~~~~~~~~~~~~~~~~~~~~~~~ ^ ~~~~~
8 warnings generated.
gcc -bundle -undefined dynamic_lookup -L/Users/jreback/miniconda3/envs/pandas/lib -arch x86_64 -L/Users/jreback/miniconda3/envs/pandas/lib -arch x86_64 -arch x86_64 build/temp.macosx-10.7-x86_64-3.7/pandas/_libs/lib.o build/temp.macosx-10.7-x86_64-3.7/pandas/_libs
```
```
s/_libs/tslibs -I./pandas/_libs -Ipandas/_libs/tslibs/src -Ipandas/_libs/tslibs -I/Users/jreback/miniconda3/envs/pandas/lib/python3.7/site-packages/numpy/core/include -I/Users/jreback/miniconda3/envs/pandas/include/python3.7m -c pandas/_libs/tslib.c -o build/temp.macosx-10.7-x86_64-3.7/pandas/_libs/tslib.o -Wno-unused-function
pandas/_libs/tslib.c:3854:21: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_15 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslib.c:3960:21: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_15 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslib.c:4194:23: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_15 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslib.c:4301:23: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_15 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslib.c:4430:23: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_15 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslib.c:8097:19: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_38 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslib.c:8173:21: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_38 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/tslib.c:8399:21: warning: incompatible pointer types assigning to 'PyObject *' (aka 'struct _object *') from 'struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *' [-Wincompatible-pointer-types]
*__pyx_t_38 = __pyx_v_6pandas_5_libs_6tslibs_7nattype_c_NaT;
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
8 warnings generated.
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"description": "Related ... | open | false | null | [] | null | 4 | 2019-04-05T02:55:28Z | 2021-07-02T05:14:44Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: df['asda']
KeyError: 'asda'
in [2]: df[['asda']]
KeyError: "None of [Index(['asda'], dtype='object')] are in the [columns]"
In [3]: df[['asda', 'lat']] # lat is a valid column
KeyError: "['asda'] not in index"
In [4]: df.loc['asda']
KeyError: 'asda'
In [5]: df.loc['asda']
KeyError: "None of [Index(['asda'], dtype='object')] are in the [index]"
In [6]: df.loc[['asda', 0]]
/home/nedcr/miniconda3/envs/ana/bin/ipython:1: FutureWarning:
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.
See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike
#!/home/nedcr/miniconda3/envs/ana/bin/python
Out[9]:
id lat lng location BL ... location 2030 scale 2030 location 2070 scale 2070
asda NaN NaN NaN NaN ... NaN NaN NaN NaN
0 1009.0 -15.4875 124.5222 38.224253 ... 38.860162 0.947698 40.494363 0.987552
[2 rows x 10 columns]
```
#### Problem description
The last example is kind of irrelevant, as it will be changed soon.
The 2nd and 5th example are annoying, because it is often useful to catch KeyErrors, and use their values to return nice error messages. That's difficult with this output.
Also they are slightly misleading, as I didn't pass and index as the indexer, so it is potentially annoying for debugging.
#### Expected Output
example 2 should have a message either like
```KeyError: ['asda'] not in columns```
or just
```KeyError: ['asda']```
Similarly for example 5.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-16-lowlatency
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: 0.12.0
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
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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] | closed | false | null | [] | null | 1 | 2019-04-05T10:36:14Z | 2019-04-05T10:53:30Z | 2019-04-05T10:53:24Z | MEMBER | null | i'll come back and edit the tittle and add a code sample
due to interaction of #25993 and #25983 | {
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} | 6 | 2019-04-05T11:19:24Z | 2019-04-06T09:25:50Z | 2019-04-05T17:11:52Z | NONE | null | Hello
I use Python 3.7 to report errors when running this module:
```python
import tushare
import pandas as pd
import datetime
#Step 1: Get the ticket code online
Ticket_Raw_Data = tushare.get_stock_basics()
tickets = Ticket_Raw_Data.index.tolist()
#Step 2: save the ticket code
data_today = datetime.datetime.today().strftime("Y%M%D")
File = "F:\\ticket\\data\\ticketa-list CN"+data_today+".csv"
Ticket_Raw_Data.to_csv(File)
print("saved.")
```
Operation error reporting
```
Traceback (most recent call last):
File "F:\ticket\programme\get-ticket-code.py", line 13, in <module>
Ticket_Raw_Data.to_csv(File)
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\frame.py", line 1745, in to_csv
formatter.save()
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\io\formats\csvs.py", line 156, in save
compression=self.compression)
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\io\common.py", line 400, in _get_handle
f = open(path_or_buf, mode, encoding=encoding)
FileNotFoundError: [Errno 2] No such file or directory: 'F:\\ticket\\data\\ticketa-list CNY0704/05/19.csv'
[Finished in 2.1s]
```
How to solve it? Help me. Thank you. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26000 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26000/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26000/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26000/events | https://github.com/pandas-dev/pandas/pull/26000 | 429,715,328 | MDExOlB1bGxSZXF1ZXN0MjY3NzkyMTQz | 26,000 | to_html formatter not called for float values in a mixed-type column (2) | {
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"color": "006b7... | closed | false | null | [] | null | 14 | 2019-04-05T11:46:23Z | 2019-07-02T21:04:27Z | 2019-07-02T21:04:26Z | MEMBER | null | - [x] closes #13021
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
resubmit of #25983 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26001 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26001/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26001/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26001/events | https://github.com/pandas-dev/pandas/issues/26001 | 429,720,882 | MDU6SXNzdWU0Mjk3MjA4ODI= | 26,001 | Question. how to reshape in pandas dataframe? | {
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} | [] | closed | false | null | [] | null | 1 | 2019-04-05T12:01:46Z | 2019-04-05T15:00:23Z | 2019-04-05T15:00:23Z | NONE | null | Dataframe looks like below
I have dataframe like above. which I want to a~t reshape `(a~t, 1)`
I want to reshape dataframe like below ( b~t column is go to under the a column)
```
날짜 역번호 역명 구분 a
2018-01-01 150 서울역 승차 379
2018-01-01 150 서울역 승차 287
2018-01-01 150 서울역 승차 371
2018-01-01 150 서울역 승차 876
2018-01-01 150 서울역 승차 965
....
2008-01-01 152 종각 승차 2920
2008-01-01 152 종각 승차 2290
2008-01-01 152 종각 승차 802
2008-01-01 152 종각 승차 1559
```
like `df = df.reshape(len(data2)*a~t, 1)`
i tried pd.melt but It does not work well.
```
df2 = pd.melt(df, id_vars=["날짜", "역번호", "역명", "구분"], value_name="t")
```
is remove b ~ t but i want insert b~t behind a
dataset is https://drive.google.com/file/d/1Upb5PgymkPB5TXuta_sg6SijwzUuEkfl/view?usp=sharing
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} | 0 | 2019-04-05T13:09:25Z | 2020-05-16T21:11:57Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
code sample adapted from `test_to_string_with_formatters` in `pandas\tests\io\formats\test_format.py`
```python
from pandas import DataFrame
df = DataFrame({'int': [1, 2, 3],
'float': [1.0, 2.0, 3.0],
'object': [(1, 2), True, False]},
columns=['int', 'float', 'object'])
formatters = [('int', lambda x: '0x{x:x}'.format(x=x)),
('float', lambda x: '[{x: 4.1f}]'.format(x=x)),
('object', lambda x: '-{x!s}-'.format(x=x))]
print(df.to_string(formatters=dict(formatters)))
```
```
int float object
0 0x1 [ 1.0] -(1, 2)-
1 0x2 [ 2.0] -True-
2 0x3 [ 3.0] -False-
```
#### Problem description
2 spaces between float and object columns, only 1 between int and float
#### Expected Output
even spacing
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.25.0.dev0+376.gfc5f29237
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.6.3
Cython: 0.29.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-04-05T18:38:49Z | 2019-04-06T04:58:37Z | 2019-04-06T04:58:37Z | CONTRIBUTOR | null | #### Problem description
The docstring for assert_frame_equals examples is not formatted correctly.
The example section of the docstring for `pandas.util.testing.assert_frame_equal` is not formatted correctly and causes them to be displayed wrong. There needs to be some added newlines to separate the code blocks.
```
This example shows comparing two DataFrames that are equal but with columns of differing dtypes.
>>> from pandas.util.testing import assert_frame_equal
>>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
df1 equals itself. >>> assert_frame_equal(df1, df1)
df1 differs from df2 as column ‘b’ is of a different type. >>> assert_frame_equal(df1, df2) Traceback (most recent call last): AssertionError: Attributes are different
Attribute “dtype” are different [left]: int64 [right]: float64
Ignore differing dtypes in columns with check_dtype. >>> assert_frame_equal(df1, df2, check_dtype=False)
```
see https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.testing.assert_frame_equal.html#pandas-testing-assert-frame-equal
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} | 3 | 2019-04-05T18:48:39Z | 2019-04-06T04:58:42Z | 2019-04-06T04:58:37Z | CONTRIBUTOR | null | This fixes the formatting in docstring examples for `pandas.util.testing.assert_frame_equal` so they will be displayed correctly.
- [X] closes #26003
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} | 1 | 2019-04-05T22:01:59Z | 2019-07-01T21:56:48Z | 2019-07-01T21:56:48Z | NONE | null | #### Code Sample
```python
# Case 1: single row
df1 = pd.DataFrame({'B': [0]}, index=[pd.Timestamp('20130101 09:00:00')])
df1.rolling('1s', closed='left').median() # <- raises 'MemoryError: skiplist_init failed'
# Case 2: multiple rows, but entries separated by a larger time than the specified window
df2 = pd.DataFrame({'B': [0, 1]}, index=[pd.Timestamp('20130101 09:00:00'), pd.Timestamp('20130101 09:00:02')])
df2.rolling('1s', closed='left').median() # <- raises 'MemoryError: skiplist_init failed'
df2.rolling('1s', closed='left').max() # <- no error, but second entry seems incorrect
# Case 3: as long as at least one row has other entries in its window, it runs without
# an exception but the values are suspect
df3 = pd.DataFrame({'B': [1, 2, 3]}, index=[pd.Timestamp('20130101 09:00:00'), pd.Timestamp('20130101 09:00:02'), pd.Timestamp('20130101 09:00:03')])
df3.rolling('1s', closed='left').median() # <- no exception, but the values seem incorrect
df3.rolling('1s', closed='left').max() # ditto
df3.rolling('2s', closed='left').median() # ditto (note longer window)
```
#### Problem description
Obviously, the exception cases are a big problem and should be addressed. The other cases laid out here seem to give unexpected results that are inconsistent with other aggregations (such as mean and sum) that do seem to be operating correctly. Note that using closed='right' or closed='both' does seem to give results consistent with my expectations, while using closed='neither' yields similar problems as closed='left'. (So, it would seem that the common factor here is whether or not the input rows are included in their own rolling windows.)
#### Expected Output
Case 1: since there are no other entries in the input row's window, I would expect that the median aggregation return NaN. (This would be consistent with mean, max, etc. for this case.)
```python
B
2013-01-01 09:00:00 NaN
```
Case 2: since neither input row should have any other entries in their windows, I would expect that the median and max results should all be NaN. (This would be consistent with what the mean aggregation returns for this case.)
```python
B
2013-01-01 09:00:00 NaN
2013-01-01 09:00:02 NaN
```
Case 3a and 3b (1s window): since neither of the first two input rows should have any other entries in their windows, I would expect that their median and max results should all be NaN. since the last row does have an entry in its window (the second row) I would expect that both the median and max should be 2.0. (This would be consistent with what the mean aggregation returns for this case.)
```python
B
2013-01-01 09:00:00 NaN
2013-01-01 09:00:02 NaN
2013-01-01 09:00:03 2.0
```
Case 3c (2s window): since the first row should have no entries in its window, I would expect the first output row to be NaN. the second row will have the first entry in its window, so I would expect its output to be 1.0. similarly, the last row will have the second entry in its window and I would expect its output to be 2.0.
```python
B
2013-01-01 09:00:00 NaN
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 2.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.14.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: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.0
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.0
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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] | closed | false | null | [] | null | 1 | 2019-04-05T22:25:41Z | 2019-04-06T05:05:41Z | 2019-04-06T05:05:41Z | NONE | null | Does pandas provide a way to set its options without importing the module?
I've seen the advice to add something like this to the iPython startup:
```
import pandas
pandas.options.display.some_pandas_option = 42
```
this works in iPython and notebooks, but has a couple of problems for my use case:
1. It doesn't help for scripts run with `$ python my_pandas_script.py` because `python` obviously doesn't run the iPython startup stuff.
2. It leaves `pandas` in the `globals()` of the user's iPython session, which I don't want to do (I'm trying to set defaults for a multiuser environment, but I otherwise want things to feel as usual). I know I can add `del pandas` to that startup script and it will work (i.e. the option will be set correctly for the user if they import pandas), but ...
3. It just _feels_ ugly (and `import pandas` is non-zero (but not trivial) startup overhead).
Patching the pandas library itself is not an option in this context (the user is free to upgrade it).
Do I have any other options? e.g. environment variables, dotfiles, etc., etc.? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26007 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26007/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26007/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26007/events | https://github.com/pandas-dev/pandas/issues/26007 | 429,971,012 | MDU6SXNzdWU0Mjk5NzEwMTI= | 26,007 | Leap year bug in tseries/holiday.py | {
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] | closed | false | null | [] | null | 6 | 2019-04-05T23:42:58Z | 2019-10-07T06:52:42Z | 2019-10-07T06:52:42Z | NONE | null | Hello. I found a leap year bug for you. Hopefully you can fix before Feb 29th 2020 comes around.
https://github.com/pandas-dev/pandas/blob/181f972d2f4e6bd5c8c33721175f68188cfa1a0d/pandas/tseries/holiday.py#L251-L255
The problem here is that if either `start_date.year - 1` or `end_date.year + 1` give a *common* year, and `self.month` is `2` and `self.day` is `29`, then the `datetime` constructor will return a `ValueError`. | {
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} | 7 | 2019-04-06T05:42:59Z | 2019-04-09T17:08:32Z | 2019-04-09T11:20:18Z | MEMBER | null | - [x] xref #25725
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- Import `from pandas.compat.chainmap import DeepChainMap` directly where used
- Removed `isidentifier`, `str_to_bytes`, `bytes_to_str`, `bind_method` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26009 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26009/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26009/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26009/events | https://github.com/pandas-dev/pandas/issues/26009 | 430,004,510 | MDU6SXNzdWU0MzAwMDQ1MTA= | 26,009 | Pandas drop_duplicates -> Fatal Python error: deallocating None | {
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] | closed | false | null | [] | null | 2 | 2019-04-06T06:47:03Z | 2019-04-09T06:14:12Z | 2019-04-09T06:14:12Z | NONE | null | I have a code that checks an Excel sheet, and if it finds some changes, then takes a snapshot (Pandas Dataframe) of the whole sheet and saves it to a csv with a timestamp. It has been running all day doing it's job correctly, but usually once or twice a day I got the error message below:
```
python
Fatal Python error: deallocating None
Current thread 0x000013d4 (most recent call first):
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\algorithms.py", line 1655 in take_nd
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\internals\blocks.py", line 1238 in take_nd
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\internals\managers.py", line 1235 in <listcomp>
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\internals\managers.py", line 1235 in reindex_indexer
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\internals\managers.py", line 1350 in take
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\generic.py", line 3359 in _take
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\frame.py", line 2969 in _getitem_bool_array
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\frame.py", line 2918 in __getitem__
File "C:\Program Files (x86)\Python37-32\lib\site-packages\pandas\core\frame.py", line 4637 in drop_duplicates
File "C:\Python projects\RTD_recorder_1thrd_Pandas.py", line 79 in <module>
```
This is the code which causes the freeze:
```
final_result = pd.concat([final_result, difference]).drop_duplicates(keep="first", subset=indexes)
```
Here is the full code if it helps: https://www.codepile.net/pile/BRx6W2e0
I installed Pandas via pip on 2019.04.04, so I assume it is the most recent one.
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https://api.github.com/repos/pandas-dev/pandas/issues/26010 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26010/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26010/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26010/events | https://github.com/pandas-dev/pandas/pull/26010 | 430,009,099 | MDExOlB1bGxSZXF1ZXN0MjY4MDIwMjE3 | 26,010 | Incorrect example in wide_to_long docstring | {
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] | closed | false | null | [] | null | 4 | 2019-04-06T07:49:52Z | 2019-05-17T06:29:31Z | 2019-05-03T14:52:44Z | NONE | null | - [x] closes #25733
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-04-06T08:56:07Z | 2019-05-05T22:20:00Z | 2019-05-05T22:20:00Z | NONE | null | #### Code Sample
```python
>>> import pandas as pd
>>> import numpy as np
>>> test = pd.DataFrame(data = [[np.NaN, 1, np.NaN], [2, 3, 4]], index = [0, 1], columns = ['one','two','three'])
>>> test
one two three
0 NaN 1 NaN
1 2.0 3 4.0
>>> test.groupby('one').nth(0)
two three
one
2.0 1 NaN
2.0 3 4.0
```
#### Problem description
A `df.groupby('colname').nth(0)` operation should return a new data-frame with an index that contains no duplicates; each unique `colname` will become a single value in the resulting index.
However if the column labeled `colname` contains a `np.NaN` value, instead of this `np.NaN` value becoming its own unique value in the index or simply ignored, it instead turns into a duplicate value as seen above, which is extremely unexpected behavior (probably a bug).
Note only `nth` is affected - `first` and `last` never produce duplicate indices. However `first` and `last` have performance problems on categorical data: https://github.com/pandas-dev/pandas/issues/25397
#### Expected Output
```python
# first actually "just works"
>>> test.groupby('one').first()
two three
one
2.0 3 4.0
# so probably this is the correct behavior
>>> test.groupby('one').nth(0)
two three
one
2.0 3 4.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.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: None
pip: 19.0.3
setuptools: 39.0.1
Cython: None
numpy: 1.15.3
scipy: 1.1.0
pyarrow: 0.12.0
xarray: 0.11.0
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.6
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.8
feather: 0.4.0
matplotlib: 3.0.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.6
bs4: 4.6.3
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: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/26012 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26012/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26012/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26012/events | https://github.com/pandas-dev/pandas/pull/26012 | 430,042,588 | MDExOlB1bGxSZXF1ZXN0MjY4MDQxMTE5 | 26,012 | BUG: Improve col_space in to_html to allow css length strings (#25941) | {
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} | 7 | 2019-04-06T15:04:37Z | 2019-04-29T00:47:52Z | 2019-04-29T00:47:47Z | CONTRIBUTOR | null | - [x] closes #25941
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adds the ability to pass a string as the `col_space` parameter in `to_html`.
For backwards compatibility and similarity for other `to_*` functions, I kept in the ability to pass an `int`. The `int` is automatically turned into `px` units (I chose `px` because that is the default if you put a unit-less CSS length into chrome). If you want that removed and to always expect a string, let me know. | {
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