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} | 0 | 2019-04-06T16:02:32Z | 2019-04-07T23:19:21Z | 2019-04-07T23:19:21Z | CONTRIBUTOR | null | Part of #25882
pandas.tseries files currently have these errors:
```
pandas/tseries/frequencies.py:40: error: Need type annotation for '_offset_map'
pandas/tseries/holiday.py:332: error: Need type annotation for 'rules'
pandas/tseries/offsets.py:1656: error: Incompatible types in assignment (expression has type "int", base class "QuarterOffset" defined the type as "None")
pandas/tseries/offsets.py:1657: error: Incompatible types in assignment (expression has type "int", base class "QuarterOffset" defined the type as "None")
pandas/tseries/offsets.py:1666: error: Incompatible types in assignment (expression has type "int", base class "QuarterOffset" defined the type as "None")
pandas/tseries/offsets.py:1667: error: Incompatible types in assignment (expression has type "int", base class "QuarterOffset" defined the type as "None")
pandas/tseries/offsets.py:1681: error: Incompatible types in assignment (expression has type "int", base class "QuarterOffset" defined the type as "None")
pandas/tseries/offsets.py:1688: error: Incompatible types in assignment (expression has type "int", base class "QuarterOffset" defined the type as "None")
pandas/tseries/offsets.py:1689: error: Incompatible types in assignment (expression has type "int", base class "QuarterOffset" defined the type as "None")
```
I have added annotations to address these issues and removed the modules from mypy.ini. PR to follow presently. | {
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} | 4 | 2019-04-06T16:05:28Z | 2019-04-08T00:00:14Z | 2019-04-07T23:19:21Z | CONTRIBUTOR | null | - [X] closes #26013
- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Part of #25882 | {
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} | 10 | 2019-04-06T16:28:39Z | 2019-05-01T11:42:36Z | 2019-05-01T11:42:32Z | CONTRIBUTOR | null | - [x] closes #23785
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Uses the built-in categorical series mapper when mapping categorical series with dictionaries or series (i.e., as opposed to with lambada functions) instead of reindexing in order to improve performance.
## benchmarking with asv
I added some new benchmarks to test mapping performance -- performance improves substantially when mapping with dicts or series against categorical series
```
before after ratio
[181f972d] [184feb77]
<master~1>
- 1.90±0.01ms 976±8μs 0.51 series_methods.Map.time_map('dict', 'category')
- 1.33±0.01ms 418±2μs 0.31 series_methods.Map.time_map('Series', 'category')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
## benchmarking using example from #23785
upstream master
```python
import pandas as pd
print(pd.__version__)
# 0.24.2
x = pd.Series(list('abcd') * 1000).astype('category')
mapper = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
%timeit x.map(mapper)
# 904 µs ± 3.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit x.map(lambda a: mapper[a])
# 254 µs ± 997 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
this diff
```python
import pandas as pd
print(pd.__version__)
# 0.25.0.dev0+379.g184feb77e
x = pd.Series(list('abcd') * 1000).astype('category')
mapper = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
%timeit x.map(mapper)
# 622 µs ± 1.08 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit x.map(lambda a: mapper[a])
# 256 µs ± 4.72 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
``` | {
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} | 3 | 2019-04-06T21:06:01Z | 2019-04-07T23:49:27Z | 2019-04-07T21:28:59Z | MEMBER | null | - [x] xref #25725
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Replace `compat.signature` for `inspect.getfullargspec` | {
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} | 11 | 2019-04-07T15:03:51Z | 2020-01-06T02:50:41Z | 2019-04-10T01:18:39Z | CONTRIBUTOR | null | - [x] xref #25932, #25933, #25934
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} | 0 | 2019-04-07T15:17:18Z | 2019-04-08T17:33:05Z | 2019-04-08T17:33:05Z | CONTRIBUTOR | null | Part of #25882
pandas.plotting._core currently has these errors:
```
pandas/plotting/_core.py:958: error: Incompatible types in assignment (expression has type "str", base class "MPLPlot" defined the type as "None")
pandas/plotting/_core.py:1177: error: Incompatible types in assignment (expression has type "str", base class "MPLPlot" defined the type as "None")
pandas/plotting/_core.py:1728: error: "type" has no attribute "_kind"
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https://api.github.com/repos/pandas-dev/pandas/issues/26022 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26022/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26022/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26022/events | https://github.com/pandas-dev/pandas/pull/26022 | 430,172,234 | MDExOlB1bGxSZXF1ZXN0MjY4MTI0MTAx | 26,022 | BUG: Replace internal use of loc with reindex in DataFrame append | {
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} | 10 | 2019-04-07T17:40:41Z | 2019-04-21T16:21:07Z | 2019-04-21T16:21:00Z | CONTRIBUTOR | null | The issue here was that the DataFrame append method was using .loc, which only throws a warning now, but would eventually throw a KeyError whenever that went into effect. Just swapped out that use of .loc for .reindex.
- [x] closes #22252
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26023 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26023/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26023/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26023/events | https://github.com/pandas-dev/pandas/issues/26023 | 430,193,742 | MDU6SXNzdWU0MzAxOTM3NDI= | 26,023 | pd.DataFrame.to_csv('filename.zip') doesn't extract with a '.csv' extension | {
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} | 7 | 2019-04-07T21:19:59Z | 2021-03-15T10:31:20Z | 2019-08-26T14:27:41Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'name': ['Raphael', 'Donatello'], 'mask': ['red', 'purple']})
# When trying to create a compressed csv, these give odd results.
df.to_csv('out.csv', compression='zip') # --> zip file named 'out.csv' containing csv file 'out.csv'
df.to_csv('out.zip') # --> zip file named 'out.zip' containing csv file 'out.zip'
df.to_csv('out.csv.zip') # --> zip file named 'out.csv.zip' containing csv file 'out.csv.zip'
# This would be the desired behaviour, if we had an 'arcname'
# parameter like zipfile.ZipFile.write(arcname, data)
df.to_csv('out.zip', arcname='data.csv') # --> zip file named 'out.zip' containing csv file 'data.csv'
```
#### Problem description
When `pd.DataFrame.to_csv` creates compressed zip files, the name of the csv file inside the archive is always the same as the name of the zip archive file itself. This is obviously problematic because the archive has a `.zip` extension but we want the csv file to have a `.csv` extension when it is extracted.
Other compression methods meant for a single file like 'bz2', 'gzip', and 'xz' do not have this problem because a file 'file.csv.gz' for instance, will automatically become 'file.csv' when decompressed.
This would be a relatively easy fix by adding an `arcname=None` parameter to `to_csv`, passing it through `pandas.io.formats.csvs.CSVFormatter` to `pandas.io.formats.csvs._get_handle` and using that instead of `ZipFile.filename` if provided.
#### Expected Output
See comments in Code Sample above for expected output.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-17-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.0.0
Cython: 0.28.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: None
IPython: 7.1.1
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: 2.4.11
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26024/events | https://github.com/pandas-dev/pandas/pull/26024 | 430,222,297 | MDExOlB1bGxSZXF1ZXN0MjY4MTU4NTAy | 26,024 | ENH: Allow compression in NDFrame.to_csv to be a dict with optional arguments (#26023) | {
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} | 24 | 2019-04-08T01:51:16Z | 2019-08-26T14:27:48Z | 2019-08-26T14:27:41Z | CONTRIBUTOR | null | - [x] closes #26023
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26025/events | https://github.com/pandas-dev/pandas/issues/26025 | 430,261,263 | MDU6SXNzdWU0MzAyNjEyNjM= | 26,025 | DataFrame.eval column creation slow due to overhead | {
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```python
df = pd.DataFrame({'a':np.random.uniform(size=int(1e7)),
'b':np.random.uniform(size=int(1e7))})
%%timeit
df['c'] = df['a'] + df['b']
41.7 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
%%timeit
df.eval('c = a + b', inplace = True)
68.8 ms ± 6.02 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
```python
%prun -l 4 df.eval('c = a + b', inplace = True)
3548 function calls (3490 primitive calls) in 0.061 seconds
Ordered by: internal time
List reduced from 390 to 4 due to restriction <4>
ncalls tottime percall cumtime percall filename:lineno(function)
7 0.039 0.006 0.039 0.006 {method 'copy' of 'numpy.ndarray' objects}
1 0.012 0.012 0.012 0.012 blocks.py:335(set)
1 0.006 0.006 0.006 0.006 necompiler.py:765(evaluate)
585 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}
%prun -l 4 df['c'] = df['a'] + df['b']
638 function calls (636 primitive calls) in 0.037 seconds
Ordered by: internal time
List reduced from 160 to 4 due to restriction <4>
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.014 0.014 0.014 0.014 blocks.py:335(set)
2 0.014 0.007 0.014 0.007 {method 'copy' of 'numpy.ndarray' objects}
1 0.007 0.007 0.007 0.007 necompiler.py:765(evaluate)
1 0.000 0.000 0.014 0.014 managers.py:1019(set)
```
#### Problem description
Column creation with DataFrame.eval seems to be slower (in this dummy example only somewhat slower, but in my "real-world" case it was slower by a factor of 4), because it is making a lot of calls to copy for numpy.ndarray. Also see [here](https://stackoverflow.com/questions/38725355/when-to-use-dataframe-eval-versus-pandas-eval-or-python-eval)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.3.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.9
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: 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/26026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26026/events | https://github.com/pandas-dev/pandas/issues/26026 | 430,267,234 | MDU6SXNzdWU0MzAyNjcyMzQ= | 26,026 | Problem with reading .dat file with missing data | {
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```python
# Your code here
df=pd.read_table('out_missingdata.dat',sep='\s+',header=None)
print(df.iloc[:37,:])
```
#### Problem description
I have a .dat file with three columns with some columns having missing values(empty) and delimiter is a whitespace.When I am printing dataframe data is getting shifted to empty spaces and NaN is displayed at last column.I don't know what is mistake in code.
#### Expected Output
24 3869089 64529089 1296.0
25 NaN 64513024 1296
26 3876961 64496961 1296.0
27 3880900 64480900 1296.0
28 3884841 64464841 1296.0
29 3888784 64448784 1296.0
30 3892729 64432729 1296.0
31 3896676 64416676 1296.0
32 3900625 64400625 1296.0
33 3904576 64384576 1296.0
34 3908529 NaN 1296
35 3912484 64352484 1296.0
36 3916441 64336441 1296.0
#### Output of ``pd.show_versions()``
24 3869089 64529089 1296.0
25 64513024 1296 NaN
26 3876961 64496961 1296.0
27 3880900 64480900 1296.0
28 3884841 64464841 1296.0
29 3888784 64448784 1296.0
30 3892729 64432729 1296.0
31 3896676 64416676 1296.0
32 3900625 64400625 1296.0
33 3904576 64384576 1296.0
34 3908529 1296 NaN
35 3912484 64352484 1296.0
36 3916441 64336441 1296.0
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"description": "S... | closed | false | null | [] | null | 3 | 2019-04-08T14:18:36Z | 2019-06-08T20:23:34Z | 2019-06-08T20:23:34Z | NONE | null | - [x] closes na (once proposed in #8749 but not implemented so far)
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR proposes to add partial string matching functionality to `query` method. In the proposed implementation, a query like `df.query('"alice" in person_name', partial_str_match=True)` returns rows with 'person_name' containing 'alice' in them. For other kind of queries (e.g. `'["alice", "bob"] in person_name'` or `'age == 20'`) with `partial_str_match=True` or those with `partial_str_match=False` (which is set to default for backward compatibility), results as before are returned. | {
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} | 0 | 2019-04-08T17:32:29Z | 2019-04-12T14:46:19Z | 2019-04-12T14:46:19Z | CONTRIBUTOR | null | Part of #25882
pandas.core.dtypes.dtypes and .missing have the following errors:
```
pandas/core/dtypes/dtypes.py:112: error: Need type annotation for 'shape'
pandas/core/dtypes/dtypes.py:117: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:175: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:175: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:220: error: Incompatible types in assignment (expression has type "Type[CategoricalDtypeType]", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:221: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:222: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:224: error: Incompatible types in assignment (expression has type "Tuple[str, str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:225: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:577: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:577: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:588: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:589: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:593: error: Incompatible types in assignment (expression has type "Tuple[str, str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:595: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:732: error: Definition of "kind" in base class "ExtensionDtype" is incompatible with definition in base class "PandasExtensionDtype"
pandas/core/dtypes/dtypes.py:732: error: Definition of "type" in base class "ExtensionDtype" is incompatible with definition in base class "PandasExtensionDtype"
pandas/core/dtypes/dtypes.py:740: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:741: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:744: error: Incompatible types in assignment (expression has type "Tuple[str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:746: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:855: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:855: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:864: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:867: error: Incompatible types in assignment (expression has type "Tuple[str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:869: error: Need type annotation for '_cache'
pandas/core/dtypes/missing.py:6: error: Module 'pandas._libs' has no attribute 'missing'
```
Working on a PR to fix these. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26029/events | https://github.com/pandas-dev/pandas/pull/26029 | 430,685,391 | MDExOlB1bGxSZXF1ZXN0MjY4NTIwMjg1 | 26,029 | Fix type annotations in pandas.core.dtypes | {
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} | 3 | 2019-04-08T22:25:48Z | 2019-04-12T15:47:46Z | 2019-04-12T14:46:20Z | CONTRIBUTOR | null | - [X] closes #26028
- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This one was more complicated than other annotation corrections I've done. The following errors persisted even if the two type definitions in `PandasExtensionDtype` and `ExtensionDtype` were identical:
```
pandas/core/dtypes/dtypes.py:175: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:175: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:577: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:577: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:732: error: Definition of "kind" in base class "ExtensionDtype" is incompatible with definition in base class "PandasExtensionDtype"
pandas/core/dtypes/dtypes.py:732: error: Definition of "type" in base class "ExtensionDtype" is incompatible with definition in base class "PandasExtensionDtype"
pandas/core/dtypes/dtypes.py:855: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:855: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
```
I suspect this has something to do with how mypy handles classes with multiple inheritance and maybe even @property-decorated accessors. See, e.g., https://github.com/python/mypy/pull/6585.
So to address the issue, I left the annotations in pandas.core.dtypes.base.ExtensionDtype.type and .kind (abstract-ish with @property decorators) alone but for a format change. I set the corresponding `type` and `kind` variables in `PandasExtensionDtype` to take the `Any` type explicitly to "correct" the mypy error. Then to provide actual type checking I explicitly typed the `kind` and `type` variables in the subclasses, i.e., CategoricalDtype, DatetimeTZDtype, etc.
Not sure if this is the best approach, but it is a functioning approach that provides static type checking.
This commit addresses all the following errors:
<details>
```
pandas/core/dtypes/dtypes.py:112: error: Need type annotation for 'shape'
pandas/core/dtypes/dtypes.py:117: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:175: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:175: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:220: error: Incompatible types in assignment (expression has type "Type[CategoricalDtypeType]", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:221: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:222: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:224: error: Incompatible types in assignment (expression has type "Tuple[str, str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:225: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:577: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:577: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:588: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:589: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:593: error: Incompatible types in assignment (expression has type "Tuple[str, str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:595: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:732: error: Definition of "kind" in base class "ExtensionDtype" is incompatible with definition in base class "PandasExtensionDtype"
pandas/core/dtypes/dtypes.py:732: error: Definition of "type" in base class "ExtensionDtype" is incompatible with definition in base class "PandasExtensionDtype"
pandas/core/dtypes/dtypes.py:740: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:741: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:744: error: Incompatible types in assignment (expression has type "Tuple[str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:746: error: Need type annotation for '_cache'
pandas/core/dtypes/dtypes.py:855: error: Definition of "type" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:855: error: Definition of "kind" in base class "PandasExtensionDtype" is incompatible with definition in base class "ExtensionDtype"
pandas/core/dtypes/dtypes.py:864: error: Incompatible types in assignment (expression has type "str", base class "PandasExtensionDtype" defined the type as "None")
pandas/core/dtypes/dtypes.py:867: error: Incompatible types in assignment (expression has type "Tuple[str]", base class "_DtypeOpsMixin" defined the type as "Tuple[]")
pandas/core/dtypes/dtypes.py:869: error: Need type annotation for '_cache'
pandas/core/dtypes/missing.py:6: error: Module 'pandas._libs' has no attribute 'missing'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/26030 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26030/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26030/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26030/events | https://github.com/pandas-dev/pandas/issues/26030 | 430,816,977 | MDU6SXNzdWU0MzA4MTY5Nzc= | 26,030 | to_sql with postgres fails silently when given uppercase/capitalized table names | {
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"default": false,... | closed | false | null | [] | null | 4 | 2019-04-09T07:47:21Z | 2019-04-10T16:52:45Z | 2019-04-09T23:03:27Z | NONE | null | I attempted to use `to_sql` to insert into a Postgres table with a non-lowercase identifier. No error was raised in python, but the insert did not succeed:
```python
import pandas as pd
import sqlalchemy
df = pd.DataFrame({
u'id': [1, 2],
u'name': [u'foo', u'bar'],
})
engine = sqlalchemy.create_engine('postgresql://{user}:{password}@{host}/{dbname}'. format(
user='myuser', password='password', host='localhost', dbname='mydatabase'))
df.to_sql('TMP1', engine, index=False, if_exists='replace')
df.to_sql('tmp2', engine, index=False, if_exists='replace')
df.to_sql('"TMP3"', engine, index=False, if_exists='replace')
```
When I check the psql console, the 'tmp2' table is populated, but there is no 'tmp1' or 'TMP3' table:
```psql
mydatabase=> select * from tmp1;
ERROR: relation "tmp1" does not exist
LINE 1: select * from tmp1;
^
mydatabase=> select * from tmp2;
id | name
----+------
1 | foo
2 | bar
mydatabase=> select * from "TMP3";
ERROR: relation "TMP3" does not exist
LINE 1: select * from "TMP3";
^
```
</details>
#### Problem description
This MWE was tested with Postgres 11.2 on OSX 10.14.4 and with Postgres 9.5.15 on Red Hat 4.8.3-9.
The most related issue I saw was [#7815](https://github.com/pandas-dev/pandas/issues/7815), but I didn't understand its resolution. It sounded to me as though a subsequent release would have `to_sql` check which table was created/updated, if any, and return a warning on mismatch with its name parameter, but I did not observe comparable behavior.
#### Expected Output
I would expect either a successful insert or an exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: 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: 5.8.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.6.1
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.2.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: 2.8.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/26031 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26031/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26031/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26031/events | https://github.com/pandas-dev/pandas/issues/26031 | 430,880,539 | MDU6SXNzdWU0MzA4ODA1Mzk= | 26,031 | Series: inconsistent behavior of setting value with timestamp dtype for existed index and newly-added | {
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} | 8 | 2019-04-09T10:05:22Z | 2019-12-10T22:20:59Z | 2019-12-10T22:20:58Z | NONE | null |
```python
sample = pd.Series([1,2],index=['a','b'])
ts = pd.Timestamp('2019-01-01')
sample['a'] = ts
sample['c'] = ts
print(sample)
```
#### output:
```
a 2019-01-01 00:00:00
b 2
c 1546300800000000000
dtype: object
```
#### Problem description
With existed index `a`, the dtype will be Timestamp, as expected. Howerver, when setting a new index like `c`, the dtype will be int.
Is this a bug?
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: zh_CN.UTF-8
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: 1.2.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
</details>
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} | 5 | 2019-04-09T15:22:32Z | 2020-09-24T03:27:39Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Series of bools
s = pd.Series(np.random.randint(0, 2, 100000)).astype(bool)
# ~1.45 ms
%timeit s.any(skipna=True)
# ~1.35 ms
%timeit s.any(skipna=False)
# ~6.5 us - Note that I get a message about possible caching, but
# even after multiplying by worst case multiplier, still an order of
# magnitude faster than s.any()
%timeit s.values.any()
# Series of ints
s2 = pd.Series(np.random.randint(0, 2, 100000))
# ~330 us
%timeit s2.any(skipna=True)
# ~280 us
%timeit s2.any(skipna=False)
# ~90 us - No possible caching warning on this one
%timeit s2.values.any()
```
#### Problem description
Calling Series.any is much slower than calling Series.values.any on a series of bools
Interestingly, calling Series.any on a series of ints is quite a bit faster than on a series of bools, though even if it is a series of ints, Series.values.any is still faster.
I ran with both skipna=True and skipna=False in case it was an issue of how NaNs are being handled.
I see the same time differences with Series.all
#### Expected Output
I would expect the performance to be comparable. Maybe not exactly the same,, but not order(s) of magnitude slower.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.3
pytest: 3.3.0
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.1
scipy: 0.18.0
pyarrow: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
feather: None
matplotlib: 1.5.1
openpyxl: 2.5.6
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.9999999
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.8
s3fs: 0.0.8
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | [] | closed | false | null | [] | null | 4 | 2019-04-09T16:07:10Z | 2019-04-09T18:31:13Z | 2019-04-09T18:31:13Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
class MyObject():
__array_priority__ =1000 # So __radd__ of this class is called
def __init__(self,s=None):
if s is None:
self.s = pd.Series([1,2,4])
def __radd__(self,other):
print("Series is passed as :",type(other))
self.s = self.s + other
print("After operation, self.s is", self.s) # a pandas series
return self # I want to return object of type:MyObject
custom_obj = MyObject()
s = pd.Series([5,6,7])
s + custom_obj # Making pd.Series interact with my custom object (reverse add)
```
#### Current Output
```python
Series is passed as : <class 'numpy.ndarray'>
After operation, self.s is
0 6
1 8
2 11
dtype: int64
0 <__main__.MyObject object at 0x7f6ebbb365f8>
1 <__main__.MyObject object at 0x7f6ebbb365f8>
2 <__main__.MyObject object at 0x7f6ebbb365f8>
dtype: object
```
#### Problem description
The returned object is inserted into a series which has same index as original series.
Is there a better way to make pandas objects interact with custom objects.
Currently a np.ndarray is passed to __radd__. I know there is a way to make pass the series itself (using extension type??) but I am not sure.
#### Expected Output
The operation s + custom_obj should return an object of type MyObject
<details>
pd.version:0.24
</details>
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} | 2 | 2019-04-09T20:15:56Z | 2019-04-09T20:54:41Z | 2019-04-09T20:35:40Z | NONE | null | #### Problem/Enhancement description
Is there a standard way to take two dataframes that have a DateTimeIndex and do a time series join where same dates will be together on the same day and allow me to apply a function to it in order to choose which value to take if more than one fall on the same day?
If this functionality doesn't exist, can we open an enhancement request?
#### Expected Output
Time Series 1:
```
Date | Value
------------
1-1-2019 | 1
1-2-2019 | 2
1-3-2019 | 3
```
Time Series 2:
```
Date | Value
------------
1-3-2019 | 4
1-4-2019 | 5
1-5-2019 | 6
```
Then,
```python
#default to taking 'right' time series value on overlapped days
ts1.time_series_join(ts2, overlap_func='right')
```
or
```python
#apply lambda applies to overlapped days
ts1.time_series_join(ts2, overlap_func=lambda x,y: max(x,y))
```
which would return (defaulting to a left join behavior as far as keeping the index goes):
```
Date | Value
------------
1-1-2019 | 1
1-2-2019 | 2
1-3-2019 | 4
```
I haven't seen anything like this on my data processing adventures and any insight for this would be great. Right now, I have resorted to doing some gross looping and utilizing the `.at[]` selector to update overlapping values.
Thanks! | {
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"de... | closed | false | null | [] | null | 2 | 2019-04-09T21:06:43Z | 2021-07-02T05:20:24Z | 2021-07-02T05:20:24Z | NONE | null | Enhancement description
A standard way to take two dataframes that have a DateTimeIndex and do a time series join where same dates will be together on the same day and allow me to apply a function to it in order to choose which value to take if more than one fall on the same day.
If this functionality doesn't exist, can we open an enhancement request?
Expected Behavior
Time Series 1:
```
Date | Value
------------
1-1-2019 | 1
1-2-2019 | 2
1-3-2019 | 3
```
Time Series 2:
```
Date | Value
------------
1-3-2019 | 4
1-4-2019 | 5
1-5-2019 | 6
```
Then,
```python
#default to taking 'right' time series value on overlapped days
ts1.time_series_join(ts2, overlap_func='right')
```
or
```python
#apply lambda applies to overlapped days
ts1.time_series_join(ts2, overlap_func=lambda x,y: max(x,y))
```
which would return (defaulting to a left join behavior as far as keeping the index goes):
```
Date | Value
------------
1-1-2019 | 1
1-2-2019 | 2
1-3-2019 | 4
```
I haven't seen anything like this on my data processing adventures and think this would be a great feature. A similar feature is implemented in SparkTS for RDDs. Right now, I have resorted to doing some gross looping and utilizing the `.at[]` selector to update overlapping values.
Thanks! | {
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} | 3 | 2019-04-10T02:40:08Z | 2019-04-11T01:32:58Z | 2019-04-10T06:20:21Z | MEMBER | null | - [X] xref #25725
- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The `PY35` flag is always `True` after dropping Python 2, as Python 3.5 is now the lowest supported version.
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] | closed | false | null | [] | null | 6 | 2019-04-10T03:54:49Z | 2019-05-12T21:23:05Z | 2019-05-12T21:23:05Z | CONTRIBUTOR | null | - [x] closes #24071
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
`./scripts/validate_docstrings.py --prefix=pandas.core --errors=GL01,GL02` is now silent. | {
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} | 2 | 2019-04-10T06:58:46Z | 2019-04-12T15:31:46Z | 2019-04-12T11:36:00Z | MEMBER | null | - [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
More `compat/__init__.py` cleaning.
Remove `parse_date` in favor of `dateutil.parser.parse` directly. pandas min dateutil version is 2.5.0 so the import check is unnecessary. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26039/events | https://github.com/pandas-dev/pandas/issues/26039 | 431,480,603 | MDU6SXNzdWU0MzE0ODA2MDM= | 26,039 | Comparing Timedelta and NaT gives inconsistent results depending on order | {
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} | 3 | 2019-04-10T12:40:03Z | 2019-04-28T16:39:55Z | 2019-04-28T16:39:54Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: pd.Timedelta(0) > pd.Timedelta(None)
TypeError: Cannot compare type Timedelta with type NaTType
In [3]: pd.Timedelta(None) < pd.Timedelta(0)
Out[3]: False
```
#### Problem description
I would expect the result of `a < b` to be the same as that of `b > a` but in this case one returns `False` and the other raises `TypeError`.
Related issue: #24983 "Separate NaT values for Timedelta and Period" - if `pd.Timedelta(None)` gave a value of type `Timedelta` then maybe this error would not have occurred.
#### Expected Output
Either both `False` or both `TypeError` (both `False` would be more helpful)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
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_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: 0.11.3
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
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/26040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26040/events | https://github.com/pandas-dev/pandas/issues/26040 | 431,493,547 | MDU6SXNzdWU0MzE0OTM1NDc= | 26,040 | date_range() option for closed='both' documented but not implemented | {
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} | 3 | 2019-04-10T13:07:54Z | 2019-04-10T15:49:51Z | 2019-04-10T15:49:40Z | NONE | null | #### Problem description
The docstring for `pandas.date_range()`'s `closed` argument says:
```
closed : {None, 'left', 'right'}, optional
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None, the default).
```
Basically, this argument sets whether the date range includes or excludes the start and end dates. But I think 'both' is not possible.
Either the docstring needs changing or (preferably) a 'both' option should be added.
#### Links to the relevant code
Here's the `date_range()` function (whose docstring I copied above):
https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L1403
it passes `closed` to `DatetimeArray._generate_range()`
which is here:
https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/datetimes.py#L421
and which passes `closed` to: `dtl.validate_endpoints(closed)`
which is here, and is where the `validate_endpoints(closed)` would need changing to add a 'both' option, or to allow 'left' and 'right' to be set simultaneously:
https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/datetimelike.py#L1471-1501
Thanks!
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```python
import pandas as pd
from dateutil import parser as date_parser
def perform_operations_that_change_dtype(df):
# If df is a single row DataFrame, the type of the
# datetime columns will be changed to object.
for x in df.columns:
if df[x].dtype.kind == 'M': # if it is a date
if df[x].dt.tz is None: # not time zone aware
df.loc[:, x] = df[x].dt.tz_localize('GMT') # tz aware
df.loc[:, x] = df[x].dt.tz_convert('UTC')
def perform_operations_that_do_not_change_dtype(df):
for x in df.columns:
if df[x].dtype.kind == 'M': # if it is a date
if df[x].dt.tz is None: # not time zone aware
df[x] = df[x].dt.tz_localize('GMT') # tz aware
df[x] = df[x].dt.tz_convert('UTC')
# Show effects of tz_operations on the 'broken' DataFrame when using
# .loc for accessing columns.
broken_df = pd.DataFrame(
[{'reference_date': '2019-03-14T10:00:00Z', 'value': '0.0'}])
broken_df['reference_date'] = broken_df['reference_date'].apply(
lambda datestr: date_parser.parse(datestr))
assert(broken_df['reference_date'].dtype == 'datetime64[ns, tzutc()]')
# broken_df reference_date will be changed to object from datetime
perform_operations_that_change_dtype(broken_df)
assert(broken_df['reference_date'].dtype == 'object')
# Show effects of tz_operations on the 'working' DataFrame when using
# .loc for accessing columns.
working_df = pd.DataFrame(
[{'reference_date': '2019-03-14T10:00:00Z', 'value': '0.0'},
{'reference_date': '2019-03-14T10:00:00Z', 'value': '0.0'}])
working_df['reference_date'] = working_df['reference_date'].apply(
lambda datestr: date_parser.parse(datestr))
assert(working_df['reference_date'].dtype == 'datetime64[ns, tzutc()]')
# this time the datetime type will be perserved
# the tz part will only be changed
perform_operations_that_change_dtype(working_df)
assert(working_df['reference_date'].dtype == 'datetime64[ns, UTC]')
# Show effects of tz_operations on the 'broken' DataFrame when using
# [] for accessing columns.
broken_df = pd.DataFrame(
[{'reference_date': '2019-03-14T10:00:00Z', 'value': '0.0'}])
broken_df['reference_date'] = broken_df['reference_date'].apply(
lambda datestr: date_parser.parse(datestr))
assert(broken_df['reference_date'].dtype == 'datetime64[ns, tzutc()]')
perform_operations_that_do_not_change_dtype(broken_df)
assert(broken_df['reference_date'].dtype == 'datetime64[ns, UTC]')
```
#### Problem description
Hello all!
I am facing a particularly weird issue, which I have pinpointed to be caused by the [_setitem_with_indexer method](https://github.com/pandas-dev/pandas/blob/6d9b702a661891724e6c10dd96eb149404205c67/pandas/core/indexing.py#L298)
From the code above, you can see that when `loc` is used inside `perform_operations_that_change_dtype` to retrieve a column and change its timezone, the resulting series is of dtype `object` instead of `datetime`. This behavior is not present when using `[]` as in `perform_operations_that_do_not_change_dtype`, and is only present for DataFrames with single rows. I know that the two approaches are different, but if both `loc` and `[]` return the same series, why is the resulting assignment different? From what I can see something happens inside the `_setitem_with_indexer` method that changes the type.
One thing that might be the issue is the `_try_coerce_args` method inside `ObjectBlock`: [link to line](https://github.com/pandas-dev/pandas/blob/6d9b702a661891724e6c10dd96eb149404205c67/pandas/core/internals/blocks.py#L2705). Why is `other`'s type changed to object when it is a DatetimeArray?
The only similar issue I managed to find is [this StackOverflow question](https://stackoverflow.com/questions/50359164/pandas-tz-convert-using-apply-returns-object-rather-than-datetime) that has 0 answers.
Thank you for looking at this issue and for creating and maintaining this amazing library! Hope my findings would be enough to guide someone to help me resolve this; I've been hammering at it the whole day.
#### Expected Output
The dtype of datetime is not changed to object.
#### 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 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.0
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
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: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 8 | 2019-04-10T17:42:46Z | 2021-05-03T12:44:08Z | 2019-04-30T09:35:46Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
df_ = pd.DataFrame([predicted_labels])
display(df_)
df_.to_csv('person.csv',index=False)
```
#### Problem description
Actually I don't want index in my csv file so I set index = false but It's not resolving the issue, my CSV file still has index values.
#### Expected Output
3,6,0,6,3,0,6,6,6,6,6,0,2,6,6,6,0,0,2,0,0,0,6,0,2,6,6,6,6,6,3,6,3,6,2,0,6,0,6,6,3,2,6,6,6,6
#### Output of ``pd.show_versions()``
<details>
0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46
3,6,0,6,3,0,6,6,6,6,6,0,2,6,6,6,0,0,2,0,0,0,6,0,2,6,6,6,6,6,3,6,3,6,2,0,6,0,6,6,3,2,6,6,6,6
</details>
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} | 33 | 2019-04-10T18:10:06Z | 2019-07-04T01:38:09Z | 2019-07-04T01:38:05Z | CONTRIBUTOR | null | - [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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} | 1 | 2019-04-10T19:17:59Z | 2019-04-10T19:54:02Z | 2019-04-10T19:53:50Z | NONE | null | #### Code Sample
I want to sort a data frame by a series that has both string and numeric values. Today I have a sequence of commands that seems to 'work', except when the .drop_duplicates method is used. Along the way I had some trouble because a series that has both letters and numbers cannot be used to sort, and I thought that was causing my problem. However, I realized later that drop_duplicates() does not return a fully workable DataFrame.
Here I have drop_duplicates to create a second DataFrame object, and the new thing df2 just doesn't work well:
```python
x = pd.Series([1, 1, 2, 2, 2, 3, "."])
y = pd.Series([1, 1, 2, 2, 2, 3, np.nan])
df = pd.DataFrame({"old": x, "new": y})
df2 = df.drop_duplicates()
df2['oldsort'] = df2['old']
## causes error sorting, must convert oldsort to string
## df2.sort_values(by = ["oldsort"], axis = 0)
df2['oldsort'] = df2['oldsort'].astype(str)
df2.sort_values(by = ["oldsort"], axis = 0)
```
#### Problem description
Output is littered with warnings like this:
```
/home/pauljohn/LinuxDownloads/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:312: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
```
Eventually I traced problem back to `drop_duplicates`. Although it seems to be a data frame object, it does not respond well to anything I want to do. Here is an alternative method I use to achieve same purpose:
```{python}
x = pd.Series([1, 1, 2, 2, 2, 3, "."])
y = pd.Series([1, 1, 2, 2, 2, 3, np.nan])
df = pd.DataFrame({"old": x, "new": y})
df['oldsort'] = df['old']
df['oldsort'] = df['oldsort'].astype(str)
df.sort_values(by = ["oldsort"], axis = 0)
df.drop(['oldsort'], axis=1)
df2 = df.drop_duplicates()
```
I'm not confident that the output from drop_duplicates() is actually a full data frame, however, so I don't trust the last step very much :)
#### Expected Output
a new data frame in which columns can be copied and altered.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
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: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-04-10T20:44:45Z | 2019-04-19T01:03:54Z | 2019-04-19T01:03:54Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.25.0.dev0+389.g6d9b702a66'
In [2]: bins = [pd.Timestamp.min, pd.Timestamp('2018-01-01'), pd.Timestamp.max]
In [3]: values = pd.date_range('2017-12-31', periods=3)
In [4]: pd.cut(values, bins=bins)
---------------------------------------------------------------------------
ValueError: bins must increase monotonically.
```
The issue appears to be due to an overflow in `np.diff`, which in turn makes it appear that the `bins` are not monotonically increasing. Essentially the following:
```python
In [5]: bins_numeric = [b.value for b in bins]
In [6]: bins_numeric
Out[6]: [-9223372036854775000, 1514764800000000000, 9223372036854775807]
In [7]: np.diff(bins_numeric)
Out[7]: array([-7708607236854776616, 7708607236854775807], dtype=int64)
```
#### Problem description
The `bins` are monotonically increasing but a `ValueError` is raised indicating that they aren't.
#### Expected Output
I'd expect `[4]` to not raise a `ValueError`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 6d9b702a661891724e6c10dd96eb149404205c67
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.25.0.dev0+389.g6d9b702a66
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.6.3
Cython: 0.28.2
numpy: 1.14.6
scipy: 1.0.0
pyarrow: 0.6.0
xarray: 0.9.6
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml.etree: 3.8.0
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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- [X] tests passed
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} | 2 | 2019-04-11T06:51:44Z | 2019-04-11T15:41:40Z | 2019-04-11T15:41:36Z | MEMBER | null | Given the master list in #25882 a review it looks like these are no longer issues, I think from the top level import machinery changes | {
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} | 5 | 2019-04-11T08:01:13Z | 2020-11-10T17:50:45Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame(columns=['col1'], data=[1,2,3,4], dtype='uint8')
print('original dtypes:')
print(df.dtypes)
print()
print('original data frame:')
print(df)
print()
df.loc[2,'col1']=300
print('dtypes after write operation:')
print(df.dtypes)
print()
print('data frameafter write:')
print(df)
```
output:
```
original dtypes:
col1 uint8
dtype: object
original data frame:
col1
0 1
1 2
2 3
3 4
dtypes after write operation:
col1 int64
dtype: object
data frameafter write:
col1
0 1
1 2
2 44
3 4
```
#### Problem description
When writing, e.g., a too big integer to an 8-bit unsigned integer column, the value of the written integer is casted to uint8 and the data type of the column is changed to int64.
#### Expected Output
I would expect that **either** the value is casted and the data type is retained **or** the data type gets changed and the value is retained.
```
original dtypes:
col1 uint8
dtype: object
original data frame:
col1
0 1
1 2
2 3
3 4
dtypes after write operation:
col1 uint8
dtype: object
data frameafter write:
col1
0 1
1 2
2 44
3 4
```
or
```
original dtypes:
col1 uint8
dtype: object
original data frame:
col1
0 1
1 2
2 3
3 4
dtypes after write operation:
col1 int64 [or even better uint16]
dtype: object
data frameafter write:
col1
0 1
1 2
2 300
3 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.125-linuxkit
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.1.1
sphinx: 2.0.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.5
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 7 | 2019-04-11T09:25:05Z | 2020-09-03T09:21:39Z | 2020-04-25T12:40:03Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
ar = np.random.normal(size=[100,10])
df = pd.DataFrame(ar).astype(str)
df.replace('0',None)
```
#### Problem description
Every time I run something similar to this (replacing, in a string DataFrame/DataFrame column, a specific string with `None`), the columns get filled with the element directly above it in the column; this creates a lot of confusion since None is, pretty much, THE "pythonic" way of signalling the absence of a variable/whatever. It works fine if replaced with np.nan, but if anything, I would expect them to work very approximately the same.
#### Expected Output
The expected output would be all strings matching the expression in `replace` to either be replaced with either `None` or `NaN`, or at least a warning message alerting for this behaviour.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.6.2
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 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
gcsfs: None
</details>
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} | 14 | 2019-04-11T11:17:57Z | 2021-08-17T12:19:59Z | 2019-06-01T14:04:15Z | NONE | null | #### Problem description
For **extremely** large sheets (row_num > 1048576, col_num > 16384), see [the code of XlsxWriter Engine](https://github.com/jmcnamara/XlsxWriter/blob/master/xlsxwriter/worksheet.py#L183), area which exceeds the limit won't be printed.
**_However, no error or warning is raised._**
#### Expected Output
```This sheet is too large! Area off (1048576, 1638) will not be printed.```
#### Output of ``pd.show_versions()``
latest (v0.24.2) | {
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] | closed | false | null | [] | null | 2 | 2019-04-11T11:17:58Z | 2019-04-16T10:39:33Z | 2019-04-16T10:39:33Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},index=['a', 'b', 'c'])
df.to_hdf('data.h5', key='df', mode='w')
```
#### Problem description
[The standard example (see above) throws the following error:
```python
Traceback (most recent call last):
File "test.py", line 5, in <module>
df.to_hdf('data.h5', key='df', mode='w')
File "/zpool/software/apps/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py", line 1996, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
File "/zpool/software/apps/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 278, in to_hdf
complib=complib) as store:
File "/zpool/software/apps/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 491, in __init__
self.open(mode=mode, **kwargs)
File "/zpool/software/apps/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 576, in open
tables = _tables()
File "/zpool/software/apps/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 249, in _tables
if LooseVersion(tables.__version__) < LooseVersion('3.0.0'):
AttributeError: module 'tables' has no attribute '__version__'
```
I did a fresh install of anaconda version 4.6.11 and the output of
```
conda list pytables
```
is
```
pytables 3.4.4 py37ha205bf6_0
```
=> pytables version should be bigger than 3.0.0
]
**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>
[INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.15.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
]
</details>
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} | 3 | 2019-04-11T14:13:09Z | 2019-04-24T21:03:05Z | 2019-04-24T21:03:05Z | NONE | null | #### Code Sample
```python
df = pd.DataFrame(list(range(8)))
a.index = pd.MultiIndex.from_product([[0,1]]*3)
a.sort_index(level=[1,2,0])
```
#### Problem description
This throws an `IndexEror: pop index out of range`.
If I change the order to `[2,0,1]`, it doesn't.
python: 3.6.6.final.0
pandas: 0.24.2
numpy: 1.15.4
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} | 6 | 2019-04-11T18:27:03Z | 2019-04-24T21:03:10Z | 2019-04-24T21:03:06Z | CONTRIBUTOR | null | - [x] closes #26053
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] tests added / passed | {
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} | 20 | 2019-04-11T20:37:07Z | 2019-04-28T16:45:44Z | 2019-04-28T16:45:40Z | CONTRIBUTOR | null | - [ ] closes #13503
- [X] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
Added axis parameter to the constructor call in the count function, fixing the original issue in #13503. This fix works for the example given in the issue. This does not fix the problem addressed by jorisvandenbossche, as that seems to be a type issue.
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} | 1 | 2019-04-12T03:05:40Z | 2019-04-12T06:47:18Z | 2019-04-12T06:47:13Z | NONE | null | Like spark-sql,can we execute sql on dataframe to transform data ?
for example:
```python
import pandas as pd
df=pd.DataFrame({"a":[1,1,2],"b":[2,3,4]})
df
# a b
# 0 1 2
# 1 1 3
# 2 2 4
df.create_temp_view("t")
result=df.sql("select a, sum(b) from t group by a")
result
# a sum(b)
# 0 1 5
# 1 2 4
```
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https://api.github.com/repos/pandas-dev/pandas/issues/26057 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26057/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26057/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26057/events | https://github.com/pandas-dev/pandas/issues/26057 | 432,391,883 | MDU6SXNzdWU0MzIzOTE4ODM= | 26,057 | Pandas to_csv not writing correctly to nas but working on lokal folders | {
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"... | closed | false | null | [] | null | 13 | 2019-04-12T05:21:22Z | 2019-04-17T09:10:32Z | 2019-04-17T09:08:50Z | NONE | null | I wrote a function which should remove duplicate lines from all .csv files in a certain path. When i run the skript on lokal ressources it works fine but when i enter a target in my nas the skript finds and states to process all files but does not drop any duplicate lines. Instead it messes up some lines and occasionally a reprocessing throws: "CRITICAL:root:'utf-8' codec can't decode byte 0xa0 in position 0: invalid start byte"
which seems also to be caused by through the skript messed up data.
I tried os.chdir(path) to make the execution path the target path - No change. I tried file as filename only and as full path - No change. if i read the file from the nas and save it locally, it works again.
I am using Win7 x64
my function looks like this:
```python
compress(path):
codec='utf-8'
file=path+name
df = pd.read_csv(file, sep=';',lineterminator='\n', encoding=codec, index_col=0)
df.drop_duplicates(subset=None, keep='first', inplace=True)
df.to_csv(file, header=True, sep=';',line_terminator='\n', encoding=codec, mode='w')
return 0
```
```
My Data look like:
;a;b;c;d;e
0;1;1;1;1;1
1;1;1;1;1;1
2;1;1;1;1;1
3;1;1;1;1;1
4;1;1;1;1;1
out comes:
;a;b;c;d;e
0;1;1;1;1;1
1;1;1;1;1;1
2;1;1;1;1;1
3;1;1;1;1;1
4;1;1;1;1;1
I would expect:
;a;b;c;d;e
0;1;1;1;1;1
```
Which is also the result when the data are stored localy and not in the nas. Whats wrong here? | {
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} | 14 | 2019-04-12T08:23:12Z | 2020-05-08T03:20:47Z | 2020-05-08T03:20:46Z | MEMBER | null | The docs are currently being built with sphinx 1.8.5 (see eg https://travis-ci.org/pandas-dev/pandas/jobs/518832177 for a recent build on master).
Sphinx has released 2.0.0 (http://www.sphinx-doc.org/en/master/changes.html#release-2-0-0-released-mar-29-2019), and it would be good to test our docs with this new release, and see if we need to make changes / report regressions to sphinx.
For somebody wanting to tackle this:
- test it locally to see if there are big problems with building the docs
- make a PR that ensures sphinx 2 is installed in the doc environment, so we can check the build log on travis (I am actually not fully sure why it is not yet picking up sphinx 2 on travis, since we don't pin the version in the [travis-36-doc.yaml file](https://github.com/pandas-dev/pandas/blob/a07ed594ec6a5befc967fb1b18244bbeb3bc2bf1/ci/deps/travis-36-doc.yaml#L36) | {
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} | 2 | 2019-04-12T08:33:02Z | 2019-04-12T14:36:59Z | 2019-04-12T14:36:55Z | MEMBER | null | I am testing the numpydoc master version (almost to be released as 0.9, see https://github.com/numpy/numpydoc/issues/203), and it got a bit more strict on misformatted See Also sections. This PR fixes that. | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"default"... | closed | false | null | [] | null | 3 | 2019-04-12T10:39:31Z | 2019-08-09T13:55:44Z | 2019-08-09T13:55:44Z | NONE | null | Since pandas 0.24.0 I found there is a function in ops.py named dispatch_to_series.
But dispatch_to_series is very slow, that makes my code slower.
when I use pandas==0.23.0, it cost about 100 seconds, but when I use pandas==0.24.* It cost 583 seconds.
This is my profile result.
A and I found pandas become more slower because you call isinstance function too many times
link to history merge : [[PERF] use numexpr in dispatch_to_series #22284](https://github.com/pandas-dev/pandas/pull/22284) | {
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} | 8 | 2019-04-12T11:10:34Z | 2019-04-15T12:14:33Z | 2019-04-15T12:14:25Z | MEMBER | null | I was looking at the sphinx doc build warnings, and of them was complaining about the `DataFrame/Series.timetuple` attribute. This dummy `timetuple` attribute was added in https://github.com/pandas-dev/pandas/pull/24056, to fix comparison with `datetime.datetime`.
Apart from the doc build problem (which can probably be fixed in another way as well), this also adds yet another (and for the rest useless / undocumented) attribute to our main classes.
But from a quick test (at least on linux with python 3.7, with Series) this is not needed to make it work. So removing again to see what travis/azure say.
@jbrockmendel do you remember the context of adding this? | {
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} | 5 | 2019-04-12T13:30:10Z | 2019-04-19T01:04:06Z | 2019-04-19T01:03:55Z | CONTRIBUTOR | null | - [X] closes #26045
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I added a relevant test with the example reported in the issue in `\tests\reshape\test_cut.py`.
I also tested with `grep -rn pandas -e 'np.diff(bins'` if we could have a similar situation elsewhere but it does not seem so.
~I will merge upstream & add changelog entry.~
Edit: whatsnew entry added & no need to merge master | {
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"de... | open | false | null | [] | null | 2 | 2019-04-12T15:24:56Z | 2021-07-08T07:31:23Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas
pandas.__version__
# '0.24.2'
df = pandas.DataFrame([[x // 2] for x in range(0, 10**7)], columns=['A'])
df['B'] = df['A'] + 10**15
df = df.set_index('B').sort_index()
# First access to index might be slow, so get that out of the way
df.loc[1000000000000000]
%timeit df.loc[1000000000000000]
# 71.2 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
index_name = df.index.name
df.index = df.index.values.tolist()
df.index.name = index_name
%timeit df.loc[1000000000000000]
# 422 µs ± 17 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
#### Problem description
The issue seems to occur when using a non-unique, but sorted column as an index which is the result of a previous calculation. Access to rows with .loc is much slower than it could be. When all index values are copied into a python list and then reinerted into the index, performance increases by a large factor, although the DataFrames before and after look exactly the same.
For the first try, the performance depends on the size of the DataFrame as well as the size of the offset. For the second try (after copying the values to a list and back) the operation seems to take constant time. I assume that these use different indexing methods.
I have found no way to determine in advance whether the index will be affected by this and how it will perform. For a code that has to do many random access lookups in this DataFrame, the performance difference of more than 100 determines whether the code can run in reasonable time or not.
The workaround is easy enough, but to me it is bad practice to premptively do this all the time, because you don't want to determine the exact history of operations that have been done on the index.
There are different ways to 'rebuild' the index for this DataFrame, but as far as I can tell, all of them involve getting the index values into a non-pandas/numpy structure and back again. .reset_index().set_index('B') or .copy(deep=True) have no effect.
#### Expected Output
Ideally, pandas would chose the correct indexing method on set_index() or sort_index() and automatically arrange the data in for optimized access.
If this is not possible for technical reasons or would create slowdowns for other use cases, I would like to have a method to check which index method will be used and a method to optimize the data for indexing.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-142-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.24.2
pytest: 3.4.2
pip: 19.0.3
setuptools: 38.5.2
Cython: 0.27.3
numpy: 1.16.2
scipy: 1.0.0
pyarrow: 0.8.0
xarray: 0.10.1
IPython: 6.2.1
sphinx: 1.7.1
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.4
feather: None
matplotlib: 2.2.0
openpyxl: 2.5.0
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 3.8.0
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.6
pymysql: 0.8.0
psycopg2: None
jinja2: 2.8.1
s3fs: 0.1.3
fastparquet: 0.1.4
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-04-12T17:19:28Z | 2019-04-30T13:05:48Z | 2019-04-30T13:05:48Z | CONTRIBUTOR | null | Part of #25882
# Errors in pandas.core.arrays
Type annotations for the following files need to be added or corrected to remove those files from the mypy blacklist:
- pandas.core.arrays.array_.py
- pandas.core.arrays.datetimelike.py
- pandas.core.arrays.integer.py
- pandas.core.arrays.interval.py
- pandas.core.arrays.period.py
- pandas.core.arrays.timedeltas.py
I have started working on these and will make PR soon.
Detailed errors are below.
<details>
## array_.py
```
pandas/core/arrays/array_.py:232: error: Item "str" of "Union[str, Any, ExtensionDtype, None]" has no attribute "construct_array_type"
pandas/core/arrays/array_.py:232: error: Item "None" of "Union[str, Any, ExtensionDtype, None]" has no attribute "construct_array_type"
```
## datetimelike.py
```
pandas/core/arrays/datetimelike.py:367: error: "DatetimeLikeArrayMixin" has no attribute "_data"
pandas/core/arrays/datetimelike.py:482: error: Argument 1 to "len" has incompatible type "Union[int, Sequence[int], Sequence[bool], slice]"; expected "Sized"
pandas/core/arrays/datetimelike.py:486: error: Argument 1 to "len" has incompatible type "Union[int, Sequence[int], Sequence[bool], slice]"; expected "Sized"
pandas/core/arrays/datetimelike.py:487: error: Argument 1 to "len" has incompatible type "Union[int, Sequence[int], Sequence[bool], slice]"; expected "Sized"
pandas/core/arrays/datetimelike.py:503: error: Item "Tuple[Type[Any], ...]" of "Union[Type[Any], Tuple[Type[Any]]]" has no attribute "__name__"
pandas/core/arrays/datetimelike.py:505: error: "DatetimeLikeArrayMixin" has no attribute "_data"
```
## integer.py
```
pandas/core/arrays/integer.py:34: error: Incompatible types in assignment (expression has type "None", base class "ExtensionDtype" defined the type as "str")
pandas/core/arrays/integer.py:36: error: Incompatible types in assignment (expression has type "None", base class "ExtensionDtype" defined the type as "Type[Any]")
```
## interval.py
```
pandas/core/arrays/interval.py:946: error: Decorated property not supported
```
## period.py
```
pandas/core/arrays/period.py:139: error: Need type annotation for '_other_ops'
pandas/core/arrays/period.py:193: error: "PeriodDtype" has no attribute "freq"
pandas/core/arrays/period.py:280: error: Read-only property cannot override read-write property
pandas/core/arrays/period.py:550: error: Unsupported operand type for unary - ("Union[ExtensionArray, Any, Any]")
pandas/core/arrays/period.py:787: error: Incompatible types in assignment (expression has type "None", variable has type "PeriodDtype")
```
## timedeltas.py
```
pandas/core/arrays/timedeltas.py:134: error: Need type annotation for '_other_ops'
pandas/core/arrays/timedeltas.py:135: error: Need type annotation for '_bool_ops'
```
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} | 3 | 2019-04-12T18:33:16Z | 2019-04-12T21:58:15Z | 2019-04-12T21:58:10Z | CONTRIBUTOR | null | These are used by PyPI and other services.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2019-04-12T18:44:44Z | 2019-04-22T07:41:41Z | 2019-04-20T16:50:39Z | MEMBER | null | - Use ``class_without_autosummary`` template for the dtypes (similar to what `CategoricalDtype` already did) to avoid a bunch of sphinx warnings
- Therefore, explicitly list some attributes in the docstrings (for now, I went with only documenting the dtype-specific "metadata")
- And while at it, I also gave the docstrings an update (documenting the parameters, etc)
- And for doing the above, turned some metadata attributes of the dtypes into properties, so they 1) can be documented (have a docstring) b) cannot be set by the user (xref https://github.com/pandas-dev/pandas/issues/26096) | {
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] | closed | false | null | [] | null | 5 | 2019-04-12T19:05:33Z | 2021-11-19T16:31:03Z | 2020-05-08T21:00:08Z | NONE | null | Reopening issue #14530. The close description is incorrect. The JSON specification explicitly states that limits are not in the specification.
From https://tools.ietf.org/html/rfc7159#section-6
> This specification allows implementations to set limits on the range and precision of numbers accepted
The standard `json` library in python supports large numbers, meaning the language supports JSON with these values.
```
Python 3.6.8 (default, Apr 7 2019, 21:09:51)
[GCC 5.3.1 20160406 (Red Hat 5.3.1-6)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> j = """{"id": 9253674967913938907}"""
>>> json.loads(j)
{'id': 9253674967913938907}
```
> Loading a json file with large integers (> 2^32), results in "Value is too big". I have tried changing the orient to "records" and also passing in dtype={'id': numpy.dtype('uint64')}. The error is the same.
>
> ```python
> import pandas
> data = pandas.read_json('''{"id": 10254939386542155531}''')
> print(data.describe())
> ```
>
> #### Expected Output
> ```
> id
> count 1
> unique 1
> top 10254939386542155531
> freq 1
> ```
> #### Actual Output (even with dtype passed in)
> ```
> File "./parse_dispatch_table.py", line 34, in <module>
> print(pandas.read_json('''{"id": 10254939386542155531}''', dtype=dtype_conversions).describe())
> File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 234, in read_json
> date_unit).parse()
> File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 302, in parse
> self._parse_no_numpy()
> File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 519, in _parse_no_numpy
> loads(json, precise_float=self.precise_float), dtype=None)
> ValueError: Value is too big
> ```
> No problem using read_csv:
>
> ```python
> import pandas
> import io
> print(pandas.read_csv(io.StringIO('''id\n10254939386542155531''')).describe())
> ```
>
> #### Output using read_csv
> ```
> id
> count 1
> unique 1
> top 10254939386542155531
> freq 1
> ```
> #### Output of `pd.show_versions()`
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} | 1 | 2019-04-12T21:09:01Z | 2019-10-09T05:43:07Z | 2019-10-09T05:43:07Z | MEMBER | null | I've noticed recently that the xfailed part of the following test is occasionally xpassing, which is causing the test suite to fail due to being strict with xpass:
https://github.com/pandas-dev/pandas/blob/90a85e0b154833577a4023f643d8aace0c0ae7f5/pandas/tests/series/test_analytics.py#L1405-L1412
The quick fix is to update the `xfail` with `strict=False` but additional investigation into why this is occasionally passing would also be appreciated.
Examples of the xpass:
- Select Attempt # 1 here: https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=10441&view=logs
- https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=10459&view=logs&jobId=521b7dfd-2989-5ff8-bc8c-7481906480fa&taskId=07b8d9d4-6363-5e2d-bc2b-146a30521256&lineStart=64&lineEnd=70&colStart=1&colEnd=24
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} | 0 | 2019-04-12T21:29:35Z | 2021-07-09T13:15:05Z | 2021-07-09T13:15:05Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
N = 10
df = pd.DataFrame(np.random.normal(size=(10, N))+np.arange(N)*100, columns=range(N))
df.style.bar(align="mid", color=['pink', 'lightgreen'], axis=0)
```
#### Problem description
With align='mid' I would expect the middle to be the mean. Essentially, as a tool to visualize the variance.
The dataframe above contains 10 columns with random numbers. To the n's column n*100 is added. So, column-wise, I would expect pretty similar output cause the mid should be the mean of each column.
But because for columns that contain only positive or (only negative) values, the alignment is done differently. The way it is done right now you cannot see anything for e.g. column 8.
An alignment method where the mean is always used would be nice. | {
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} | 4 | 2019-04-12T22:53:35Z | 2019-04-30T19:32:00Z | 2019-04-30T13:05:48Z | CONTRIBUTOR | null | - [X] closes #26065
- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 3 | 2019-04-13T02:00:05Z | 2019-04-13T02:17:28Z | 2019-04-13T02:17:15Z | NONE | null | ```
import pandas as pd
d = {'column': {'one': 1, 'two': 2, 'three': 3}}
pd.DataFrame(d) # Or pd.DataFrame.from_dict(d, orient='columns')
```
results in:
```
column
one 1
three 3
two 2
```
instead of what we expect:
```
column
one 1
two 2
three 3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.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: 4.4.0
pip: 19.0.3
setuptools: 39.0.1
Cython: 0.28.3
numpy: 1.15.4
scipy: 1.2.1
pyarrow: 0.13.0
xarray: 0.10.0
IPython: 7.4.0
sphinx: None
patsy: 0.4.1
dateutil: 2.7.2
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.9
feather: None
matplotlib: 2.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.0
bs4: None
html5lib: 0.9999999
sqlalchemy: 1.1.10
pymysql: None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.3.0
pandas_gbq: None
pandas_datareader: None
gcsfs: 0.2.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26073 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26073/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26073/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26073/events | https://github.com/pandas-dev/pandas/issues/26073 | 432,810,115 | MDU6SXNzdWU0MzI4MTAxMTU= | 26,073 | Reading pdf tables directly using Pandas | {
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} | [] | closed | false | null | [] | null | 2 | 2019-04-13T05:31:47Z | 2019-04-15T06:15:40Z | 2019-04-13T08:47:44Z | NONE | null | Would be pretty neat if you could read tables directly from pdf into a pandas dataframe | {
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"d... | closed | false | null | [] | null | 6 | 2019-04-13T11:24:28Z | 2019-09-19T05:17:22Z | 2019-09-19T05:17:22Z | NONE | null | kwargs={'delimiter': ',', 'doublequote': True, 'escapechar': None, 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False, 'lineterminator': None, 'header': 0, 'index_col': None, 'names': None, 'skiprows': None, 'na_values': {'nan', '', 'NaN', 'N/A', '-NaN', 'null', '#N/A N/A', 'n/a', '-nan', '-1.#QNAN', 'NA', '1.#IND', '-1.#IND', '1.#QNAN', 'NULL', '#NA', '#N/A'}, 'true_values': None, 'false_values': None, 'converters': {}, 'dtype': None, 'keep_default_na': True, 'thousands': None, 'comment': None, 'decimal': b'.', 'usecols': None, 'verbose': False, 'encoding': 'gb2312', 'compression': None, 'mangle_dupe_cols': True, 'tupleize_cols': False, 'skip_blank_lines': True, 'delim_whitespace': False, 'na_filter': True, 'low_memory': True, 'memory_map': False, 'error_bad_lines': True, 'warn_bad_lines': True, 'float_precision': None, 'na_fvalues': set(), 'allow_leading_cols': True}
parsers.TextReader('中文文件名-chinese-in-filename.csv',**kwargs)
Traceback (most recent call last)
<ipython-input-6-8864dc3e024e> in <module>()
----> 1 parsers.TextReader(f,**_2)
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
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} | 5 | 2019-04-13T12:46:16Z | 2019-05-16T13:11:18Z | 2019-05-16T13:11:18Z | MEMBER | null | The doc builds sees the following failure:
```
PyperclipException:
Pyperclip could not find a copy/paste mechanism for your system.
For more information, please visit https://pyperclip.readthedocs.org
```
which is happening since the refactor of the CI envs in https://github.com/pandas-dev/pandas/pull/24632. In unifying things we might have by accident removed some clipboard related things for the doc build.
cc @saurav2608 if you want to take a look at this, always welcome
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} | 4 | 2019-04-13T13:33:53Z | 2019-04-16T07:26:05Z | 2019-04-16T07:26:01Z | MEMBER | null | Cleaning up errors / warnings in the doc build, related to the ipython code blocks:
- put a section about hdf5 and Panel in verbatim code blocks + added some `:okwarning:` for Panel (will need to be properly cleaned up when Panel is removed)
- fixed cases that raised deprecation warning for `TimedeltaIndex.fillna()` with integers
- reworded case that raised deprecation warning about passing integers + timezone to DatetimeIndex
- `pd.core.config` doesn't exist anymore -> removed that call + made the actual example verbatim | {
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} | 1 | 2019-04-13T16:22:15Z | 2020-09-23T17:14:58Z | 2020-09-23T17:14:57Z | CONTRIBUTOR | null | #### Problem description
Following up from a discussion in: https://github.com/pandas-dev/pandas/pull/25913#discussion_r272157952
Currently `set_ops` are not tested across all indexes and should probably be consolidated in a new file maybe `pandas/tests/indexes/test_setops.py` pulling tests from subdirectories and relevant tests from `pandas/tests/indexes/test_base.py` and potentially other files together.
@jreback Would be good to get your input as well. I know that @reidy-p wanted to have a look at reorganising tests as well.
I'll start looking into this and adding to this issue with an idea of how to proceed but input would be welcome. | {
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} | 5 | 2019-04-13T18:18:49Z | 2021-08-19T11:56:48Z | 2019-04-17T12:48:32Z | CONTRIBUTOR | null | - [x] closes #22305
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The problem was in contradictory behavior between `pd.Index([pd.NaT, None]).is_unique` that returns `False` and `algorithms.unique([pd.NaT, None])` that returns `array([NaT, None], dtype=object)` equivalent to the object being unique.
We are only using `Index(arg).unique()` now.
The test written by @realead was added to verify changes. | {
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- [ ] tests added / passed
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Removes ``compat.iterkeys``. | {
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} | 4 | 2019-04-14T09:49:58Z | 2019-05-08T04:47:02Z | 2019-04-16T11:47:54Z | CONTRIBUTOR | null | Currently, ``Categorical .ordered``, ``CategoricaDtype.ordered`` and similar is tested using different fixtures + parametrization in some places. This PR collects the ordered fixtures/parametrizations and places a common ``ordered`` fixture in pandas.conftest. | {
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- [x] closes #17347
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The notable change here is removing the SIX license since I think we've removed any borrowed compatibility code. | {
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Possible performance regression in https://github.com/pandas-dev/pandas/commit/a1fee9199eba7ebf423880243936b9f1501d3d3a.
Regression: http://pandas.pydata.org/speed/pandas/index.html#rolling.Quantile.time_quantile?commits=a1fee9199eba7ebf423880243936b9f1501d3d3a&p-window=1000&p-param5=%27lower%27&p-percentile=0.5&p-dtype=%27float%27&p-constructor=%27DataFrame%27
Benchmark: 15.23% rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5, 'lower')
cc @BryanCutler. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26088 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26088/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26088/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26088/events | https://github.com/pandas-dev/pandas/issues/26088 | 433,044,716 | MDU6SXNzdWU0MzMwNDQ3MTY= | 26,088 | Possible performance regression in a1fee9 | {
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Possible performance regression in https://github.com/pandas-dev/pandas/commit/a1fee9199eba7ebf423880243936b9f1501d3d3a.
Regression: http://pandas.pydata.org/speed/pandas/index.html#rolling.Quantile.time_quantile?commits=a1fee9199eba7ebf423880243936b9f1501d3d3a&p-window=1000&p-param5=%27lower%27&p-percentile=0.5&p-dtype=%27float%27&p-constructor=%27DataFrame%27
Benchmark: 15.23% rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5, 'lower')
cc @BryanCutler. | {
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} | 3 | 2019-04-14T23:49:31Z | 2020-01-16T00:34:12Z | 2019-04-17T22:00:11Z | MEMBER | null | More typing cleanups
Original failures:
```sh
pandas/core/groupby/ops.py:13: error: Module 'pandas._libs' has no attribute 'groupby'
pandas/core/groupby/ops.py:13: error: Module 'pandas._libs' has no attribute 'reduction'
pandas/core/groupby/groupby.py:22: error: Module 'pandas._libs' has no attribute 'groupby'
pandas/core/groupby/groupby.py:329: error: Need type annotation for '_apply_whitelist'
pandas/core/groupby/generic.py:220: error: Incompatible types in assignment (expression has type "Callable[[Arg(Any, 'arg'), VarArg(Any), KwArg(Any)], Any]", base class "SelectionMixin" defined the type as "Callable[[Arg(Any, 'func'), VarArg(Any), KwArg(Any)], Any]")
``` | {
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} | 4 | 2019-04-14T23:54:15Z | 2020-01-16T00:34:12Z | 2019-04-16T11:56:03Z | MEMBER | null | Removing some dead code I came across on review of the module which appears in NDFrameGroupBy but is overridden by its only public child class DataFrameGroupBy
FWIW with the removal of PanelGroupBy the NDFrameGroupBy is an unnecessary layer of inheritance and could probably be merged in with DataFrameGroupBy, though I figure that could be a separate dedicated PR to merge those if so desired | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26091 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26091/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26091/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26091/events | https://github.com/pandas-dev/pandas/pull/26091 | 433,052,982 | MDExOlB1bGxSZXF1ZXN0MjcwMzQwNzU2 | 26,091 | CLN: pylint references | {
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} | 4 | 2019-04-15T00:31:30Z | 2019-04-15T05:19:58Z | 2019-04-15T04:07:48Z | MEMBER | null | Removing leftover `pylint` references since we exclusively use `flake8` for style checking. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26092 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26092/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26092/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26092/events | https://github.com/pandas-dev/pandas/issues/26092 | 433,053,786 | MDU6SXNzdWU0MzMwNTM3ODY= | 26,092 | export a R-compatible msgpack file? | {
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Please have a look here https://stackoverflow.com/questions/55578378/how-to-exchange-msgpack-files-between-python-and-r
Would it be possible/doable to have `pandas` export a `msgpack` file that can be read in `R`?
What do you think?
Thanks! | {
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- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-04-15T05:40:43Z | 2019-04-15T17:04:05Z | 2019-04-15T12:25:29Z | MEMBER | null | Removes this accidentally committed file https://github.com/pandas-dev/pandas/pull/26079/files#diff-0e2f9eb4477870d5461d93c7d8add1ec
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} | 3 | 2019-04-15T09:12:43Z | 2019-11-12T20:51:20Z | 2019-11-12T20:51:20Z | MEMBER | null | You can currently do this:
```
In [2]: pdt = pd.PeriodDtype(freq='D')
In [3]: pdt
Out[3]: period[D]
In [4]: pdt.freq = 'F'
In [5]: pdt
...
AttributeError: 'str' object has no attribute 'freqstr'
```
I think in general it would be good to make those dtype specific attributes not settable (this is already the case for DatetimeTZDtype.unit/tz, but not yet for the above case of PeriodDtype, and also not eg for IntervalDtype.subtype). | {
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} | 26 | 2019-04-15T12:22:57Z | 2019-07-03T13:18:04Z | 2019-07-03T13:17:59Z | CONTRIBUTOR | null | - [x] closes #N/A
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Bug was found there: https://github.com/pandas-dev/pandas/pull/26043#issuecomment-483214458
Reproduction:
```python
import pandas as pd
N = 10
half = int(N / 2)
ts_string_1 = 'March 1, 2018 12:00:00+0400'
ts_string_2 = 'March 1, 2018 12:00:00+0500'
diff_offset = [ts_string_1] * half + [ts_string_2] * half
pd.to_datetime(diff_offset, cache=True)
``` | {
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} | 4 | 2019-04-15T12:47:06Z | 2019-04-15T22:22:29Z | 2019-04-15T17:09:20Z | CONTRIBUTOR | null | - [x] xref #25725
Removes ``compat.itervalues``. | {
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In [76]: from pandas.core.dtypes.common import is_categorical_dtype
In [77]: class Foo(object):
...: def __bool__(self):
...: raise NotImplementedError("Foo doesn't support boolness")
...:
...: def __eq__(self, other):
...: return Foo()
In [78]: is_categorical(Foo())
NotImplementedError Traceback (most recent call last)
<ipython-input-78-e619c3f62c63> in <module>
----> 1 is_categorical(Foo())
/usr/python3.7/site-packages/pandas/core/dtypes/common.py in is_categorical(arr)
289 """
290
--> 291 return isinstance(arr, ABCCategorical) or is_categorical_dtype(arr)
292
293
/usr/python3.7/site-packages/pandas/core/dtypes/common.py in is_categorical_dtype(arr_or_dtype)
600 if arr_or_dtype is None:
601 return False
--> 602 return CategoricalDtype.is_dtype(arr_or_dtype)
603
604
/usr/python3.7/site-packages/pandas/core/dtypes/base.py in is_dtype(cls, dtype)
110 return True
111 try:
--> 112 return cls.construct_from_string(dtype) is not None
113 except TypeError:
114 return False
/usr/python3.7/site-packages/pandas/core/dtypes/dtypes.py in construct_from_string(cls, string)
456 it's not possible """
457 try:
--> 458 if string == 'category':
459 return cls()
460 else:
<ipython-input-77-645d89edd55f> in __bool__(self)
1 class Foo(object):
2 def __bool__(self):
----> 3 raise NotImplementedError("Foo doesn't support boolness")
4
5 def __eq__(self, other):
NotImplementedError: Foo doesn't support boolness
```
#### Problem description
is_categorical breaks if \_\_boo\_\_ of an object returns another (or same) object for which \_\_bool\_\_ raises an exception. We return the an object of **Foo** itself in the reproducer above to demonstrate this behavior. This affects `pandas.to_datetime` and probably other functionalities as well.
I guess converting an object to datetime should not depend on it's bool'ness.
#### Expected Output
True or False
#### Output of ``pd.show_versions()``
In [79]: pandas.show_versions()
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.saim.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.4.0
pip: 10.0.1
setuptools: 39.0.1
Cython: 0.29.5
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 2.0.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2017.3
blosc: 1.8.1
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 3.6.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.5+4.saim
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 2 | 2019-04-15T13:52:25Z | 2019-04-22T15:31:40Z | 2019-04-22T15:31:40Z | NONE | null | #21528 improved the documentation for assert_almost_equal, but this is actually an undocumented function (not listed in the Pandas docs) and the same issue seems to apply to both `assert_series_equal` and `assert_frame_equal`, which are documented.
Could we apply the same change as #21580 to those methods too, i.e. add the following text:
> When comparing two numbers, if the first number has magnitude less than 1e-5, we compare the two numbers directly and check whether they are equivalent within the specified precision. Otherwise, we compare the **ratio** of the second number to the first number and check whether it is equivalent to 1 within the specified precision. | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 6 | 2019-04-15T21:20:55Z | 2019-04-22T15:54:34Z | 2019-04-20T18:33:34Z | CONTRIBUTOR | null | pandas-gbq 0.10.0 adds a new `use_bqstorage_api` parameter to speed up downloads of large dataframes.
- [ ] ~closes #xxxx~
- [x] tests added / passed
```
$ pytest pandas/tests/io/test_gbq.py
=============================== test session starts ================================
platform darwin -- Python 3.6.4, pytest-4.4.1, py-1.5.3, pluggy-0.9.0
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/Users/swast/src/pandas/pandas/.hypothesis/examples')
rootdir: /Users/swast/src/pandas/pandas, inifile: setup.cfg
plugins: xdist-1.22.2, forked-0.2, cov-2.5.1, hypothesis-3.70.3
collected 1 item
pandas/tests/io/test_gbq.py . [100%]
============================= 1 passed in 9.84 seconds =============================
```
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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```python
import pandas as pd
df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5], 'C': [6, 7, 8]})
df = df.set_index(['A', 'B'])
c = df.C
# series with multiindex and two rows:
#
c[:2].groupby(['A', 'B']).sum()
#
# expected output
# A B
# 0 3 6
# 1 4 7
# Name: C, dtype: int64
#
# actual output:
# A 6
# B 7
# Name: C, dtype: int64
# series with three rows:
#
c.groupby(['A', 'B']).sum()
#
# expected and actual output:
# A B
# 0 3 6
# 1 4 7
# 2 5 8
# Name: C, dtype: int64
# as a dataframe with two rows and one column:
#
df[:2].groupby(['A', 'B']).sum().C
# expected and actual output:
# A B
# 0 3 6
# 1 4 7
# Name: C, dtype: int64
```
#### Problem description
When doing a `groupby` and aggregation on a multi-index Series, if the length of the series is two, the index of the output is incorrect.
The behavior is unexpected and inconsistent with other operations that would be expected to produce the same output:
- for a multi-index DataFrame of two rows and a single column (`C`), performing a `groupby`, aggregation and returning the column (`C`) as a series should be the same as performing the `groupby` and aggregation on that Series (`C`).
- performing the `groupby` and aggregation on a multi-index series with at least three rows yields the expected output and is consistent with a single column DataFrame.
#### Expected Output
```python
# c[:2].groupby(['A', 'B']).sum()
#
A B
0 3 6
1 4 7
Name: C, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.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: 19.0.3
setuptools: 41.0.0
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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} | 3 | 2019-04-16T10:58:30Z | 2019-04-22T15:31:48Z | 2019-04-22T15:31:40Z | CONTRIBUTOR | null | - [x] closes #26101
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 3 | 2019-04-16T13:17:13Z | 2020-08-29T11:47:48Z | 2020-08-29T11:47:48Z | NONE | null | ```python
In [37]: import pandas as pd
...: import numpy as np
...: from pandas.core.indexes.datetimes import DatetimeIndex
...:
...: data = np.ma.array(data=DatetimeIndex(['2011-01-01']))
...: s = pd.Series(data=data)
...: s.values # FAILS
OUTPUT:
TypeError Traceback (most recent call last)
<ipython-input-37-b6f50d56e315> in <module>
5 data = np.ma.array(data=DatetimeIndex(['2011-01-01']))
6 s = pd.Series(data=data)
----> 7 s.values # FAILS
/home/user1/python3.7/site-packages/pandas/core/series.py in values(self)
472 '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
473 """
--> 474 return self._data.external_values()
475
476 @property
/home/user1/python3.7/site-packages/pandas/core/internals/managers.py in external_values(self)
1545
1546 def external_values(self):
-> 1547 return self._block.external_values()
1548
1549 def internal_values(self):
/home/user1/python3.7/site-packages/pandas/core/internals/blocks.py in external_values(self)
2234
2235 def external_values(self):
-> 2236 return np.asarray(self.values.astype('datetime64[ns]', copy=False))
2237
2238
TypeError: astype() got an unexpected keyword argument 'copy'
```
#### Problem description
This breaks with **0.24.2** but not with **0.23.4**.
#### Expected Output
masked_array(data=['2011-01-01T00:00:00.000000000'],
mask=False,
fill_value=numpy.datetime64('NaT'),
dtype='datetime64[ns]')
OR
['2011-01-01T00:00:00.000000000']
#### Output of ``pd.show_versions()``
<details>
In [38]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.rsaim.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.4.0
pip: 10.0.1
setuptools: 39.0.1
Cython: 0.29.5
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 2.0.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2017.3
blosc: 1.8.1
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 3.6.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.5+4.rsaim
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | open | false | null | [] | null | 8 | 2019-04-16T13:27:07Z | 2020-03-08T18:24:17Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Method used to set index of data frame (defined for make time-memory recording possible)
def set_index(data_frame: pd.DataFrame, index_cols: list):
time_res = %timeit -n 1 -r 1 -o data_frame.set_index(index_cols)
return time_res
# Method that appends N-integers columns and call set_index while recording its memory usage
def insert_cols_and_index(data_frame: pd.DataFrame, col_number: int, values: np.ndarray):
idf = data_frame.copy()
index_cols = [None] * col_number
for i in range(col_number):
current_col_name = str(i)
index_cols[i] = current_col_name
idf[current_col_name] = values
# Record memory usage and get spent time on indexing
return memory_usage((set_index, (idf, index_cols)), interval=1e-7, retval=True)
# Values we'll use as new columns content
df_size = int(1e7)
unique_len = int(1e7)
int_array = np.tile(np.arange(unique_len), df_size // unique_len)
# Create an empty pandas data frame
df = pd.DataFrame()
# Make iterative add of integer columns
# Set index on all data frame columns and record memory usage peaks
max_col_num = 14
min_mem_usages = np.zeros(max_col_num)
max_mem_usages = np.zeros(max_col_num)
time_usages = np.zeros(max_col_num)
for i in range(max_col_num):
print("{0} iteration(s)".format(i+1))
mem, time = insert_cols_and_index(df, i + 1, int_array)
min_mem_usages[i] = min(mem)
max_mem_usages[i] = max(mem)
time_usages[i] = time.average
np.save("min_mem_usages_" + pd.__version__ + "_" + str(unique_len), min_mem_usages)
np.save("max_mem_usages_" + pd.__version__ + "_" + str(unique_len), max_mem_usages)
np.save("time_usages_" + pd.__version__ + "_" + str(unique_len), time_usages)
```
#### Problem description
For a bit of context : I'm working on a mid-range ETL software based on pandas and recently, we decided to upgrade our pandas version to the latest one : 0.24.2.
We're working with 0.20.3 version and since we've passed to the latest, set_index method seems to be responsible of big performance losses on our soft.
Setting index now takes ~2 times as long compared with the older version and use more memory.
After making some research about indexing changes between these two versions, I've found that indexing in pandas has changed from the 0.23 version : #19074
I've made a bunch of tests to understand what could be the cause of this overhead.
One type of tests i've made shows a big difference between the two version.
For this test, I've used a data frame of 1e7 size. This test consisting of iteratively adding a new integer column to the data frame and then, trying to call set_index method with every columns as index cols.
The code is expressed above. I've made some variations to it by changing the ratio : number of unique values / size of the column.
For each set_index call, i've recorded min and max memory usage and spent time on indexing.
First, I've concluded that the time and memory usage difference between these two versions becomes larger when we want to index on many columns.
```python
df_size = int(1e7)
unique_len = int(1e7)
int_array = np.tile(np.arange(unique_len), df_size // unique_len)
```
Even if this test is non-representative, we clearly see that the difference increases with the number of columns we're indexing on.
This may be also caused by :
- the number of combinations of unique values along index levels (product of length of unique values for each column)
- the number of unique values for each index level (sum of length of unique values for each column)
If I reduce the number of unique values to 100, memory usage peaks and spent time (for the two versions) are :
```python
df_size = int(1e7)
unique_len = int(1e2)
int_array = np.tile(np.arange(unique_len), df_size // unique_len)
```
The above plot shows that after some point, pandas seems to change its indexing method. I'm not sure about that but it seems to be.
So, i've tried with a more representative case according to our use cases (but always with integer columns) :
```python
df_size = int(1e7)
unique_len = int(1e4)
int_array = np.tile(np.arange(unique_len), df_size // unique_len)
```
Same pattern for memory usages and spent time.
According to my tests, indexing in newer pandas version takes a lot of memory compared to the older version and takes more time to do the same thing. Even if recording memory could affect performance, difference is still visible.
This difference seems growing with how long the unique values set is for each column.
Indexing is a functionality we use a lot where i'm working and we often put 15-30 columns in the index and just a few in columns (1-5). Some of our columns we're indexing on may contain more than 10e4 unique values.
Maybe, it is not a good practice to index on so many columns, but it is really convenient to represent and explain data in high dimensions. Selections in data frame are also faster. Maybe, we're doing wrong ?
Thanks in advance for your help or your suggestions.
All tests were made in two exactly environnements except for the pandas version.
I put pd.show_versions() output of this two environnements in 'details' section.
For information, is used numpy+mkl 1.16.2 got from :
https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS (0.24.2)
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 2008ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, 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: 1.1.0
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.2
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
INSTALLED VERSIONS (0.20.3)
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 2008ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.20.3
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.1.0
xarray: None
IPython: 7.4.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.3.2
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_gbq: None
pandas_datareader: None
None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26109 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26109/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26109/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26109/events | https://github.com/pandas-dev/pandas/issues/26109 | 433,809,682 | MDU6SXNzdWU0MzM4MDk2ODI= | 26,109 | Constructing PeriodIndex with string data | {
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} | 3 | 2019-04-16T14:24:18Z | 2020-04-10T17:29:56Z | 2020-04-10T17:29:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# quarters as string representation
quarters = pd.Series(["2018Q1", "2018Q2", "2018Q1", "2018Q3"])
# gives TypeError (see details for stack trace)
pi = pd.PeriodIndex(quarters, freq="Q")
TypeError: Incorrect dtype
# works as intended
pi = pd.PeriodIndex(quarters.values, freq="Q")
```
#### Problem description
Creating a PeriodIndex with string data fails when using a pandas.Series but works if using the underlying np.array.
Is this expected behavior?
#### Output of stack trace
<details>
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-1d88474891ea> in <module>
----> 1 pi = pd.PeriodIndex(quarters, freq="Q")
/usr/local/anaconda3/envs/PCS/lib/python3.7/site-packages/pandas/core/indexes/period.py in __new__(cls, data, ordinal, freq, start, end, periods, tz, dtype, copy, name, **fields)
238 else:
239 # don't pass copy here, since we copy later.
--> 240 data = period_array(data=data, freq=freq)
241
242 if copy:
/usr/local/anaconda3/envs/PCS/lib/python3.7/site-packages/pandas/core/arrays/period.py in period_array(data, freq, copy)
767 return PeriodArray._from_datetime64(data, freq)
768 if isinstance(data, (ABCPeriodIndex, ABCSeries, PeriodArray)):
--> 769 return PeriodArray(data, freq)
770
771 # other iterable of some kind
/usr/local/anaconda3/envs/PCS/lib/python3.7/site-packages/pandas/core/arrays/period.py in __init__(self, values, freq, dtype, copy)
155 values = values._values
156 if not isinstance(values, type(self)):
--> 157 raise TypeError("Incorrect dtype")
158
159 elif isinstance(values, ABCPeriodIndex):
TypeError: Incorrect dtype
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.2.1
pyarrow: 0.11.1
xarray: None
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: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-04-16T15:27:46Z | 2019-05-05T06:59:30Z | 2019-04-16T17:17:17Z | CONTRIBUTOR | null | - [x] xref #25725
Removes ``compat.lfilter``. | {
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} | 1 | 2019-04-16T17:29:38Z | 2021-05-24T13:44:06Z | 2021-05-24T13:44:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'], index=pd.RangeIndex(2, name='Idx'))
# When the index has a name you get an extra header row for it
print(df.to_latex())
# \begin{tabular}{lrr}
# \toprule
# {} & a & b \\
# Idx & & \\
# \midrule
# 0 & 1 & 2 \\
# 1 & 3 & 4 \\
# \bottomrule
# \end{tabular}
# However, when you do not want the index you still get the extra header row
print(df.to_latex(index=False))
# \begin{tabular}{rr}
# \toprule
# a & b \\
# & \\
# \midrule
# 1 & 2 \\
# 3 & 4 \\
# \bottomrule
# \end{tabular}
# What I would expect is the same as if there was no index name
print(df.reset_index(drop=True).to_latex(index=False))
# \begin{tabular}{rr}
# \toprule
# a & b \\
# \midrule
# 1 & 2 \\
# 3 & 4 \\
# \bottomrule
# \end{tabular}
```
#### Problem description
As shown above, producing LaTeX tables with indices that have a name produces an extra row in the header even when `index=False`. As I see it, this is not correct, since you just get an empty extra header line, and the behavior should be the same as if the data frame did not have an index with a name.
#### Expected Output
```python
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'], index=pd.RangeIndex(2, name='Idx'))
print(df.to_latex(index=False))
# \begin{tabular}{rr}
# \toprule
# a & b \\
# \midrule
# 1 & 2 \\
# 3 & 4 \\
# \bottomrule
# \end{tabular}
```
#### 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: 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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} | 3 | 2019-04-16T21:53:06Z | 2019-04-17T12:51:16Z | 2019-04-17T12:51:13Z | CONTRIBUTOR | null | - [x] closes #23050
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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