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} | 6 | 2019-05-24T19:28:27Z | 2020-07-10T19:16:28Z | 2020-07-10T19:16:28Z | CONTRIBUTOR | null | Followup to https://github.com/pandas-dev/pandas/pull/26399
```python
In [2]: df = pd.DataFrame({"A": [1, 2, 1, 2], "B": [1, 2, 3, 4]})
In [3]: df
Out[3]:
A B
0 1 1
1 2 2
2 1 3
3 2 4
In [4]: df.agg(foo=("B", "sum"))
```
Expected Output
```python
In [13]: df.agg({"B": {"foo": "sum"}})
/Users/taugspurger/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/frame.py:6284: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version
result, how = self._aggregate(func, axis=axis, *args, **kwargs)
Out[13]:
B
foo 10
```
without the warning. Similar for `Series.agg`
```python
In [16]: df.B.agg({"foo": "sum"}) # allow foo="sum"
Out[16]:
foo 10
Name: B, dtype: int64
```
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} | 17 | 2019-05-24T19:31:13Z | 2019-06-01T14:51:33Z | 2019-06-01T14:51:27Z | CONTRIBUTOR | null | - [x] closes #26504
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR fixes issues comparison of ordered categorical with missing values evaluates to True(#26504 ). Now making comparison with missing values always gives False. The missing values could be introduced when constructing a Categorical with values that don't fall in with the categories provided. | {
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... | open | false | null | [] | null | 1 | 2019-05-24T21:27:33Z | 2021-07-10T01:51:53Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
a = pd.DataFrame({'a': [1,2,3,1,2,3], 'b': [2,1,2,1,2,1]})
print(a['a'].groupby(a['b']).apply(lambda x: x.drop_duplicates()))
a = pd.DataFrame({'a': [1,2,1,1,2,3], 'b': [2,1,2,1,2,1]})
print(a['a'].groupby(a['b']).apply(lambda x: x.drop_duplicates()))
```
Output:
```
0 1
1 2
2 3
3 1
4 2
5 3
Name: a, dtype: int64
b
1 1 2
3 1
5 3
2 0 1
4 2
Name: a, dtype: int64
```
#### Problem description
The two outputs have different schemas out when only changing the data. Expected that they would actually have the same levels of indicies. The first output should not "reset index" and completely lose the groupby context.
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} | 6 | 2019-05-24T21:43:24Z | 2019-05-26T17:57:59Z | 2019-05-26T17:57:48Z | CONTRIBUTOR | null | Bring numpy changes about emulating the behavior of python's divmod to
pandas.
- [x] closes #26500
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2019-05-24T23:59:45Z | 2019-05-29T12:40:40Z | 2019-05-29T12:40:40Z | CONTRIBUTOR | null | Part of #25882
Current errors are:
pandas\core\indexes\period.py:171: error: Incompatible types in assignment (expression has type "PeriodArray", base class "DatetimeIndexOpsMixin" defined the type as "None") | {
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} | 9 | 2019-05-25T00:10:42Z | 2019-05-29T16:44:54Z | 2019-05-29T12:40:41Z | CONTRIBUTOR | null | - [x] closes #26517
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 21 | 2019-05-25T01:58:28Z | 2019-05-30T01:28:19Z | 2019-05-30T01:27:47Z | CONTRIBUTOR | null | - [x] fixes docstring for #26506 until implementation is ready
- [x] 46585 tests passed - none added as documentation change only
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
```
$ git diff upstream/master -u -- "*.py" | flake8 --diff && echo yes
yes
```
- [x] whatsnew entry
Provides correct desciption in docstring that Interval.get_indexer using any method other than default are not yet supported
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- [1] tests added / passed
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tests : `./scripts/validate_docstrings.py --prefix=pandas.core --errors=GL01,GL02` | {
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- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Removes use of ``StringMixin`` from io.pytables and from io.stata. It's now only used in core.computation.
I haven't been able to remove this class from core.computation. If I do, I get an error about setting an attribute:
```python
# assumes that resolvers are going from outermost scope to inner
if isinstance(local_dict, Scope):
resolvers += tuple(local_dict.resolvers.maps)
self.resolvers = DeepChainMap(*resolvers)
E AttributeError: 'Scope' object has no attribute 'resolvers'
pandas\core\computation\scope.py:134: AttributeError
```
I've looked into this and can't understand why this happens. From the looks of it, I should be able to set that atribute. I'd appreciate any hint.
Because of the above problem, I've changed ``StringMixin`` to only define a ``__repr__`` and making related changes. This means that all string reps in pandas are now defined through ``__repr__``, which is better than defining them through ``__str__``. | {
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- [x] tests added / passed
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} | 3 | 2019-05-25T16:58:59Z | 2019-05-26T15:30:36Z | 2019-05-26T14:31:44Z | CONTRIBUTOR | null | - [x] xref #25725 & #26495
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Removes use of ``StringMixin`` from pandas.io StringMixin is now only used in pandas.core.computation.
I haven't been able to remove this class from core.computation. If I do, I get an error about setting an attribute:
```python
# assumes that resolvers are going from outermost scope to inner
if isinstance(local_dict, Scope):
resolvers += tuple(local_dict.resolvers.maps)
self.resolvers = DeepChainMap(*resolvers)
E AttributeError: 'Scope' object has no attribute 'resolvers'
pandas\core\computation\scope.py:134: AttributeError
```
I've looked into this and can't understand why this happens. From the looks of it, I should be able to set that atribute here, but it jusr doesn't work. I'd appreciate any hint.
I'll keep loking into removing it completely, but don't know if I'll crack that part. | {
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} | 36 | 2019-05-26T11:04:55Z | 2019-06-21T02:10:54Z | 2019-06-21T02:10:49Z | CONTRIBUTOR | null | - [X] closes #24071 and also includes requested fixes from #25324
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ref:`categorical`
or
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https://api.github.com/repos/pandas-dev/pandas/issues/26529 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26529/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26529/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26529/events | https://github.com/pandas-dev/pandas/pull/26529 | 448,561,312 | MDExOlB1bGxSZXF1ZXN0MjgyMjkyNzcw | 26,529 | Feature: Memory Layout of DataFrames ndarrays can be set/changed | {
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} | [] | closed | false | null | [] | null | 9 | 2019-05-26T12:19:13Z | 2019-05-26T23:04:13Z | 2019-05-26T23:04:13Z | NONE | null | - [ ] closes #26502
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26530 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26530/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26530/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26530/events | https://github.com/pandas-dev/pandas/issues/26530 | 448,577,375 | MDU6SXNzdWU0NDg1NzczNzU= | 26,530 | Drop by column index does not exist | {
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} | [] | closed | false | null | [] | null | 9 | 2019-05-26T15:18:17Z | 2019-05-26T19:49:31Z | 2019-05-26T17:03:16Z | CONTRIBUTOR | null | #### Code Sample
```python
>>> import pandas
>>> a = pandas.DataFrame({'a': [1,2,3], 'b': [4,5,6]})
>>> a = pandas.concat([a, a], axis=1)
>>> a
a b a b
0 1 4 1 4
1 2 5 2 5
2 3 6 3 6
>>> a.drop(0, axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/frame.py", line 3697, in drop
errors=errors)
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/generic.py", line 3111, in drop
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/generic.py", line 3162, in _drop_axis
raise KeyError('{} not found in axis'.format(labels))
KeyError: '[0] not found in axis'
```
#### Problem description
It does not seem to be an easy way to drop a column by its column index. This makes it hard to drop columns when there are duplicate column names in the DataFrame.
#### Expected Output
```
b a b
0 4 1 4
1 5 2 5
2 6 3 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-20-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: 4.4.1
pip: 19.0.3
setuptools: 40.4.3
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.13.0
xarray: None
IPython: None
sphinx: 1.8.1
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26531 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26531/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26531/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26531/events | https://github.com/pandas-dev/pandas/issues/26531 | 448,577,792 | MDU6SXNzdWU0NDg1Nzc3OTI= | 26,531 | ExtensionDtype is missing hasobject | {
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"description": null,
"id": 76812,
"name": "Enhancement",
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"description": "Extendin... | open | false | null | [] | null | 6 | 2019-05-26T15:22:56Z | 2021-07-10T01:53:37Z | null | CONTRIBUTOR | null | #### Problem description
For better compatibility with numpy dtypes, `hasobject` could be added to `ExtensionDtype`. We use it to determine if columns contain Python objects or not, but it does not work with sparse column types which are extending `ExtensionDtype`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+610.gd2beaf3c8
pytest: None
pip: 18.1
setuptools: 40.7.1
Cython: 0.29.7
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.13.0
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26532 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26532/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26532/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26532/events | https://github.com/pandas-dev/pandas/issues/26532 | 448,585,271 | MDU6SXNzdWU0NDg1ODUyNzE= | 26,532 | Merge on key idenfitied by column index is not possible | {
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} | [] | closed | false | null | [] | null | 6 | 2019-05-26T16:47:37Z | 2019-05-26T20:00:34Z | 2019-05-26T19:20:41Z | CONTRIBUTOR | null | #### Code Sample
```python
>>> import pandas
>>> a = pandas.DataFrame([[1,2,3], [4,5,6], [7, 8, 9]], columns=['a', 'b', 'a'])
>>> b = pandas.DataFrame({'a': [1,1,1], 'c': [10,11,12]})
>>> a
a b a
0 1 2 3
1 4 5 6
2 7 8 9
>>> b
a c
0 1 10
1 1 11
2 1 12
>>> b.merge(a, left_on=0, right_on=0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/frame.py", line 6929, in merge
copy=copy, indicator=indicator, validate=validate)
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 46, in merge
validate=validate)
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 534, in __init__
self.join_names) = self._get_merge_keys()
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/reshape/merge.py", line 875, in _get_merge_keys
right._get_label_or_level_values(rk))
File "/home/mitar/.virtualenv/test/lib/python3.6/site-packages/pandas/core/generic.py", line 1741, in _get_label_or_level_values
raise KeyError(key)
KeyError: 0
```
#### Problem description
If column names are not unique (which happens to us much more often than one would hope for, for example, when reading multiple CSV files and horizontally concatenating them together) merge operation cannot operate on them. If you try, you get `ValueError: The column label 'a' is not unique.` exception. Which is understandable, because it is not possible to know on which column really you want to do the merge.
But there is also no other way currently. You cannot merge by specifying the index of the column you want to do join on. I would suggest such an option/argument should be added.
#### Expected Output
```
a c b a
0 1 10 2 3
1 1 11 2 3
2 1 12 2 3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+610.gd2beaf3c8
pytest: None
pip: 18.1
setuptools: 40.7.1
Cython: 0.29.7
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.13.0
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26534 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26534/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26534/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26534/events | https://github.com/pandas-dev/pandas/pull/26534 | 448,669,838 | MDExOlB1bGxSZXF1ZXN0MjgyMzY5MDIz | 26,534 | BUG: assignment to multiple columns when some column do not exist | {
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] | closed | false | null | [] | null | 14 | 2019-05-27T05:21:08Z | 2019-11-02T03:20:23Z | 2019-10-22T02:04:40Z | CONTRIBUTOR | null | - [x] closes #13658
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
In particular, the following code now behaves correctly.
```python
import pandas as pd
df = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]})
df[['a', 'c']] = 1
print(df)
```
v0.22: error
master: column `'c'` is converted to index `-1`, which causes last column to be overwritten.
```
a b
0 1 1
1 1 1
2 1 1
```
After this PR:
```
a b c
0 1 3 1
1 1 4 1
2 1 5 1
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} | 9 | 2019-05-27T07:39:24Z | 2019-06-08T21:45:55Z | 2019-06-08T21:45:50Z | CONTRIBUTOR | null | - xref #26302
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/26536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26536/events | https://github.com/pandas-dev/pandas/issues/26536 | 448,756,635 | MDU6SXNzdWU0NDg3NTY2MzU= | 26,536 | With MacOS and PyPy installation fails: AttributeError: 'LooseVersion' object has no attribute 'version' | {
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} | 8 | 2019-05-27T09:28:44Z | 2019-06-21T16:25:32Z | 2019-06-21T16:25:32Z | NONE | null | On MacOS 10.14, PyPy3.6-7.1.1 installation fails:
```
$ pip3 install pandas
Collecting pandas
Using cached https://files.pythonhosted.org/packages/b2/4c/b6f966ac91c5670ba4ef0b0b5613b5379e3c7abdfad4e7b89a87d73bae13/pandas-0.24.2.tar.gz
ERROR: Complete output from command python setup.py egg_info:
ERROR: Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/private/var/folders/50/8j9r2pm93n15lw18936l7zkc0000gn/T/pip-install-96rmqzke/pandas/setup.py", line 438, in <module>
if python_target < '10.9' and current_system >= '10.9':
File "/Users/haoyu/.pyenv/versions/pypy3.6-7.1.1/lib-python/3/distutils/version.py", line 52, in __lt__
c = self._cmp(other)
File "/Users/haoyu/.pyenv/versions/pypy3.6-7.1.1/lib-python/3/distutils/version.py", line 335, in _cmp
if self.version == other.version:
AttributeError: 'LooseVersion' object has no attribute 'version'
```
A workaround is to install with:
`MACOSX_DEPLOYMENT_TARGET=10.14.4 pip3 install pandas`
The cause for this is that in pandas `setup.py`, `get_config_var('MACOSX_DEPLOYMENT_TARGET')` will return `None` on PyPy, while a version number like `10.9` is expected. (See https://github.com/pandas-dev/pandas/blob/master/setup.py#L453).
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} | 40 | 2019-05-27T11:49:42Z | 2019-07-19T13:19:30Z | 2019-06-16T14:30:39Z | CONTRIBUTOR | null | - [x] closes #5768 (at least the reading part, this PR does not cover writing SPSS files)
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I haven't added a test yet because I wanted to ask which test .sav file I should use (and where to put it). Also, there's no whatsnew entry yet - in which section should this entry go (and I assume it will be out for 0.25.0, so I'll have to change 0.24.3 to 0.25.0 in the docstring).
This PR adds the capability to load SPSS .sav files with `df = pd.io.read_spss("spss_file.sav")`. Currently, there are two optional arguments: `usecols` (should be self-explanatory, let me know if you don't want me to handle a simple `str`) and `categorical`, which maps to the `apply_value_formats` parameter in `read_sav`. With `categorical=True`, a categorical columns is created with the labels from the .sav file. If `False`, numbers will be used.
A few open questions:
- Which additional optional arguments should be made available? [Pyreadstat](https://github.com/Roche/pyreadstat/blob/master/pyreadstat/pyreadstat.pyx#L208-L244) has `dates_as_pandas_datetime`, `encoding`, and `user_missing` which I haven't mapped yet.
- Should the function be called `read_spss` or `read_sav`? SPSS files have the extension `sav`, but the R haven package has a function `read_spss` (which is why I'd prefer `read_spss`).
- Are there any additional meta information bits that could be used/integrated into the data frame? `pyreadstat.read_sav` returns a dataframe and meta-information separately, which I think we shouldn't do in pandas. | {
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- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26539 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26539/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26539/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26539/events | https://github.com/pandas-dev/pandas/issues/26539 | 448,947,937 | MDU6SXNzdWU0NDg5NDc5Mzc= | 26,539 | ENH: Supporting rolling aggregations of datetime data | {
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... | open | false | null | [] | null | 9 | 2019-05-27T17:33:09Z | 2021-03-02T05:34:31Z | null | NONE | null |
I have dataframe with 3 columns.The objective is to calculate rolling sum of n consecutive observations and minimum time of those n observations.
I got ValueError in following code:
```python
import pandas as pd
df = pd.DataFrame({'time': ['09:16:00', '09:17:00', '09:15:00','09:20:00',
'09:16:00','09:19:00','09:21:00','09:28:00','09:16:00',
'09:22:00','09:24:00','09:16:00'],
'value': [i for i in range(2,14)],
'date': ['2018-05-03']*12})
df.time = pd.to_datetime(df.time,format= '%H:%M:%S' ).dt.time
df.date = pd.to_datetime(df.date)
df.rolling(4).agg({'value':[sum],'time':[min]})
```
Error details:
```python-traceback
ValueError Traceback (most recent call last)
<ipython-input-17-908245138798> in <module>
7 df.time = pd.to_datetime(df.time,format= '%H:%M:%S' ).dt.time
8 df.date = pd.to_datetime(df.date)
----> 9 df.rolling(4).agg({'value':[sum],'time':[min]})
~/anaconda3/lib/python3.7/site-packages/pandas/core/window.py in aggregate(self, arg, *args, **kwargs)
1683 @Appender(_shared_docs['aggregate'])
1684 def aggregate(self, arg, *args, **kwargs):
-> 1685 return super(Rolling, self).aggregate(arg, *args, **kwargs)
1686
1687 agg = aggregate
~/anaconda3/lib/python3.7/site-packages/pandas/core/window.py in aggregate(self, arg, *args, **kwargs)
310
311 def aggregate(self, arg, *args, **kwargs):
--> 312 result, how = self._aggregate(arg, *args, **kwargs)
313 if result is None:
314 return self.apply(arg, raw=False, args=args, kwargs=kwargs)
~/anaconda3/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
496
497 try:
--> 498 result = _agg(arg, _agg_1dim)
499 except SpecificationError:
500
~/anaconda3/lib/python3.7/site-packages/pandas/core/base.py in _agg(arg, func)
447 result = compat.OrderedDict()
448 for fname, agg_how in compat.iteritems(arg):
--> 449 result[fname] = func(fname, agg_how)
450 return result
451
~/anaconda3/lib/python3.7/site-packages/pandas/core/base.py in _agg_1dim(name, how, subset)
430 raise SpecificationError("nested dictionary is ambiguous "
431 "in aggregation")
--> 432 return colg.aggregate(how, _level=(_level or 0) + 1)
433
434 def _agg_2dim(name, how):
~/anaconda3/lib/python3.7/site-packages/pandas/core/window.py in aggregate(self, arg, *args, **kwargs)
1683 @Appender(_shared_docs['aggregate'])
1684 def aggregate(self, arg, *args, **kwargs):
-> 1685 return super(Rolling, self).aggregate(arg, *args, **kwargs)
1686
1687 agg = aggregate
~/anaconda3/lib/python3.7/site-packages/pandas/core/window.py in aggregate(self, arg, *args, **kwargs)
310
311 def aggregate(self, arg, *args, **kwargs):
--> 312 result, how = self._aggregate(arg, *args, **kwargs)
313 if result is None:
314 return self.apply(arg, raw=False, args=args, kwargs=kwargs)
~/anaconda3/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
557 return self._aggregate_multiple_funcs(arg,
558 _level=_level,
--> 559 _axis=_axis), None
560 else:
561 result = None
~/anaconda3/lib/python3.7/site-packages/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _level, _axis)
615 # if we are empty
616 if not len(results):
--> 617 raise ValueError("no results")
618
619 try:
ValueError: no results
```
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} | 3 | 2019-05-27T18:01:47Z | 2019-05-27T18:44:03Z | 2019-05-27T18:44:00Z | MEMBER | null | - [x] xref #6581
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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... | open | false | null | [] | null | 0 | 2019-05-27T20:20:10Z | 2020-04-02T04:32:07Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
Holiday(
name="Health and Sports Day", # TODO: shift to July 24 in 2020 for Olympics
month=10,
day=1,
start_date=Timestamp(2000, 1, 1),
offset=DateOffset(weekday=MO(2)),
# filter=do_not_allow_year_2020 # suggested enhancement
),
```
#### Problem description
When setting up a Holiday() descriptor, some holidays do not recur every year, but they recur most years.
For example, Health and Sport Day in Japan in the year 2020 is being shifted to July 24, but this is not easily expressed with a Holiday() rule (one can't use the observance parameter because offset is also defined).
If I could define another parameter, filter, which takes a datetime-like object as an argument and returns a bool, I could then just define my own general logic to handle this case without having to break apart the general rule. (So in the example above, the routine do_not_allow_year_2020 would check whether date.year != 2020.)
I believe this parameter could be added to the Holiday.dates function without extensive changes elsewhere. And it would simplify the writing of calendar rules for a number of countries.
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```python
# I have a dataset of part2 and part5
part2 = pd.DataFrame([1,3,4,2,452,2,4,1,4,3,1,4,31,57,8,9,1,1,2,2,12,1,5,4,123,5,6,7,87])
part5 = pd.DataFrame([1,2,3,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,123,4])
```
```
part2.rolling(3).corr(part5)
```
0
--
NaN
NaN
9.819805e-01
-5.000000e-01
8.640902e-01
5.000000e-01
8.829221e-08
-2.341224e-07
0.000000e+00
2.341224e-07
2.341224e-07
2.341224e-07
0.000000e+00
0.000000e+00
0.000000e+00
-1.021577e-07
0.000000e+00
0.000000e+00
3.097148e-07
0.000000e+00
1.238859e-07
0.000000e+00
0.000000e+00
0.000000e+00
0.000000e+00
0.000000e+00
8.434557e-08
0.000000e+00
-1.231093e-07
NaN
NaN
NaN
```
p2 = part2.loc[5:15]
p5 = part5.loc[5:15]
p2.rolling(3).corr(p5)
```
0
5 NaN
6 NaN
7 -inf
8 NaN
9 inf
10 inf
11 inf
12 NaN
13 NaN
14 NaN
15 -inf
#### Problem description
part5 has some data same so the result of rolling.corr() should has some inf or nan in
but there isn't
when I cut some part of part2 and part5 to do the rolling.corr I got the different result
so i want to know that which answer is correct
and i also get some result of rolling.corr which is greater than 1 or smaller than -1 when i cut these data to do rolling.corr it become to nan or inf
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.1.3.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: zh_CN.UTF-8
pandas: 0.24.2
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 32 | 2019-05-28T06:55:14Z | 2019-05-30T01:48:43Z | 2019-05-30T01:02:01Z | MEMBER | null | Continued simplification of this module by moving towards pytest idiom. Here I have eliminated any test instance methods and replaced with fixtures
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https://api.github.com/repos/pandas-dev/pandas/issues/26544 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26544/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26544/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26544/events | https://github.com/pandas-dev/pandas/issues/26544 | 449,096,492 | MDU6SXNzdWU0NDkwOTY0OTI= | 26,544 | DataFrame.columns.slice_locs() is undocumented, ignores 'step' parameter or gives wrong result, `kind` parameter does nothing | {
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"color": "53... | open | false | null | [] | null | 6 | 2019-05-28T07:03:25Z | 2021-07-10T01:56:24Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(columns=['a','b1','c1','d1','b2','c2','d2','b3','c3','d3','e'])
# ISSUE 1: EXPECTING (2,5,8)
>>> df.columns.slice_locs(start='c1',end='c3', step=3, kind='ix')
(2, 9)
>>> df.columns.slice_locs(start='c1',end='c3', step=3, kind='loc')
(2, 9)
>>> df.columns.slice_locs(start='c1',end='c3', step=3, kind='getitem')
(2, 9)
# ISSUE 2: we supply a nonsensical 'step', get different wrong behavior
>>> df.columns.slice_locs(start='c1',end='c3', step=pd.np.nan)
(2, 7)
```
#### Problem description
1. `DataFrame.columns.slice_locs` ignores its 'step' argument and gives the wrong answer for any positive integer step. In above example we expect the specific slice `(2,5,8)` but only get the slice `(2,9)` (with the 'end' arg being off-by-one, for `.iloc[:, ...]` I suppose). b) Why do we have to manually specify the step arg anyway, it can and should be computed? If anything it should only be a hint for efficiency, not a required option. c) For step=negative or NaN we now get the wrong `(2,7)` instead of `(2,5,8)`
1. `DataFrame.columns.slice_locs` `kind` argument is totally underdocumented, just seems to be totally ignored. b) I can't understand what the 'getitem' setting is supposed to mean. If it's not needed or used, please remove it. Else please document what it's supposed to do. c) Also, since 'ix' method was deprecated, best to rename that value 'iloc'.
1. The entire `DataFrame.columns.slice_locs` function is severely underdocumented (also it's hardly mentioned anywhere in the doc and not at all in "10 Minutes to pandas"; I've been using pandas for 10 years but only just discovered it, totally by chance).
1. As a separate enhance req, `DataFrame.columns.slice_locs` could seriously do with a regex option, e.g. above would be `df.columns.slice_locs(regex='^c[0-9]')`. Let me know if you want me to raise a separate enhance req. I think it would be really neat. Here's my workaround: `tuple(df.columns.get_loc(col) for col in df.columns if re.match('^c[1-7]', col))` gives `(2,5,8)`. If you don't use `tuple` you get a generator object, which seems unwanted.
#### Expected Output
(2,5,8)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 3.8.0
pip: 19.1.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.9
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.6
xlrd: 1.0.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml: 4.2.5
bs4: 4.5.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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} | 6 | 2019-05-28T11:09:40Z | 2019-06-12T18:40:47Z | 2019-06-12T18:40:47Z | NONE | null | In my Mac, I have a Tab-Separated values file with encoding UTF-8, and the version of pandas is
0.24.2.
[74872_zh_CN_UI.txt](https://github.com/pandas-dev/pandas/files/3227255/74872_zh_CN_UI.txt)
when I use read_csv function with engine 'python' like this:
`b = pd.read_csv("/Users/GHIBLI/Documents/vmware-L10n/bert/zh_CN/74872_zh_CN_UI.tsv",
engine="python", delimiter="\t")`
` print(b.shape)`
I got (8, 1)
if with default engine:
`b = pd.read_csv("/Users/GHIBLI/Documents/vmware-L10n/bert/zh_CN/74872_zh_CN_UI.tsv",
delimiter="\t")`
` print(b.shape)`
I got (8,22)
In contrast to 'C' engine, this 'python' engine seems that is not as simple as just 'feature-complete' I think.
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} | 17 | 2019-05-28T11:11:04Z | 2019-05-31T03:40:37Z | 2019-05-31T03:40:37Z | MEMBER | null | appeared on latest push to master, and recently updated PRs.
```
=================================== FAILURES ===================================
____________ TestDataFrameConstructors.test_constructor_list_frames ____________
[gw1] linux -- Python 3.7.3 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.frame.test_constructors.TestDataFrameConstructors object at 0x7fd3adeb3278>
def test_constructor_list_frames(self):
# see gh-3243
result = DataFrame([DataFrame()])
> assert result.shape == (1, 0)
E assert (1, 1) == (1, 0)
E At index 1 diff: 1 != 0
E Use -v to get the full diff
pandas/tests/frame/test_constructors.py:119: AssertionError
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} | 8 | 2019-05-28T13:57:11Z | 2019-05-29T10:18:52Z | 2019-05-29T10:17:25Z | MEMBER | null | fix error in azure log
```
ci/setup_env.sh: line 121: [: ==: unary operator expected
```
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} | 4 | 2019-05-28T17:05:01Z | 2019-05-28T20:21:52Z | 2019-05-28T20:21:42Z | CONTRIBUTOR | null | xref #26546. It seems like that will be fixed upstream, which will require new wheels. xfailing for now.
When fixed and new wheels are available, this will fail again, but we can merge PRs in the meantime. | {
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} | 12 | 2019-05-28T17:09:30Z | 2020-05-03T09:15:22Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
Python 2.7.12
```python
## !pip2 install numexpr --user
import pandas as pd
dfc = pd.DataFrame(map(str,range(10)),columns=['colA'])
dfc.query('colA.str.contains("0")',engine="numexpr")
```
#### Problem description
Cannot use `pd.Series.str` accessor in `pd.DataFrame.query`
### Error Log
```
TypeErrorTraceback (most recent call last)
<ipython-input-10-d6daed1ce1a9> in <module>()
1 import pandas as pd
2 dfc = pd.DataFrame(map(str,range(10)),columns=['colA'])
----> 3 dfc.query('colA.str.contains("0")')
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/frame.pyc in query(self, expr, inplace, **kwargs)
3086 kwargs['level'] = kwargs.pop('level', 0) + 1
3087 kwargs['target'] = None
-> 3088 res = self.eval(expr, **kwargs)
3089
3090 try:
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/frame.pyc in eval(self, expr, inplace, **kwargs)
3201 kwargs['target'] = self
3202 kwargs['resolvers'] = kwargs.get('resolvers', ()) + tuple(resolvers)
-> 3203 return _eval(expr, inplace=inplace, **kwargs)
3204
3205 def select_dtypes(self, include=None, exclude=None):
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/computation/eval.pyc in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
297 eng = _engines[engine]
298 eng_inst = eng(parsed_expr)
--> 299 ret = eng_inst.evaluate()
300
301 if parsed_expr.assigner is None:
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/computation/engines.pyc in evaluate(self)
74
75 # make sure no names in resolvers and locals/globals clash
---> 76 res = self._evaluate()
77 return _reconstruct_object(self.result_type, res, self.aligned_axes,
78 self.expr.terms.return_type)
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/computation/engines.pyc in _evaluate(self)
120 scope = env.full_scope
121 truediv = scope['truediv']
--> 122 _check_ne_builtin_clash(self.expr)
123 return ne.evaluate(s, local_dict=scope, truediv=truediv)
124 except KeyError as e:
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/computation/engines.pyc in _check_ne_builtin_clash(expr)
29 Terms can contain
30 """
---> 31 names = expr.names
32 overlap = names & _ne_builtins
33
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/computation/expr.pyc in names(self)
770 """Get the names in an expression"""
771 if is_term(self.terms):
--> 772 return frozenset([self.terms.name])
773 return frozenset(term.name for term in com.flatten(self.terms))
774
/home/shouldsee/envs/jupyter/local/lib/python2.7/site-packages/pandas/core/generic.pyc in __hash__(self)
1814 def __hash__(self):
1815 raise TypeError('{0!r} objects are mutable, thus they cannot be'
-> 1816 ' hashed'.format(self.__class__.__name__))
1817
1818 def __iter__(self):
TypeError: 'Series' objects are mutable, thus they cannot be hashed
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-143-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.2
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: 1.8.5
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 7 | 2019-05-29T01:07:08Z | 2019-06-27T02:15:13Z | 2019-06-27T02:15:12Z | CONTRIBUTOR | null | closes #18502
closes #23743 | {
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} | 23 | 2019-05-29T01:19:38Z | 2021-05-30T22:29:55Z | 2019-06-06T19:20:19Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
ticker = 'AA'
stocks_close_df = pd.read_parquet(
'data/v4.parquet',
columns=['DateTime', 'Close', 'Ticker'],
engine='pyarrow',
filters=[('Ticker','=',ticker)]
)
# This is what the above should effect
stocks_close_df = stocks_close_df[stocks_close_df.Ticker == ticker]
```
This results in the following exception:
```python
TypeError Traceback (most recent call last)
<ipython-input-7-450ddb513430> in <module>
6 columns=['DateTime', 'Close', 'Ticker'],
7 engine='pyarrow',
8 filters=[('Ticker','=',ticker)]
9 )
10 stocks_close_df.index = stocks_close_df['DateTime']
~/anaconda3/lib/python3.6/site-packages/pandas/io/parquet.py in read_parquet(path, engine, columns, **kwargs)
280
281 impl = get_engine(engine)
282 return impl.read(path, columns=columns, **kwargs)
~/anaconda3/lib/python3.6/site-packages/pandas/io/parquet.py in read(self, path, columns, **kwargs)
127 kwargs['use_pandas_metadata'] = True
128 result = self.api.parquet.read_table(path, columns=columns,
129 **kwargs).to_pandas()
130 if should_close:
131 try:
TypeError: read_table() got an unexpected keyword argument 'filters'
```
#### Problem description
I would like to pass a `filters` argument from `pandas.read_parquet` through to the `pyarrow` engine to do filtering on partitions in Parquet files. The `pyarrow` engine has this capability, it is just a matter of passing through the `filters` argument.
From a discussion on dev@arrow.apache.org:
> But, filtering could also be done *when* reading the parquet file(s), to
> actually prevent reading everything into memory. However, this is only
> partly implemented in pyarrow at this moment. If you have a dataset
> consisting of partitioned files in nested directories (Hive like), pyarrow
> can filter on which files to read. See the "filters" keyword of
> ParquetDataset (
> https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetDataset.html).
> I am only not fully sure you can already use this through the pandas
> interface, it might be you need to use the pyarrow interface directly (in
> which case, feel free to open an issue on the pandas issue tracker).
Note that [https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetDataset.html](https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetDataset.html) takes a `filters (List[Tuple] or List[List[Tuple]] or None (default))` argument.
#### Expected Output
A filtered `pandas.DataFrame`.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-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: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: 0.13.0
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: 0.3.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 9 | 2019-05-29T05:03:47Z | 2020-12-08T10:11:54Z | 2020-12-08T10:11:54Z | MEMBER | null | Similar to the conversation in #26487 xlwt isn't maintained. If we deprecate this could open up the path to simplify our Excel writers / extensions and supported file types going forward.
I would think this is also much easier than deprecating xlrd since we have other writers already implemented. | {
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} | 3 | 2019-05-29T05:14:28Z | 2019-05-30T01:49:20Z | 2019-05-30T01:49:17Z | MEMBER | null | More cleanup... | {
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} | 1 | 2019-05-29T07:06:54Z | 2019-06-02T23:42:55Z | 2019-06-02T23:42:55Z | MEMBER | null | `pd.Series.sparse.from_coo` only accepts a coo matrix format, but when giving it different matrix type, you get a not so useful error message:
```python
In [11]: import scipy.sparse
In [12]: m = scipy.sparse.csr_matrix(np.array([[0,1],[0,0]]))
In [13]: pd.Series.sparse.from_coo(m)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-13-b31c13c3a79c> in <module>
----> 1 pd.Series.sparse.from_coo(m)
~/scipy/pandas/pandas/core/arrays/sparse.py in from_coo(cls, A, dense_index)
2015 from pandas import Series
2016
-> 2017 result = _coo_to_sparse_series(A, dense_index=dense_index,
2018 sparse_series=False)
2019 result = Series(result.array, index=result.index, copy=False)
~/scipy/pandas/pandas/core/sparse/scipy_sparse.py in _coo_to_sparse_series(A, dense_index)
122 Convert a scipy.sparse.coo_matrix to a SparseSeries.
123
--> 124 Parameters
125 ----------
126 A : scipy.sparse.coo.coo_matrix
~/miniconda3/envs/dev37/lib/python3.7/site-packages/scipy/sparse/base.py in __getattr__(self, attr)
687 return self.getnnz()
688 else:
--> 689 raise AttributeError(attr + " not found")
690
691 def transpose(self, axes=None, copy=False):
AttributeError: row not found
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} | 3 | 2019-05-29T07:23:17Z | 2019-06-30T19:26:15Z | 2019-06-30T19:26:15Z | MEMBER | null | If you are still using the SparseDataFrame class, the warnings are quite sticky:
```python
In [5]: sparse_df = pd.SparseDataFrame(np.array([[1,2, np.nan], [np.nan, 5, 6]]), columns=['a','b','c'])
/home/joris/miniconda3/envs/dev37/bin/ipython:1: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
#!/home/joris/miniconda3/envs/dev37/bin/python
In [6]: sparse_df
Out[6]: /home/joris/scipy/pandas/pandas/core/frame.py:3337: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
return klass(values, index=self.index, name=items, fastpath=True)
a b c
0 1.0 2.0 NaN
1 NaN 5.0 6.0
In [7]: sparse_df + 1
/home/joris/scipy/pandas/pandas/core/frame.py:3337: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
return klass(values, index=self.index, name=items, fastpath=True)
/home/joris/scipy/pandas/pandas/core/ops.py:2304: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
name=self.name)
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:298: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
default_fill_value=self.default_fill_value).__finalize__(self)
Out[7]:
a b c
0 2.0 3.0 NaN
1 NaN 6.0 7.0
```
So they are also raised each time you display them (repr), on each operation (like the addition I did).
I know users can filter them, but ideally they would be shown a bit less I think to not annoy users too much.
cc @TomAugspurger | {
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} | 6 | 2019-05-29T07:29:18Z | 2019-06-01T14:48:43Z | 2019-06-01T14:48:37Z | CONTRIBUTOR | null | - xref #22471
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 0 | 2019-05-29T07:35:55Z | 2019-06-19T00:41:03Z | 2019-06-19T00:41:03Z | MEMBER | null | We should properly deprecate `to_dense` and `to_sparse` on Series and DataFrame.
`Series.to_dense` already is disfunctional (https://github.com/pandas-dev/pandas/issues/25680), and `DataFrame.to_dense` can be replaced with `DataFrame.sparse.to_dense()`.
And the `to_sparse` methods return a SparseSeries / SparseDataFrame, which are deprecated (and in that sense already show some deprecation warning, but this should be done properly with their own deprecation warning and doc update).
Those could in principle be replaced with a new `DataFrame/Series.sparse.to_sparse` method? (although you can already do `astype('Sparse')`) | {
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} | 4 | 2019-05-29T09:39:08Z | 2021-07-10T01:58:53Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime
df1 = pd.DataFrame(
data = [10],
index = pd.MultiIndex.from_tuples([('Z', '2019-05-31')]),
columns= ['A']
)
df2 = pd.DataFrame(
data = [20],
index = pd.MultiIndex.from_tuples([('Z', datetime.datetime(2019,5,31))]),
columns= ['A']
)
df1.add(df2, fill_value=0)
```
#### Problem description
Two multiindexed dataframes, where the inner level is datetime-like string in the first, and actual datetime in the second.
Applying the .add function returns a dataframe with two index lines as expected (one for the string date, and the other for the datetime), but the values in the output do not match the two input frames.
```ipython
df1.add(df2, fill_value=0)
A
Z 2019-05-31 20.0
2019-05-31 20.0
```
#### Expected Output
```ipython
df1.add(df2, fill_value=0)
A
Z 2019-05-31 10.0
2019-05-31 20.0
```
OR
``` ipython
df1.add(df2, fill_value=0)
A
Z 2019-05-31 30
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 18.1
setuptools: 40.4.3
Cython: None
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.3
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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I wanted to use default string as a representation for missing values for my project. Is there a way to incorporate inside my pandas lib or implementing it in the project is the only way to do?
For example : I want to use '?' to show that my some value in my datetime type series is missing, instead of pd.NaT. Now pd.NaT will respond to addition operation but '?' won't. Can I make '?' to be treated as missing value and if yes is there a way to do inside pandas? This should also mean that <series> is retained as datetime type and not object. | {
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] | open | false | null | [] | null | 0 | 2019-05-29T11:18:47Z | 2019-11-02T21:38:59Z | null | NONE | null | #### Feature request
The post is derived from my post on SO: https://stackoverflow.com/questions/56352100
`get_dummies()` treats data frames with multi-index columns pretty clumsily: The multi-index tuple is converted into a string and the same suffix mechanism applies as with the simple-index columns.
```python
df = pd.DataFrame({('i','A'): ['a', 'b', 'a'],
('ii','B'): ['b', 'a', 'c']})
ret = pd.get_dummies(df)
print(ret)
print(type(ret.columns[0]))
# ('i','A')_a ('i','A')_b ('ii','B')_a ('ii','B')_b ('ii','B')_c
# 0 1 0 0 1 0
# 1 0 1 1 0 0
# 2 1 0 0 0 1
#
# str
```
What I would expect, however, is that `get_dummies()` (optionally) creates a new column level, rather than applying the post-fixes on the columns, which is fine for single-index headers.
```
ret = pd.get_dummies(df, ???)
print(ret)
print(type(ret.columns[0]))
# i ii
# A B
# a b a b c
# 0 1 0 0 1 0
# 1 0 1 1 0 0
# 2 1 0 0 0 1
```
Possibly, the feature can be implemented by expanding [`_make_col_name` (reshape.py)](https://github.com/pandas-dev/pandas/blob/93a67dbbb6f8adad0061eaa21a046ba44e297957/pandas/core/reshape/reshape.py#L909). | {
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} | 3 | 2019-05-29T11:20:50Z | 2019-06-01T14:45:56Z | 2019-06-01T14:45:07Z | CONTRIBUTOR | null | - [x] closes #25512
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
parsing.try_parse_year_month_day() in _attempt_YYYYMMDD() throws not only ValueError but also OverFlowError for incorrect dates. So handling of this error was added.
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} | 15 | 2019-05-29T16:26:27Z | 2019-06-11T07:51:54Z | 2019-06-06T14:28:26Z | MEMBER | null | - [ ] closes #xxxx
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I think it makes sense to provide a standard `construct_from_string` by default, instead of forcing subclasses of `ExtensionDtype` to implement it.
This way we can define a simple dtype with:
```python
class SimpleDtype(pandas.core.dtypes.dtypes.ExtensionDtype):
name = 'simple'
@property
def type(self):
return object
```
instead of:
```python
class SimpleDtype(pandas.core.dtypes.dtypes.ExtensionDtype):
name = 'simple'
@property
def type(self):
return object
@classmethod
def construct_from_string(cls, string):
if string != cls.name:
raise TypeError("Cannot construct a '{}' from '{}'".format(
cls.__name__, string))
return cls()
```
CC: @TomAugspurger | {
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} | 1 | 2019-05-29T18:12:15Z | 2019-06-02T21:09:45Z | 2019-06-02T21:09:45Z | MEMBER | null | We have a few "import gtk" imports in the clipboards part of the code base but I cannot find any information as to what these actually are, so I assume these are especially outdated given our drop of Py27 support.
I *think* this used to be a part of PyGTK, but links to that (like http://www.pygtk.org/) seem to redirect now to PyGObject which has different import machinery anyway | {
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} | 3 | 2019-05-29T18:50:34Z | 2019-11-22T23:06:05Z | null | NONE | null | #### Code Sample (copy-pastable)
```python
import pandas as pd
df1 = pd.DataFrame([["a", 1, 2, "05/29/2019"], ["a", 4, 5, "05/28/2019"], ["b", 2, 3, "05/27/2019"]], columns=["type", "num1", "num2", "date"]).assign(date=lambda df: pd.to_datetime(df["date"]))
df2 = pd.DataFrame(columns=["type", "num1", "num2", "date"]).assign(date=lambda df: pd.to_datetime(df["date"]))
groupbys = ["type", pd.Grouper(key="date", freq="1D")]
df1.groupby(groupbys).head()
df2.groupby(groupbys).head()
```
#### Problem description
The above code sample throws the following error:
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-ae6b527f8edf> in <module>
7
8 df1.groupby(groupbys).head()
----> 9 df2.groupby(groupbys).head()
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in head(self, n)
2062 """
2063 self._reset_group_selection()
-> 2064 mask = self._cumcount_array() < n
2065 return self._selected_obj[mask]
2066
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in _cumcount_array(self, ascending)
730 (though the default is sort=True) for groupby in general
731 """
--> 732 ids, _, ngroups = self.grouper.group_info
733 sorter = get_group_index_sorter(ids, ngroups)
734 ids, count = ids[sorter], len(ids)
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/ops.py in group_info(self)
250 @cache_readonly
251 def group_info(self):
--> 252 comp_ids, obs_group_ids = self._get_compressed_labels()
253
254 ngroups = len(obs_group_ids)
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/ops.py in _get_compressed_labels(self)
266
267 def _get_compressed_labels(self):
--> 268 all_labels = [ping.labels for ping in self.groupings]
269 if len(all_labels) > 1:
270 group_index = get_group_index(all_labels, self.shape,
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/ops.py in <listcomp>(.0)
266
267 def _get_compressed_labels(self):
--> 268 all_labels = [ping.labels for ping in self.groupings]
269 if len(all_labels) > 1:
270 group_index = get_group_index(all_labels, self.shape,
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/grouper.py in labels(self)
365 def labels(self):
366 if self._labels is None:
--> 367 self._make_labels()
368 return self._labels
369
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/grouper.py in _make_labels(self)
386 # we have a list of groupers
387 if isinstance(self.grouper, BaseGrouper):
--> 388 labels = self.grouper.label_info
389 uniques = self.grouper.result_index
390 else:
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~/miniconda3/envs/mbc/lib/python3.6/site-packages/pandas/core/groupby/ops.py in label_info(self)
261 labels, _, _ = self.group_info
262 if self.indexer is not None:
--> 263 sorter = np.lexsort((labels, self.indexer))
264 labels = labels[sorter]
265 return labels
ValueError: all keys need to be the same shape
```
The `pd.Grouper` object seems to be modified inside the list. This is not the behavior I expect. I can resolve this by making explicit (deep) copies of the list so that new instances of `pd.Grouper` are passed into both `groupby` methods. Like so:
```python
In [11]: import copy
In [12]: groupbys = ["type", pd.Grouper(key="date", freq="1D")]
In [13]: df1.groupby(copy.deepcopy(groupbys)).head()
Out[13]:
type num1 num2 date
0 a 1 2 2019-05-29
1 a 4 5 2019-05-28
2 b 2 3 2019-05-27
In [14]: df2.groupby(copy.deepcopy(groupbys)).head()
Out[14]:
Empty DataFrame
Columns: [type, num1, num2, date]
Index: []
```
#### Expected Output
As show above, expected output is the empty dataframe, instead of the `ValueError`. Interestingly, if I reverse the order and run the `df2.groupby` first, then run `df1.groupby`, it works fine. However, doing `df2.groupby` again throws the `ValueError`. There's definitely something in the `df1.groupby` that is modifying the `pd.Grouper`.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-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: 4.4.0
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: 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: 1.1.6
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.8.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
If this is expected behavior please let me know (we can close the issue). If this is not expected behavior, I'd love to take a crack at resolving this (any insight into the issue would be appreciated). | {
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} | 5 | 2019-05-29T19:50:55Z | 2019-06-01T17:28:14Z | 2019-06-01T17:03:59Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I've looked into ``RangeIndex`` and found that the index type creates and caches a int64 array if/when ``RangeIndex._data`` property is being called. This basically means that in many cases, a ``RangeIndex`` has the same memory consumption and the same speed as an ``Int64Index``.
This PR improves on that situation by giving ``RangeIndex`` custom ``.get_loc`` and ``._format_with_header`` methods. This avoids the calls to ``._data`` in some cases, which helps on the speed and memory consumption (see performance improvements below). There are probably other case where ``RangeIndex._data`` can be avoided, which I'll investigate over the coming days.
```python
>>> %timeit pd.RangeIndex(1_000_000).get_loc(900_000)
8.95 ms ± 485 µs per loop # master
4.31 µs ± 303 ns per loop # this PR
>>> rng = pd.RangeIndex(1_000_000)
>>> %timeit rng.get_loc(900_000)
17.3 µs ± 392 ns per loop # master
547 ns ± 8.26 ns per loop # this PR. get_loc is now lightningly fast
>>> df = pd.DataFrame({'a': range(1_000_000)})
>>> %timeit df.loc[800_000: 900_000]
132 µs ± 5.79 µs per loop # master
89 µs ± 2.95 µs per loop # this PR
``` | {
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} | 4 | 2019-05-29T19:51:32Z | 2019-06-10T21:55:09Z | 2019-06-10T21:55:09Z | MEMBER | null | This is currently valid code:
```python
>>> xl = pd.ExcelFile('foo.xlsx', engine='xlrd')
>>> pd.read_excel(xl, engine='openpyxl')
Empty DataFrame
Columns: []
Index: []
```
Though ambiguous as to which engine is actually being used. I think we should be raising if an engine is specified in `read_excel` when the target is a `pd.ExcelFile`, as the latter is a lower level construct and will already have an engine in place.
@simonjayhawkins | {
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} | 1 | 2019-05-29T20:43:10Z | 2019-05-29T21:25:40Z | 2019-05-29T21:25:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame([1,2,3,4,5])
df.replace([], 99, inplace=False)
```
#### Problem description
The current output is a recursion error. It is throwing an exception which then goes into an infinite exception loop (``During handling of the above exception, another exception occurred``), which finally breaks due to maximum recursion depth reached:
#### Expected Output
The expected output of the code is to replace nothing with 99. I am running into this issue when running through a list of lists and so it is unknown whether a specific list will be empty or not.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-1032-gcp
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: 0.23.3
pytest: 4.5.0
pip: 18.1
setuptools: 38.4.0
Cython: 0.29.7
numpy: 1.12.1
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.4
feather: None
matplotlib: 2.1.0
openpyxl: 2.5.0b1
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: 4.1.1
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 2 | 2019-05-29T21:29:52Z | 2021-07-23T08:52:25Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
Setup:
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.25.0.dev0+620.ga91da0c94e'
In [2]: df = pd.DataFrame([['a', 'x', 1], ['a', 'y', 2],
...: ['b', 'y', 3], ['b', 'z', 4]])
In [3]: df
Out[3]:
0 1 2
0 a x 1
1 a y 2
2 b y 3
3 b z 4
```
When `index=0` and `columns=1` the `'All'` row has zeros instead of the sums:
```python
In [4]: df.pivot_table(index=0, columns=1, values=2,
...: aggfunc="sum", fill_value=0, margins=True)
Out[4]:
1 x y z All
0
a 1 2 0 3
b 0 3 4 7
All 0 0 0 10
```
If the `index` parameter is not 0 or -1 an `IndexError` is raised, but only if `margins=True`:
```python
In [5]: df.columns = [10, 20, 30]
...: df.pivot_table(index=10, columns=20, values=30,
...: aggfunc="sum", fill_value=0, margins=True)
---------------------------------------------------------------------------
IndexError: Too many levels: Index has only 1 level, not 11
In [6]: df.pivot_table(index=10, columns=20, values=30,
...: aggfunc="sum", fill_value=0)
Out[6]:
20 x y z
10
a 1 2 0
b 0 3 4
```
This works fine for non-numeric columns:
```python
In [7]: df.columns = ['foo', 'bar', 'baz']
...: df.pivot_table(index='foo', columns='bar', values='baz',
...: aggfunc="sum", fill_value=0, margins=True)
Out[7]:
bar x y z All
foo
a 1 2 0 3
b 0 3 4 7
All 1 5 4 10
```
It also works fine if `index=0` and the `columns` parameter is a numeric value other than 1:
```python
In [8]: df.columns = [0, 2, 1]
...: df.pivot_table(index=0, columns=2, values=1,
...: aggfunc="sum", fill_value=0, margins=True)
Out[8]:
2 x y z All
0
a 1 2 0 3
b 0 3 4 7
All 1 5 4 10
```
#### Problem description
The output of `pivot_table` with `margins=True` is inconsistent for numeric column names
#### Expected Output
I'd expect the output to be consistent with `Out[7]` / `Out[8]`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: a91da0c94e541217865cdf52b9f6ea694f0493d3
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+620.ga91da0c94e
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.6.3
Cython: 0.28.2
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.6.0
xarray: 0.9.6
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml.etree: 3.8.0
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: 0.1.5
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | [] | closed | false | null | [] | null | 9 | 2019-05-29T23:38:40Z | 2019-06-09T15:53:51Z | 2019-06-09T15:53:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
from sqlalchemy import create_engine
engine = sqlalchemy.create_engine("sqlite:///databasefile.db")
dataframe.to_sql("CS_table", engine, chunksize = 100)
```
#### Problem description
I created a large database in Pandas, about 6 million rows of text data. I wanted to save this as a SQL database file, but when I try to save it, I get an out of memory RAM error. I even reduced the chuck size to 100 and it still crashes.
However, if I just have smaller version of that dataframe with 100,000 rows, and save it to a database with no chucksize specified, I have no issues saving tha dataframe.
My understanding was that since it's only processing 100 rows at a time, the RAM usage should reflect that of a save of 100 rows. Is there something else happening behind the scenes? Perhaps multi-threading?
Before I run this code, I am using 4.8 GB RAM, out of the 12.8 GB RAM available in Google Colab. Running the above code eats up all the RAM until the enviroment crashes.
I would like to be able to save my pandas dataframe to a SQL file without my environment crashing. The environment I am in is Google Colab. The pandas datafame is 2 columns, ~6 million rows. Each cell contains about this much text:
> "The dominant sequence transduction models are based on complex
> recurrent or convolutional neural networks in an encoder-decoder
> configuration. The best performing models also connect the encoder and
> decoder through an attention mechanism. We propose a new simple
> network architecture, the Transformer, based solely on attention
> mechanisms, dispensing with recurrence and convolutions entirely.
> Experiments on two machine translation tasks show these models to be
> superior in quality while being more parallelizable and requiring
> significantly less time to train. Our model achieves 28.4 BLEU on the
> WMT 2014 English-to-German translation task, improving over the
> existing best results, including ensembles by over 2 BLEU. On the WMT
> 2014 English-to-French translation task, our model establishes a new
> single-model state-of-the-art BLEU score of 41.8 after training for
> 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer
> generalizes well to other tasks by applying it successfully to English
> constituency parsing both with large and limited training data."
I posted a SO issue here
https://stackoverflow.com/questions/56369565/large-6-million-rows-pandas-df-causes-memory-error-with-to-sql-when-chunksi
And one of the users said
From stepping through the code I think it's this line, which reads creates a bunch of DataFrames
https://github.com/pandas-dev/pandas/blob/a91da0c94e541217865cdf52b9f6ea694f0493d3/pandas/io/sql.py#L676
`chunk_iter = zip(*[arr[start_i:end_i] for arr in data_list])`
Which looks like it's probably a bug. Specifically this happens prior to database insertion, in preparation.
#### Expected Output
The code should be executed without a crash.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.79+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.6.4
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.3.0
pyarrow: 0.13.0
xarray: 0.11.3
IPython: 5.5.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.5.3
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 3.0.3
openpyxl: 2.5.9
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.6
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: 0.2.1
fastparquet: None
pandas_gbq: 0.4.1
pandas_datareader: 0.7.0
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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] | closed | false | null | [] | null | 1 | 2019-05-30T12:54:14Z | 2019-05-30T14:49:32Z | 2019-05-30T14:49:32Z | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame([{}, {}, {}])
df.to_dict(orient='records')
# Output: []
```
#### Problem description
The `DataFrame.to_dict` method is inconsistent when there are no columns and `orient='records'`.
I think that this is a recent regression, since it was not a problem on pandas 0.22.0.
Searching through previous issues I see it might be related to a change in version 0.24.0.
I expect pandas to return a list of three empty dictionaries here: `[{}, {}, {}]`.
That way, for example, doing a `from_dict` followed by a `to_dict` will round trip and vice versa. E.g.
```python
pd.DataFrame.from_dict([{}, {}, {}], orient='records').to_dict(orient='records') == [{}, {}, {}]
# Output: False (I expected True)
```
Also, things like the length will be consistent:
```python
df = pd.DataFrame([{}, {}, {}])
len(df)
# Output: 3
len(df.to_dict(orient='records'))
# Output: 0
```
#### Expected Output
See "Problem Description"
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: 3.3.2
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 1.4.8
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.9
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10.1
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/26573 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26573/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26573/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26573/events | https://github.com/pandas-dev/pandas/issues/26573 | 450,308,994 | MDU6SXNzdWU0NTAzMDg5OTQ= | 26,573 | Unexpected pd.concat/reindex_axis behaviour for MultiIndexed dataframes with > 10,000 rows | {
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] | closed | false | null | [] | null | 2 | 2019-05-30T13:30:26Z | 2019-08-21T16:31:33Z | 2019-08-21T16:31:33Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
Sadly I am having troubles creating a small reproducible example of this issue, as the problem seems to disappear if I pickle the dataframes and re-load them. I can only see what is happening in a pdb session, and for large MultiIndexed dataframes, which makes them hard to analyze.
Given two dataframes, ```a``` and ```b```, with identical indices and different columns, and a unique level for each row called ```unique_level```:
```python
small_slice_idx = a.index.get_level_values('unique_level').tolist()[:10000]
big_slice_idx = a.index.get_level_values('unique_level').tolist()[:10001]
right = pd.concat([a.loc[a.index.get_level_values('unique_level').isin(small_slice_idx)], b.loc[b.index.get_level_values('unique_level').isin(small_slice_idx)]], axis=1)
wrong = pd.concat([a.loc[a.index.get_level_values('unique_level').isin(big_slice_idx)], b.loc[b.index.get_level_values('unique_level').isin(big_slice_idx)]], axis=1)
```
#### Problem description
Whenever I concatenate two MultiIndexed dataframes with over 10,000 rows (10 levels in index, 1 level in columns), the dataframes are merged correctly (expected shape), but the columns of the second dataframe are transformed to NaNs.
I noticed that if I slice the dataframes, as shown above (I do not use iloc as they are not ordered), to less or equal than 10,000 rows, this does not happen. That is the result of the ```right``` dataframe, while the issue appears again for larger dataframes, like ```wrong```.
I noticed the same issue with the ```reindex_axis```, e.g.:
```python
wrong = a.reindex_axis(b.index)
right = a.iloc[:10000].reindex_axis(b.index)
```
The indexes of ```a``` and ```b``` are seemingly identical, although in different order, but I have a suspicion that their underlying structure is somehow different, like for issue #20565, which causes troubles when concatenating/reindexing.
It is also very odd that the issue disappeared if I pickled and un-pickled again the dataframes.
I could not try this with the latest version of pandas as it is the result of calculations for which I need the exact version I am using, but I am open to any suggestions to find the smallest working example that I can try on the latest version.
Thanks!
#### Expected Output
Same as one for dataframe with less than 10,001 rows
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.22.0
pytest: None
pip: 19.1
setuptools: 40.6.3
Cython: 0.28.5
numpy: 1.14.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.1
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: None
xlsxwriter: 1.1.1
lxml: 4.2.5
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.6.0
</details>
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documentation is a good place to start for new contributors.
should a doc build be included in CI to ensure that the documentation builds smoothly on Windows?
if so:
- should a build failure be an allowed failure?
- should it be a separate job or included in the one of the two current windows jobs? | {
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} | 6 | 2019-05-30T14:31:41Z | 2019-05-30T19:17:06Z | 2019-05-30T19:16:58Z | CONTRIBUTOR | null | Fix benchmark suite import error on master and 0.23.
Run `asv check` on all commits to lint the benchmark suite.
Also, fixed a timeout error that appears on newer asv versions,
which have an updated asizeof.py module. | {
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df = pd.read_csv(file_path, parse_dates=['dateTime'])
df.to_csv(file_path, date_format='%Y-%m-%d %H:%M:%S',header=True,index=False)
output:
Unnamed: 0,dateTime,ID,open,high,low,close,vol,SplitAdjustment,DivAdjustment,DividendPerShare,SplitFactor
0,2002-05-30 00:00:00,500045,13.5,14.11,12.99,13.03,1663700.0,1.0,1.0,0.0,1.0
I got this version from conda today, python 3, fresh install.
this is a bummer I have several thousand files to fix. | {
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} | 3 | 2019-05-30T17:09:13Z | 2019-05-30T21:54:40Z | 2019-05-30T19:51:04Z | MEMBER | null | new error message for df.plot.line
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} | 3 | 2019-05-30T18:35:06Z | 2019-05-30T20:17:41Z | 2019-05-30T19:53:11Z | CONTRIBUTOR | null | - [x] xref #26565
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Make RangeIndex use python's ``range`` internally, rather than the three scalars ``_start``, ``_stop`` and ``_step``.
Python3's ``range`` has several nice properties, that were not available in ``xrange`` in Python2. For example it's sliceable, and has ``.index`` and ``count`` and various magic methods. These can be used in Pandas, rather than maintaining Pandas-specific code, offering cleaner code and possibly faster operations.
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} | 4 | 2019-05-30T18:57:20Z | 2020-01-16T00:34:49Z | 2019-06-05T12:59:13Z | MEMBER | null | Pretty big step towards logically partitioning tests in this module - replaced SharedItems and instance attributes with fixtures. Should make subsequent test reorg easier
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} | 5 | 2019-05-30T19:14:12Z | 2019-06-10T12:45:08Z | 2019-06-10T12:45:05Z | CONTRIBUTOR | null | ```python
In [4]: animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
...: 'height': [9.1, 6.0, 9.5, 34.0],
...: 'weight': [7.9, 7.5, 9.9, 198.0]})
...: animals.groupby("kind").height.agg(max_height='max')
Out[4]:
max_height
kind
cat 9.5
dog 34.0
```
Closes #26512
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 18 | 2019-05-30T20:37:22Z | 2019-06-05T12:59:33Z | 2019-06-05T12:50:34Z | CONTRIBUTOR | null | - [x] xref #26565
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Make RangeIndex use python's ``range`` internally, rather than the three scalars ``_start``, ``_stop`` and ``_step``.
Python3's ``range`` has several nice properties, that were not available in ``xrange`` in Python2. For example it's sliceable, and has ``.index`` and ``count`` methods. These can be used in Pandas, rather than maintaining Pandas-specific code, offering cleaner code and possibly faster operations.
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"d... | closed | false | null | [] | null | 2 | 2019-05-30T23:18:40Z | 2019-06-09T18:21:52Z | 2019-06-09T18:21:42Z | MEMBER | null | The allowed failures on Travis is currently failing on TestDataFramePlots.test_subplots
the matplotlib version used has changed today from 3.0.3 to 3.1.0
#### Code Sample, a copy-pastable example if possible
```python
import matplotlib
print(matplotlib.__version__)
import numpy as np
import string
from pandas import DataFrame
df = DataFrame(np.random.rand(10, 3),
index=list(string.ascii_letters[:10]))
axes = df.plot(kind='bar', subplots=True, sharex=True, legend=True)
for ax in axes[:-2]:
collections = ax.get_xticklabels(minor=True)
print(collections)
for patch in collections:
print(patch.get_visible())
```
#### Problem description
3.1.0
<a list of 2 Text xticklabel objects>
True
True
#### Expected Output
3.0.3
<a list of 7 Text xticklabel objects>
False
False
False
False
False
False
False
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 2 | 2019-05-31T01:29:10Z | 2019-06-05T19:06:14Z | 2019-06-05T19:06:14Z | CONTRIBUTOR | null | ```python
In [1]: pd.__version__
Out[1]: '0.25.0.dev0+625.g8154efb0c'
```
```python
pd.to_datetime(20199911, format="%Y%m%d", errors='ignore')
pd.to_datetime(2019121212, format="%Y%m%d", errors='ignore')
```
throws "TypeError: 'int' object is unsliceable" instead of returning initial values
```python
Traceback (most recent call last):
File "/home/talka/projects/pandas/tmp.py", line 21, in <module>
pd.to_datetime(2019121212, format="%Y%m%d", errors='ignore')
File "/home/talka/projects/pandas/pandas/util/_decorators.py", line 188, in wrapper
return func(*args, **kwargs)
File "/home/talka/projects/pandas/pandas/core/tools/datetimes.py", line 626, in to_datetime
result = convert_listlike(np.array([arg]), box, format)[0]
File "/home/talka/projects/pandas/pandas/core/tools/datetimes.py", line 270, in _convert_listlike_datetimes
arg, format, exact=exact, errors=errors)
File "pandas/_libs/tslibs/strptime.pyx", line 149, in pandas._libs.tslibs.strptime.array_strptime
TypeError: 'int' object is unsliceable
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} | 14 | 2019-05-31T01:31:58Z | 2019-06-02T23:43:04Z | 2019-06-02T23:42:55Z | CONTRIBUTOR | null | - [x] closes #26554
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 6 | 2019-05-31T02:06:51Z | 2019-06-05T19:06:47Z | 2019-06-05T19:06:15Z | CONTRIBUTOR | null | - [x] closes #26583
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
array_strptime returned TypeError when trying to slice 'too long' integer for the given format %Y%m%d (for example 2121010101).
After parsing date in the first 8 symbols it tried to return the remaining symbols in ValueError message as a slice of integer which in turn caused TypeError.
Converted to string value is now used to make slice for that ValueError message.
In case of 20209911, it tried to parse 20209911 to datetime(2020, 9, 9) and had 11 left unparsed.
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} | 3 | 2019-05-31T02:56:57Z | 2019-05-31T03:40:37Z | 2019-05-31T03:40:37Z | CONTRIBUTOR | null | Reverts https://github.com/pandas-dev/pandas/pull/26548
xref https://github.com/numpy/numpy/pull/13663
Closes https://github.com/pandas-dev/pandas/issues/26546
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] | closed | false | null | [] | null | 3 | 2019-05-31T08:27:44Z | 2019-06-02T09:59:19Z | 2019-06-02T02:55:42Z | NONE | null | #### Code Sample
```python
import pandas as pd
print(pd.DataFrame({0:{'B':'First row','A':'Second row'}}))
print(pd.DataFrame([{'B':'First column','A':'Second column'}]))
```
Results in
```
0
A Second row
B First row
A B
0 Second column First column
```
#### Problem description
The two constructor calls above reorder the rows or columns, respectively, alphabetically, so the row or column, respectively, indexed by 'A' always comes first.
#### Expected Output
The order of the dict should be preserved now that Python dictionaries are officially ordered.
Hence, the row or column, respectively, indexed by 'A' should come second.
I don't need to make an argument for this, because pandas itself has already decided so:
https://pandas.pydata.org/pandas-docs/version/0.23.0/whatsnew.html#whatsnew-0230-api-breaking-dict-insertion-order
#### Versions
Tested with pandas 0.24.2 and Python 3.7
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} | 12 | 2019-05-31T08:31:49Z | 2019-06-09T03:12:26Z | 2019-06-08T21:55:31Z | CONTRIBUTOR | null | - [x] closes #26453
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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https://travis-ci.org/MacPython/pandas-wheels/builds/539541484 (the builds)
just started failing
cc @pandas-dev/pandas-core | {
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} | 5 | 2019-05-31T12:11:28Z | 2019-06-02T21:09:53Z | 2019-06-02T21:09:45Z | CONTRIBUTOR | null | - [x] closes #26563
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 7 | 2019-05-31T12:36:54Z | 2019-06-02T15:29:18Z | 2019-06-01T14:12:41Z | MEMBER | null | - [X] xref #22766, #26574
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/26592 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26592/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26592/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26592/events | https://github.com/pandas-dev/pandas/issues/26592 | 450,819,729 | MDU6SXNzdWU0NTA4MTk3Mjk= | 26,592 | Exception while MultiIndex slice contains "min", "mean" or "max" | {
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} | [] | closed | false | null | [] | null | 5 | 2019-05-31T14:13:09Z | 2019-06-03T12:07:02Z | 2019-06-03T12:07:02Z | NONE | null | I want to slice the dataframe contains MultiIndex
```python
import pandas as pd
stages = ("a", "b", "c")
stats = ("s0", "s1", "s2")
index = pd.MultiIndex.from_product([stages, stats], names=("lev0", "lev1"))
df = pd.DataFrame(index=index, columns=["f0", "f1"])
df
```
Let's slicing
```python
df.loc[("b", "s2"):("c", "s1")]
```
Let's change the ``stats`` value and recreate the dataframe:
```python
stats = ("min", "mean", "max")
index = pd.MultiIndex.from_product([stages, stats], names=("lev0", "lev1"))
df = pd.DataFrame(index=index, columns=["f0", "f1"])
df
```
and the similar slicing
```python
df.loc[("b", "max"):("c", "mean")]
```
raises ``UnsortedIndexError``:
<details>
```
---------------------------------------------------------------------------
UnsortedIndexError Traceback (most recent call last)
<ipython-input-68-21fd12d039a9> in <module>
----> 1 df.loc[("b", "max"):("c", "mean")]
c:\opt\miniconda3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1498
1499 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1500 return self._getitem_axis(maybe_callable, axis=axis)
1501
1502 def _is_scalar_access(self, key):
c:\opt\miniconda3\lib\site-packages\pandas\core\indexing.py in _getitem_axis(self, key, axis)
1865 if isinstance(key, slice):
1866 self._validate_key(key, axis)
-> 1867 return self._get_slice_axis(key, axis=axis)
1868 elif com.is_bool_indexer(key):
1869 return self._getbool_axis(key, axis=axis)
c:\opt\miniconda3\lib\site-packages\pandas\core\indexing.py in _get_slice_axis(self, slice_obj, axis)
1531 labels = obj._get_axis(axis)
1532 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,
-> 1533 slice_obj.step, kind=self.name)
1534
1535 if isinstance(indexer, slice):
c:\opt\miniconda3\lib\site-packages\pandas\core\indexes\base.py in slice_indexer(self, start, end, step, kind)
4671 """
4672 start_slice, end_slice = self.slice_locs(start, end, step=step,
-> 4673 kind=kind)
4674
4675 # return a slice
c:\opt\miniconda3\lib\site-packages\pandas\core\indexes\multi.py in slice_locs(self, start, end, step, kind)
2303 # This function adds nothing to its parent implementation (the magic
2304 # happens in get_slice_bound method), but it adds meaningful doc.
-> 2305 return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
2306
2307 def _partial_tup_index(self, tup, side='left'):
c:\opt\miniconda3\lib\site-packages\pandas\core\indexes\base.py in slice_locs(self, start, end, step, kind)
4870 start_slice = None
4871 if start is not None:
-> 4872 start_slice = self.get_slice_bound(start, 'left', kind)
4873 if start_slice is None:
4874 start_slice = 0
c:\opt\miniconda3\lib\site-packages\pandas\core\indexes\multi.py in get_slice_bound(self, label, side, kind)
2247 if not isinstance(label, tuple):
2248 label = label,
-> 2249 return self._partial_tup_index(label, side=side)
2250
2251 def slice_locs(self, start=None, end=None, step=None, kind=None):
c:\opt\miniconda3\lib\site-packages\pandas\core\indexes\multi.py in _partial_tup_index(self, tup, side)
2310 'Key length (%d) was greater than MultiIndex'
2311 ' lexsort depth (%d)' %
-> 2312 (len(tup), self.lexsort_depth))
2313
2314 n = len(tup)
UnsortedIndexError: 'Key length (2) was greater than MultiIndex lexsort depth (1)'
```
</details>
My environment:
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.1
pip: 19.1
setuptools: 41.0.0
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
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} | 3 | 2019-05-31T14:22:30Z | 2019-06-03T11:16:28Z | 2019-06-01T14:09:28Z | MEMBER | null | add concrete examples of dataframe fixtures to docstrings to make it easier when looking for fixtures to reuse.
and also appear when using `pytest --fixtures` and `pytest --fixtures-per-test` so could be an aid to debugging
```
------------------ fixtures used by test_set_index_directly -------------------
----------------- (pandas\tests\frame\test_alter_axes.py:20) ------------------
add_imports
no docstring available
configure_tests
no docstring available
doctest_namespace
Fixture that returns a :py:class:`dict` that will be injected into the namespace of doctests.
float_string_frame
Fixture for DataFrame of floats and strings with index of unique strings
Columns are ['A', 'B', 'C', 'D', 'foo'].
A B C D foo
w3orJvq07g -1.594062 -1.084273 -1.252457 0.356460 bar
PeukuVdmz2 0.109855 -0.955086 -0.809485 0.409747 bar
ahp2KvwiM8 -1.533729 -0.142519 -0.154666 1.302623 bar
3WSJ7BUCGd 2.484964 0.213829 0.034778 -2.327831 bar
khdAmufk0U -0.193480 -0.743518 -0.077987 0.153646 bar
LE2DZiFlrE -0.193566 -1.343194 -0.107321 0.959978 bar
HJXSJhVn7b 0.142590 1.257603 -0.659409 -0.223844 bar
... ... ... ... ... ...
9a1Vypttgw -1.316394 1.601354 0.173596 1.213196 bar
h5d1gVFbEy 0.609475 1.106738 -0.155271 0.294630 bar
mK9LsTQG92 1.303613 0.857040 -1.019153 0.369468 bar
oOLksd9gKH 0.558219 -0.134491 -0.289869 -0.951033 bar
9jgoOjKyHg 0.058270 -0.496110 -0.413212 -0.852659 bar
jZLDHclHAO 0.096298 1.267510 0.549206 -0.005235 bar
lR0nxDp1C2 -2.119350 -0.794384 0.544118 0.145849 bar
[30 rows x 5 columns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/26594 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26594/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26594/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26594/events | https://github.com/pandas-dev/pandas/issues/26594 | 450,834,396 | MDU6SXNzdWU0NTA4MzQzOTY= | 26,594 | SettingWithCopyWarning does not trigger on the second time? | {
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} | [] | closed | false | null | [] | null | 2 | 2019-05-31T14:44:03Z | 2019-05-31T23:30:25Z | 2019-05-31T23:30:25Z | CONTRIBUTOR | null | Found a false negative for the SettingWithCopyWarning that surprised me a bit:
```python
import numpy as np
import pandas as pd
def create_df():
NROWS = 10
return (pd.DataFrame({'i1': range(NROWS),
'f1': np.arange(NROWS) / NROWS})
.assign(i2=lambda df: -pd.Series(reversed(df.i1)),
f2=lambda df: -pd.Series(reversed(df.f1))))
# Warning the first time.
df = create_df()
df[['i1', 'f1']].loc[0:5] = 2000
# For the same access, on a completely new dataframe, no warning the second time??
df = create_df()
df[['i1', 'f1']].loc[0:5] = 2000
```
This is rather surprising to me. Is this a bug in the code that raises SettingWithCopyWarning? At least, it'd be nice to give an intuition in the docs on why this behavior happens. I could create a pull request for the doc, if I understood why this was happening. (Or create one for a fix, if there's a reasonably easy one.)
This is with Pandas 0.24.2, on CPython 3.7.3. | {
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] | closed | false | null | [] | null | 10 | 2019-05-31T14:44:43Z | 2019-06-26T12:17:57Z | 2019-06-26T11:21:37Z | CONTRIBUTOR | null | Testing conda 4.7.x | {
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} | 3 | 2019-05-31T16:01:11Z | 2019-06-01T15:14:11Z | 2019-06-01T00:17:54Z | CONTRIBUTOR | null | Split off from #25637 at the [request](https://github.com/pandas-dev/pandas/pull/25637#discussion_r289447555) of @gfyoung
Relevant discussions:
https://github.com/pandas-dev/pandas/pull/25637#discussion_r289042255
https://github.com/pandas-dev/pandas/pull/25637#discussion_r289441644
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} | 7 | 2019-05-31T17:45:24Z | 2019-11-08T14:45:01Z | 2019-11-08T14:45:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'a': range(6)})
df['a'].rolling(3, win_type='blackman').agg(['mean', 'std'])
```
#### Problem description
When calculating rolling aggregations with a window function, sometimes there is an error which is quite hard to understand. I think it might be that the std() aggregation is not compatible with certain window types, or something like that- but it's not clear from the error messages or documentation what is going wrong.
Here is the stack trace that I see when I run the code sample above:
<details><pre><code>
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-48-41cc0256ede1> in <module>
3 df = pd.DataFrame({'a': range(6)})
4
----> 5 print(df['a'].rolling(3, win_type='blackman').agg(['mean', 'std']))
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\pandas\core\window.py in aggregate(self, arg, *args, **kwargs)
741 @Appender(_shared_docs['aggregate'])
742 def aggregate(self, arg, *args, **kwargs):
--> 743 result, how = self._aggregate(arg, *args, **kwargs)
744 if result is None:
745
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
557 return self._aggregate_multiple_funcs(arg,
558 _level=_level,
--> 559 _axis=_axis), None
560 else:
561 result = None
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\pandas\core\base.py in _aggregate_multiple_funcs(self, arg, _level, _axis)
587 try:
588 colg = self._gotitem(obj.name, ndim=1, subset=obj)
--> 589 results.append(colg.aggregate(a))
590
591 # make sure we find a good name
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\pandas\core\window.py in aggregate(self, arg, *args, **kwargs)
741 @Appender(_shared_docs['aggregate'])
742 def aggregate(self, arg, *args, **kwargs):
--> 743 result, how = self._aggregate(arg, *args, **kwargs)
744 if result is None:
745
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
354 if isinstance(arg, compat.string_types):
355 return self._try_aggregate_string_function(arg, *args,
--> 356 **kwargs), None
357
358 if isinstance(arg, dict):
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\pandas\core\base.py in _try_aggregate_string_function(self, arg, *args, **kwargs)
321 f = getattr(np, arg, None)
322 if f is not None:
--> 323 return f(self, *args, **kwargs)
324
325 raise ValueError("{arg} is an unknown string function".format(arg=arg))
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\numpy\core\fromnumeric.py in std(a, axis, dtype, out, ddof, keepdims)
3240
3241 return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
-> 3242 **kwargs)
3243
3244
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\numpy\core\_methods.py in _std(a, axis, dtype, out, ddof, keepdims)
138 def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
139 ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
--> 140 keepdims=keepdims)
141
142 if isinstance(ret, mu.ndarray):
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\numpy\core\_methods.py in _var(a, axis, dtype, out, ddof, keepdims)
110 arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
111 else:
--> 112 arrmean = arrmean.dtype.type(arrmean / rcount)
113
114 # Compute sum of squared deviations from mean
~\AppData\Local\Continuum\anaconda3\envs\petropy\lib\site-packages\pandas\core\window.py in __getattr__(self, attr)
148
149 raise AttributeError("%r object has no attribute %r" %
--> 150 (type(self).__name__, attr))
151
152 def _dir_additions(self):
AttributeError: 'Window' object has no attribute 'dtype'
</details></pre></code>
#### Expected Output
When I run the code above without the win_type paramater, everything works fine:
```python
import pandas as pd
df = pd.DataFrame({'a': range(6)})
df['a'].rolling(3, win_type=None).agg(['mean', 'std'])
```
Result:
<pre><code>
mean std
0 NaN NaN
1 NaN NaN
2 1.0 1.0
3 2.0 1.0
4 3.0 1.0
5 4.0 1.0
</pre></code>
Thanks in advance for any tips!
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: 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: 4.3.1
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: None
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 | 2019-05-31T17:57:53Z | 2019-06-01T14:08:37Z | 2019-06-01T14:08:20Z | CONTRIBUTOR | null | Very minor benchmark cleanup now that I was on it...
These attributes appear to have been brought in by direct replacement of a
deprecated ASV attribute goal_time -> sample_time, but the two have
different semantics (the value sample_time=0.2 is 20x the default).
Increasing this from the default value is probably not necessary and
increases runtime (here 3min vs 17min for rolling.*), so it's better to restore it to defaults. | {
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} | 10 | 2019-05-31T21:26:46Z | 2021-01-26T18:17:49Z | 2020-05-12T16:10:36Z | NONE | null | Hi,
I was trying to open zipped Stata files and thought one could do it as in ``read_csv``using the ``compression='zip'`` keyword option. Is this not implemented? Saw #15644 and follow ups, but at the time it seems the discussion was about output not input files. In many cases providers give Stata files in zip format, which e.g. in my case may mean 1000's of different zipped archives containing Stata + other files. #12103 seems to have added the functionality more generally, but at least in ``pd.__version__=0.23.4`` it still complains when passing the ``compression`` keyword. I do not see the option in ``pandas/io/stata.py`` although in ``common.py`` the option seems to be there. Any pointers? I am happy to contribute if I can figure out where and how this is implemented for other formats. | {
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} | 10 | 2019-06-01T06:33:07Z | 2019-06-06T18:11:24Z | 2019-06-06T14:50:05Z | MEMBER | null | Follow-up to #26596 | {
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} | 1 | 2019-06-01T10:47:00Z | 2019-06-28T14:12:24Z | 2019-06-28T14:12:24Z | MEMBER | null | Azure Pipelines can show errors and warnings clearer, by adding some specific syntax to the log.
We are already using this for the linting, where we output flake8 errors like `##vso[task.logissue type=error;sourcepath=%(path)s;linenumber=%(row)s;columnnumber=%(col)s;code=%(code)s;]%(text)s` (see `ci/code_checks.sh`).
This makes it easier to find the errors in the logs, as they appear in the summary. See for example the `Checks_and_docs` build in https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=12078
We should be using the same for tests, and also "upload" to Azure pipelines the summary of the tests.
This is implemented in this pytest plugin: https://github.com/tonybaloney/pytest-azurepipelines
I guess the best option is to install the plugin (may be install in the azure yaml files, as the dependency is not useful outside of azure). The plugin is quite simple, if it makes sense we may prefer to implement something specific to our needs based on the plugin code: https://github.com/tonybaloney/pytest-azurepipelines/blob/master/pytest_azurepipelines.py | {
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} | 7 | 2019-06-01T14:54:46Z | 2019-06-03T08:00:35Z | 2019-06-01T15:10:11Z | MEMBER | null | master builds are failing, this temporary fixes them while we figure out why the ssh key is not working.
See: #26591
CC: @jreback
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} | 5 | 2019-06-01T15:04:51Z | 2019-06-03T11:15:20Z | 2019-06-02T23:13:09Z | MEMBER | null | this PR is basically just to promote the float_frame fixture to top-level conftest.
once there, there is opportunities to re-use by fixturising current instances of
```
df = DataFrame(tm.getSeriesData())
```
also allows removing duplicate fixture from pandas/tests/io/formats/test_to_latex.py
so this is just a move for now, fixturization in follow-on PRs | {
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} | 2 | 2019-06-01T15:15:21Z | 2019-06-01T16:09:22Z | 2019-06-01T15:34:58Z | MEMBER | null | #26591 made master builds to fail. Looks like there was a problem with the ssh keys. I regenerated them and updated them in the github deployment keys and azure pipelines secret. Updating the settings here.
CC: @jreback
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} | 3 | 2019-06-01T15:32:46Z | 2019-07-31T13:26:42Z | 2019-07-31T12:29:59Z | CONTRIBUTOR | null | Follow-up to #23167, resp dropping py2. The branch I'm readding here had to be deleted originally to pass some python2 bytes-tests. In case there is no separator, we can avoid all the list-ops and speed up cat_core by a fair bit. | {
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} | 5 | 2019-06-01T15:42:26Z | 2019-06-03T05:17:26Z | 2019-06-02T23:34:28Z | CONTRIBUTOR | null | Found an old branch lying around I had forgotten about after the numpy bump, which had been requested by @TomAugspurger in [this comment](https://github.com/pandas-dev/pandas/pull/25554#pullrequestreview-212474594)
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https://api.github.com/repos/pandas-dev/pandas/issues/26607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26607/events | https://github.com/pandas-dev/pandas/pull/26607 | 451,098,483 | MDExOlB1bGxSZXF1ZXN0Mjg0MjgwMTA0 | 26,607 | Better error for str.cat with listlike of wrong dtype. | {
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} | 4 | 2019-06-01T15:51:00Z | 2019-06-14T18:16:15Z | 2019-06-14T12:27:01Z | CONTRIBUTOR | null | - [x] closes #22722
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This had been blocked on #23167. | {
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} | 3 | 2019-06-01T15:56:52Z | 2019-06-05T22:15:17Z | 2019-06-03T11:56:30Z | CONTRIBUTOR | null | - [x] closes #23679
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Another PR that had been blocked by #23167. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26609 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26609/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26609/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26609/events | https://github.com/pandas-dev/pandas/pull/26609 | 451,100,749 | MDExOlB1bGxSZXF1ZXN0Mjg0MjgxNTM5 | 26,609 | CI: Removing doc build in azure | {
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] | closed | false | null | [] | null | 2 | 2019-06-01T16:14:38Z | 2019-06-01T17:15:20Z | 2019-06-01T16:46:56Z | MEMBER | null | Stop building the docs in azure. Beside not working, azure became unreliable, raising random errors for unrelated changes.
See: #26591 #26602 #26604
CC: @jreback (sorry for the trouble, hopefully this takes things back to normality) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26610/events | https://github.com/pandas-dev/pandas/issues/26610 | 451,118,052 | MDU6SXNzdWU0NTExMTgwNTI= | 26,610 | Read from HDF with empty `where` throws an error | {
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} | 10 | 2019-06-01T19:44:08Z | 2019-06-11T00:14:59Z | 2019-06-11T00:14:59Z | CONTRIBUTOR | null | #### Code Sample
```python
df = pd.DataFrame(np.random.rand(4,4))
where = ''
with pd.HDFStore('test.h5') as store:
store.put('df', df, 't')
store.select('df', where = where)
```
#### Problem description
Wanted to be able construct "by hands" and save `where` condition for later, so declare it as variable. But some times constructed `where` becomes empty and code throws an error.
```python-traceback
Traceback (most recent call last):
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3267, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-101-48181c3b59fb>", line 6, in <module>
store.select('df', where = where)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py", line 740, in select
return it.get_result()
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py", line 1518, in get_result
results = self.func(self.start, self.stop, where)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py", line 733, in func
columns=columns)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py", line 4254, in read
if not self.read_axes(where=where, **kwargs):
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py", line 3443, in read_axes
self.selection = Selection(self, where=where, **kwargs)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py", line 4815, in __init__
self.terms = self.generate(where)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py", line 4828, in generate
return Expr(where, queryables=q, encoding=self.table.encoding)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/core/computation/pytables.py", line 548, in __init__
self.terms = self.parse()
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 766, in parse
return self._visitor.visit(self.expr)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 331, in visit
return visitor(node, **kwargs)
File "/home/beforeflight/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 335, in visit_Module
raise SyntaxError('only a single expression is allowed')
File "<string>", line unknown
SyntaxError: only a single expression is allowed
```
#### Expected Output
When empty string is passed to `where` - just select whole DataFrame. It may be easily achieved by changing last statement to `store.select('df', where = where if where else None)`. But it would be better to add this checking inside pandas, so user may not worry about it all the times using selection from HDF with `where`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-16-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: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
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: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26611 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26611/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26611/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26611/events | https://github.com/pandas-dev/pandas/issues/26611 | 451,122,942 | MDU6SXNzdWU0NTExMjI5NDI= | 26,611 | Groupby with multiple aggregations doesn't pass through positional or keyword arguments. | {
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"id": 233160... | open | false | null | [] | null | 1 | 2019-06-01T20:45:39Z | 2021-02-28T12:58:44Z | null | CONTRIBUTOR | null | We pass positional `*args` through to the aggfunc for a single grouper.
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"A": [1, 2]})
In [3]: def f(x, y):
...: return x.sum() + y
...:
In [4]: df.groupby([0, 0]).agg(f, 10)
Out[4]:
A
0 13
```
But not for multiple.
```pytb
In [5]: df.groupby([0, 0]).agg([f], 10)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-c8273728b28e> in <module>
----> 1 df.groupby([0, 0]).agg([f], 10)
~/sandbox/pandas/pandas/core/groupby/generic.py in aggregate(self, arg, *args, **kwargs)
1354 @Appender(_shared_docs['aggregate'])
1355 def aggregate(self, arg=None, *args, **kwargs):
-> 1356 return super().aggregate(arg, *args, **kwargs)
1357
1358 agg = aggregate
~/sandbox/pandas/pandas/core/groupby/generic.py in aggregate(self, func, *args, **kwargs)
167 func = func2
168
--> 169 result, how = self._aggregate(func, _level=_level, *args, **kwargs)
170 if how is None:
171 return result
~/sandbox/pandas/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
534 return self._aggregate_multiple_funcs(arg,
535 _level=_level,
--> 536 _axis=_axis), None
537 else:
538 result = None
~/sandbox/pandas/pandas/core/base.py in _aggregate_multiple_funcs(self, arg, _level, _axis)
592 # if we are empty
593 if not len(results):
--> 594 raise ValueError("no results")
595
596 try:
ValueError: no results
```
Same for keyword arguments.
Is that intentional? It's a bit strange, since *all* your aggfuncs would need to be expecting the same positional and keyword arguments. I think we should recommend using something like functools.partial to get a unary function, and documents that args and kwargs aren't passed through for multi-agg. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26612/events | https://github.com/pandas-dev/pandas/pull/26612 | 451,136,870 | MDExOlB1bGxSZXF1ZXN0Mjg0MzA1NzY3 | 26,612 | CLN: Remove convert_objects | {
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} | 3 | 2019-06-02T00:02:51Z | 2019-06-03T15:11:03Z | 2019-06-02T23:20:16Z | MEMBER | null | - [x] xref #6581
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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