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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 4 | 2019-02-22T15:27:58Z | 2021-06-27T18:34:48Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
With 0.23.4:
```python
In [2]: df1 = pd.DataFrame(np.random.randint(0, 10, (4,3)), columns=['A', 'B', 'C'])
In [3]: df2 = pd.DataFrame(np.random.randint(0, 10, (4,3)), columns=['A', 'B', 'C'], index=pd.MultiIndex.from_product([['a', 'b'], [1, 2]]))
In [4]: pd.concat([df1, df2])
Out[4]:
A B C
0 1 3 2
1 6 6 2
2 2 6 4
3 7 5 5
(a, 1) 7 4 2
(a, 2) 1 7 3
(b, 1) 8 3 6
(b, 2) 1 2 9
In [5]: pd.concat([df1, df2], keys=[1, 2])
....
TypeError: 'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.
```
With master, the error became clearer:
```
In [3]: pd.concat([df1, df2], keys=[1, 2])
...
~/scipy/pandas/pandas/core/reshape/concat.py in _make_concat_multiindex(indexes, keys, levels, names)
589 # make sure that all of the passed indices have the same nlevels
590 if not len({idx.nlevels for idx in indexes}) == 1:
--> 591 raise AssertionError("Cannot concat indices that do"
592 " not have the same number of levels")
593
AssertionError: Cannot concat indices that do not have the same number of levels
```
But, we should not be raising AssertionErrors to users.
Further, we could maybe also say that this can work? It works in the case where no `keys` are passed, converting the mixture of single index and MultiIndex into an object dtype Index with mixed scalars and tuples. That could be extended to the `keys=` case where this mixed Index is just the second level of the final MultiIndex. | {
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} | 4 | 2019-02-22T16:13:04Z | 2019-02-23T19:47:56Z | 2019-02-23T19:47:47Z | CONTRIBUTOR | null | Added documentation to Parse Error.
- [x] closes #22881
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"color": "613... | closed | false | null | [] | null | 12 | 2019-02-22T17:42:24Z | 2019-06-27T22:12:05Z | 2019-06-27T22:12:05Z | CONTRIBUTOR | null | this builds on #22226 and #22238 by @WillAyd and the ``ArrowBoolArray`` by @TomAugspurger to create a first class bool array that is backed by a numpy EA array or a pyarrow array if available. Uses this MaskArray as the mask inside IntegerArray. The is a POC to see how feasible this is generally. Note that I did this with ``bitarray`` as well, but this is more general purpose / forward looking.
Some decent memory savings as the bits are packed by arrow.
PR:
```
In [2]: s = pd.Series(np.arange(100000), dtype='Int64')
In [3]: s.nbytes
Out[3]: 812500
In [4]: s._values._mask.nbytes
Out[4]: 12500
```
0.24.1 / master
```
In [2]: s = pd.Series(np.arange(100000), dtype='Int64')
In [3]: s.nbytes
Out[3]: 900000
In [4]: s._values._mask.nbytes
Out[4]: 100000
```
TODO:
- [x] ``pyarrow`` uses ``pandas`` as a dependency, so we have a hoop to jump to actually have this import in a sane way
- [ ] we need a ``pd.bool_`` dtype to avoid exposing the pyarrow dtypes directly (mainly because we have this as an optional dep and now BoolArray can either be ``np.bool_`` or ``arrow_bool`` so need a single type to expose to end users; this is not hard just not done).
- [x] The ``BoolArray`` allows NaN's when its an ArrowBoolArray but not for a NumpyBoolArray; I avoided this entirely but just using this as a masking array (so its guaranteed as only bool types), but eventually want to allow this
- [ ] A number of missing methods on ArrowBoolArray that we should push upstream (implemented now by simply coercing and using numpy)
- [x] ~Should just rename ``MaskArray`` to ``BoolArray`` I think.~ This is now internal only, ``BoolArray`` will use this.
cc @pandas-dev/pandas-core | {
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} | 4 | 2019-02-22T22:38:40Z | 2019-10-19T17:08:36Z | 2019-10-19T17:08:36Z | NONE | null | ```
#Under pandas==0.23.0
import pandas
df1 = pd.DataFrame([[1]], columns = [[1]], index = [ 1, 2 ])
print(df1)
df2 = pd.DataFrame([[1]], columns = [[1]], index = [ [ 1, 2 ] ])
print(df2)
Output:
1
1 1
2 1
ValueError: Shape of passed values is (1, 1), indices imply (1, 2)
#Under pandas==0.22.0, this issue did not arise.
import pandas
df1 = pd.DataFrame([[1]], columns = [[1]], index = [ 1, 2 ])
print(df1)
df2 = pd.DataFrame([[1]], columns = [[1]], index = [ [ 1, 2 ] ])
print(df2)
Output:
1
1 1
2 1
1
1 1
2 1
```
It seems that if the index is just a list then broadcasting the values occurs. If it a list of lists, the index still maintains the same shape but broadcasting is not done in this case.
#### Output of ``pd.show_versions()``
<details>
pandas: 0.23.0
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.11.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.8
feather: None
matplotlib: 3.0.1
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
</details>
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} | 7 | 2019-02-23T00:07:04Z | 2019-03-07T17:33:35Z | 2019-02-23T18:36:38Z | CONTRIBUTOR | null | Ran into errors when trying to run the test suite in a freshly installed conda environment. There's an error being caused by the fact that #25346 added `pytest-mock` as a dependency in all the CI jobs (to be able to use the `mocker` fixture), but didn't adapt `environment.yml`. This PR fixes that.
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https://api.github.com/repos/pandas-dev/pandas/issues/25418 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25418/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25418/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25418/events | https://github.com/pandas-dev/pandas/pull/25418 | 413,627,031 | MDExOlB1bGxSZXF1ZXN0MjU1NTcwNjk1 | 25,418 | Correct a typo of version number for interpolate() | {
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} | 1 | 2019-02-23T00:12:57Z | 2019-02-23T03:18:57Z | 2019-02-23T00:16:37Z | CONTRIBUTOR | null | Correct a typo of version number in the docstring of _shared_docs['interpolat e'] which is the docstring for pandas.core.resample.Resampler.interpolate, pandas.DataFrame.interpolate, panda s.Series.interpolate, and pandas.Panel.interpolate, and in documentation/user_guide/missing_data about the limit_area keyword argument in interpolate(). The reference can be found at https://github.com/pandas-dev/pandas/issue s/8000#issuecomment-465802842.
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https://api.github.com/repos/pandas-dev/pandas/issues/25419 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25419/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25419/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25419/events | https://github.com/pandas-dev/pandas/pull/25419 | 413,731,152 | MDExOlB1bGxSZXF1ZXN0MjU1NjM1MDQ0 | 25,419 | ENH: Add Series.str.casefold | {
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} | 13 | 2019-02-23T19:41:19Z | 2019-02-28T21:21:48Z | 2019-02-28T21:21:40Z | MEMBER | null | - [x] closes #25405
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25420 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25420/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25420/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25420/events | https://github.com/pandas-dev/pandas/pull/25420 | 413,731,884 | MDExOlB1bGxSZXF1ZXN0MjU1NjM1NDA3 | 25,420 | fixed geo accessor example in extending.rst | {
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} | 6 | 2019-02-23T19:47:34Z | 2019-02-25T22:35:46Z | 2019-02-25T22:35:46Z | CONTRIBUTOR | null | The previous code was incompatible with the following paragraph. See the second block of code in https://pandas.pydata.org/pandas-docs/stable/development/extending.html#registering-custom-accessors
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
<details>
```python
import pandas as pd
import numpy as np
```
# Before
## Register the DataFrame accessor "geo"
```python
@pd.api.extensions.register_dataframe_accessor("geo")
class GeoAccessor(object):
def __init__(self, pandas_obj):
self._validate(pandas_obj)
self._obj = pandas_obj
@staticmethod
def _validate(obj):
if 'lat' not in obj.columns or 'lon' not in obj.columns:
raise AttributeError("Must have 'lat' and 'lon'.")
@property
def center(self):
# return the geographic center point of this DataFrame
lat = self._obj.latitude
lon = self._obj.longitude
return (float(lon.mean()), float(lat.mean()))
def plot(self):
# plot this array's data on a map, e.g., using Cartopy
pass
```
## Test the DataFrame accessor "geo"
```python
ds = pd.DataFrame({'longitude': np.linspace(0, 10),
'latitude': np.linspace(0, 20)})
ds.geo.center
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-71f0445704bc> in <module>()
1 ds = pd.DataFrame({'longitude': np.linspace(0, 10),
2 'latitude': np.linspace(0, 20)})
----> 3 ds.geo.center
~\Anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
5061 if (name in self._internal_names_set or name in self._metadata or
5062 name in self._accessors):
-> 5063 return object.__getattribute__(self, name)
5064 else:
5065 if self._info_axis._can_hold_identifiers_and_holds_name(name):
~\Anaconda3\lib\site-packages\pandas\core\accessor.py in __get__(self, obj, cls)
169 # we're accessing the attribute of the class, i.e., Dataset.geo
170 return self._accessor
--> 171 accessor_obj = self._accessor(obj)
172 # Replace the property with the accessor object. Inspired by:
173 # http://www.pydanny.com/cached-property.html
<ipython-input-2-114caa965d3b> in __init__(self, pandas_obj)
2 class GeoAccessor(object):
3 def __init__(self, pandas_obj):
----> 4 self._validate(pandas_obj)
5 self._obj = pandas_obj
6
<ipython-input-2-114caa965d3b> in _validate(obj)
8 def _validate(obj):
9 if 'lat' not in obj.columns or 'lon' not in obj.columns:
---> 10 raise AttributeError("Must have 'lat' and 'lon'.")
11
12 @property
AttributeError: Must have 'lat' and 'lon'.
# After
## Register the DataFrame accessor "geo"
```python
@pd.api.extensions.register_dataframe_accessor("geo")
class GeoAccessor(object):
def __init__(self, pandas_obj):
self._validate(pandas_obj)
self._obj = pandas_obj
@staticmethod
def _validate(obj):
# verify each column name contains 'lat' or 'lon'
for col in obj.columns:
if 'lat' not in col and 'lon' not in col:
raise AttributeError("Must have 'lat' and 'lon'.")
@property
def center(self):
# return the geographic center point of this DataFrame
lat = self._obj.latitude
lon = self._obj.longitude
return (float(lon.mean()), float(lat.mean()))
def plot(self):
# plot this array's data on a map, e.g., using Cartopy
pass
```
## Test the DataFrame accessor "geo"
```python
ds = pd.DataFrame({'longitude': np.linspace(0, 10),
'latitude': np.linspace(0, 20)})
ds.geo.center
```
(5.0, 10.0)
</details>
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} | 3 | 2019-02-23T19:57:33Z | 2019-02-24T03:27:17Z | 2019-02-24T03:27:13Z | MEMBER | null | Follow-up to #25414.
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] | closed | false | null | [] | null | 3 | 2019-02-23T21:33:55Z | 2020-04-03T04:32:15Z | 2020-04-03T04:32:15Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
pandas.Timedelta('PT03H45M00S')
#### Problem description
Raises ValueError invalid abbreviation.
It should behave like:
pandas.Timedelta('P0DT0H1M0S')
Timedelta('0 days 00:01:00')
According to https://www.w3.org/TR/2004/REC-xmlschema-2-20041028/#duration
> If the number of years, months, days, hours, minutes, or seconds in any expression equals zero, the number and its corresponding designator ·may· be omitted. However, at least one number and its designator ·must· be present.
Implies that durations like PT3H45M0S or P30D or PT2S are syntactically valid.
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.3
pytest: 3.10.1
pip: 18.1
setuptools: 40.7.1
Cython: 0.29.2
numpy: 1.16.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: 1.8.3
patsy: 0.5.0+dev
dateutil: 2.7.3
pytz: 2018.9
blosc: 1.7.0
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.4.9
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 6 | 2019-02-24T00:10:15Z | 2019-09-18T08:07:17Z | 2019-05-02T12:17:31Z | NONE | null | #### Code Sample
```python
import datetime
import fastparquet
import pandas as pd
from dateutil.tz import tzutc
def setup():
# note: mock or real API call results in the same error
data = mock_list_s3_bucket()
df = pd.DataFrame.from_dict(data)
#print(data)
df.to_parquet(path, engine='auto', compression='snappy')
def main():
# error happens here - pytz.exceptions.UnknownTimeZoneError: 'tzutc()'
df = pd.read_parquet(path, engine='auto')
print(df)
def mock_list_s3_bucket():
# mock a call to S3 API
return [
{'Key': 'fun/file1.txt', 'LastModified': datetime.datetime(2018, 6, 7, 2, 59, 59, tzinfo=tzutc()), 'ETag': '"8c768c05b4faea563a8520acb983fb79"', 'Size': 4445, 'StorageClass': 'GLACIER'},
{'Key': 'fun/file2.txt', 'LastModified': datetime.datetime(2018, 6, 7, 2, 59, 59, tzinfo=tzutc()), 'ETag': '"8c768c05b4faea563a8520acb983fb79"', 'Size': 4445, 'StorageClass': 'GLACIER'},
{'Key': 'fun/file3.txt', 'LastModified': datetime.datetime(2018, 6, 7, 2, 59, 59, tzinfo=tzutc()), 'ETag': '"8c768c05b4faea563a8520acb983fb79"', 'Size': 4445, 'StorageClass': 'GLACIER'}
]
if __name__ == "__main__":
setup()
main()
```
#### Problem description
```
File "pandas\_libs\tslibs\timezones.pyx", line 84, in pandas._libs.tslibs.timezones.maybe_get_tz
File "pandas\_libs\tslibs\timezones.pyx", line 99, in pandas._libs.tslibs.timezones.maybe_get_tz
File "C:\Users\userx\AppData\Local\Programs\Python\Python37\lib\site-packages\pytz\__init__.py", line 178, in timezone
raise UnknownTimeZoneError(zone)
pytz.exceptions.UnknownTimeZoneError: 'tzutc()'
```
I have an app that reads bucket list data on a periodic basis from the S3 API. Up until recently, everything worked fine. When we upgraded to pandas 0.24 the problem with the parquet files being generated started to surface.
**Note**: I created a clean VM, installed pandas 0.24 and all dependencies, and was able to reproduce the issue.
Here is more info on the column metadata generated by fastparquet.
**pandas 0.23 metadata**
```
{"columns": [{"metadata": null, "name": "ETag", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": null, "name": "Key", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": {"timezone": "UTC"}, "name": "LastModified", "numpy_type": "datetime64[ns, UTC]", "pandas_type": "datetimetz"}, {"metadata": null, "name": "Size", "numpy_type": "int64", "pandas_type": "int64"}, {"metadata": null, "name": "StorageClass", "numpy_type": "object", "pandas_type": "unicode"}], "index_columns": [], "pandas_version": "0.23.4"}
```
**pandas 0.24 metadata**
```
{"columns": [{"metadata": null, "name": "ETag", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": null, "name": "Key", "numpy_type": "object", "pandas_type": "unicode"}, {"metadata": {"timezone": "tzutc()"}, "name": "LastModified", "numpy_type": "datetime64[ns, tzutc()]", "pandas_type": "datetimetz"}, {"metadata": null, "name": "Size", "numpy_type": "int64", "pandas_type": "int64"}, {"metadata": null, "name": "StorageClass", "numpy_type": "object", "pandas_type": "unicode"}], "index_columns": [], "pandas_version": "0.24.1"}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 39.0.1
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"color": "e11d21"... | closed | false | null | [] | null | 13 | 2019-02-24T02:16:12Z | 2019-04-16T20:10:13Z | 2019-04-16T20:10:13Z | CONTRIBUTOR | null | - [x] closes #22247
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As pull #19074 changed compute of hashtable from `self.values` to `self.codes` and `self.levels`, NaN values doesn't cover.
I just simply fixed this regression the special case, by check return value from the hashtable. | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This PR is the culmination of ongoing work since the start of November, and is therefore a bit on the bigger side, with several notes to make.
Things started out with me wanting to unify `.update` for Series/DF (#22358), resp. aiming towards a beefed-up `update`/`combine_first`/`coalesce` (#22812). While tackling the former (#23192), I encountered some problems with `df.update` upcasting stuff unnecessarily (#23606), and while trying to fix it, I ran into problems with `maybe_upcast_putmask` (#23823), which were directly caused by the utterly broken (and completely untested) `maybe_promote` (#23833).
I started with writing some tests (#23982), which turned out to be not so trivial, because there's a lot of complexity, and the correct behaviour wasn't alwasy immediate (also encountered some fun numpy bugs in the process: e.g. numpy/numpy#12525, numpy/numpy#12550)
I set out to write out a PR to fix those tests then, with the obvious goal of getting the test suite to pass - already that required a full rewrite of the method. I cracked my own tests after a while, but the test suite eluded me. As it turns out, `maybe_promote` mixes two very different behaviours - scalar values get cast to the new dtype, whereas arrays return their missing value marker. I tried kludging around this for a while, and decided it wasn't possible without creating a franken-solution.
The next step was to *separate* these two different behaviours into different functions, `maybe_promote_with_scalar` and `maybe_promote_with_array`, where `maybe_promote` is then just a thin wrapper that switches between the two. Actually also `maybe_promote_with_scalar` is just a fairly thin wrapper around `maybe_promote_with_array`, so that the actual many-cased promotion logic does not have to be implemented twice.
Often, the call-sites in the code just need the one or the other, and this could later be broken up correspondingly.
I updated the tests in #23982 (taking care to fully capture all the xfails there) and based this PR on that. This should give already an overview of what changed. In many cases, the current behaviour is broken, but I did make a few design decisions worth noting:
* `maybe_promote_with_array` consistently returns the missing value marker for the updated dtype. Since integer dtypes (plus bools and bytes) cannot hold `np.nan`, these cases now return `None`.
* all promotion logic is as conservative as possible, also within subtypes. For arrays, promotion always goes by value, and never by dtype. That means that, for example:
```
>>> maybe_promote(np.dtype('uint8'), fill_value=np.iinfo('uint8').max + 1)
(dtype('uint16'), 256)
>>> maybe_promote(np.dtype('uint8'), fill_value=np.array([-1], dtype='int64'))
(dtype('int16'), None)
```
* all promotion logic is as type-safe as possible, which means that [x] only stays [x] if the `fill_value` is of type [x] as well, where x is one of (datetime, timedelta, bool, bytes). Datetimetz must additionally match the timezone.
* all scalar `fill_values` now truly get cast to the updated dtype (before there were lots of ambiguities around int/float/complex/datetime/timedelta subtypes)
* I have changed the behavior that strings get interpreted for datetimes/timedeltas. Since this is an untested private method, and the test suite still passes just fine, I think this is actually a good thing, because it's too much in one method. String to datetime/timedelta *should* need an explicit cast, IMO.
```
>>> # master
>>> maybe_promote(np.dtype('datetime64[ns]'), '2018-01-01')
(dtype('<M8[ns]'), 1514764800000000000)
>>> # PR
>>> maybe_promote(np.dtype('datetime64[ns]'), '2018-01-01')
(dtype('O'), '2018-01-01')
>>> # master
>>> maybe_promote(np.dtype('timedelta64[ns]'), '1 day')
(dtype('<m8[ns]'), 86400000000000)
>>> # PR
>>> maybe_promote(np.dtype('timedelta64[ns]'), '1 day')
(dtype('O'), '1 day')
```
* `iNaT` is considered a missing value from the POV of `maybe_promote_with_array` in all situations. This takes one single integer out of the usable `int64`-range, but I think this is much cleaner.
There's still a few issues with `lib.infer_dtype` (e.g. #23554, of which I already fixed the complex case #25382), most notably that it cannot infer `datetime64tz` yet. Actually, through this PR, I'm learning how broken that method is *as well*, but fixing that will have to wait for some other time. Among other things, it currently faceplants for `PeriodArray` / `IntervalArray` (#23553). I haven't added tests for these types here, but ~9000 tests is already better than nothing, I hope. ;-)
Another point that could/should be considered is how EAs should deal with this (#24246). | {
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} | 2 | 2019-02-24T03:47:48Z | 2019-02-24T07:25:55Z | 2019-02-24T07:25:54Z | NONE | null | Backport PR #25289: BUG: fixed merging with empty frame containing an Int64 column (#25183) | {
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} | 41 | 2019-02-24T06:59:48Z | 2019-11-09T15:04:12Z | 2019-07-03T21:45:44Z | CONTRIBUTOR | null | closes #2311
This is primarily intended for LibreOffice calc spreadsheets but will
also work with LO Writer and probably with LO Impress documents.
This is an alternate solution to https://github.com/pandas-dev/pandas/pull/9070
There are test cases with several different problematic LibreOffice spread sheets.
git diff upstream/master -u | flake8 appeared to pass.
... I didn't do the whats new entry. Though I expect there's some more work to do before submitting this. I just wanted to get the core code in for comments.
The open issues is, the workaround for https://github.com/pandas-dev/pandas/issues/25422 is embedded in the current code (so all my tests pass right now) but that, or a better solution should move closer to the iso 8601 parser.
Also I don't have the parser class hooked up to a read_excel or read_ods function.
Using read_excel bothers me some... because its not an excel file. I think it would be more clear if there was either a separate read_ods function or a generic read_spreadsheet function than just pressing read_excel into being a generic spreadsheet reader.
Also this just a reader. I had never gotten around to implementing a writer.
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"descript... | closed | false | null | [] | null | 2 | 2019-02-24T08:59:10Z | 2019-04-24T16:25:07Z | 2019-04-24T16:25:07Z | NONE | null | ```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(["1","2","3","4","5"], ["a","b","c","d","e"], ["w"])
df.plot.line(x=df.index,y="w")
```
**Problem**
I was going to create a data frame and run it as a line plot.
When I run the command print(df) and hide the line of df.plot.line, I was able to see the data frame exactly as I expected
However, once I put it as a plot with the code above, an error occurs, How do it solve this problem?
Traceback (most recent call last):
File "version.py", line 5, in <module>
df.plot.line(x=df.index,y="w")
File "C:\Anaconda3\lib\site-packages\pandas\plotting\_core.py", line 3005, in line
return self(kind='line', x=x, y=y, **kwds)
File "C:\Anaconda3\lib\site-packages\pandas\plotting\_core.py", line 2941, in __call__
sort_columns=sort_columns, **kwds)
File "C:\Anaconda3\lib\site-packages\pandas\plotting\_core.py", line 1977, in plot_frame
**kwds)
File "C:\Anaconda3\lib\site-packages\pandas\plotting\_core.py", line 1766, in _plot
elif not isinstance(data[x], ABCSeries):
File "C:\Anaconda3\lib\site-packages\pandas\core\frame.py", line 2682, in __getitem__
return self._getitem_array(key)
File "C:\Anaconda3\lib\site-packages\pandas\core\frame.py", line 2726, in _getitem_array
indexer = self.loc._convert_to_indexer(key, axis=1)
File "C:\Anaconda3\lib\site-packages\pandas\core\indexing.py", line 1327, in _convert_to_indexer
.format(mask=objarr[mask]))
KeyError: "Index(['a', 'b', 'c', 'd', 'e'], dtype='object') not in index"
**pd.version**
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 17 Stepping 0, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: zh_TW.UTF-8
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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} | 3 | 2019-02-24T10:59:36Z | 2019-03-05T05:56:33Z | 2019-02-24T14:17:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
df = pd.DataFrame({'time': ['2013-12-01 08:00:00', '2013-12-01 08:30:00', '2013-12-01 09:00:00', '2014-05-01 09:00:00-07:00', '2014-05-01 09:30:00-07:00', '2014-05-01 10:00:00-07:00'], 'current': [208.0, 482.0, 774.0, 263.0, 538.5, 822.5], 'total': [67.5, 250.5, 573.5, 173.0, 377.0, 735.0]})
df['time'] = pd.to_datetime(df.time)
df = df.set_index('time')
type(df.index)
```
#### Problem description
Got the output: type(df.index) = <class 'pandas.core.indexes.base.Index'>
If 'time' is filled with all same type of data such as '2013-12-01 08:00:00' or all same type of data like
'2014-05-01 10:00:00-07:00', then the output is
<class 'pandas.core.indexes.datetimes.DatetimeIndex'>
Same result on 0.24.0
But worked for versions <= 0.23.x
#### Expected Output
type(df.index) = <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
#### 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: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 37 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: Chinese (Traditional)_Taiwan.950
pandas: 0.24.1
pytest: None
pip: 19.0.2
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-02-24T15:04:24Z | 2019-03-22T11:51:15Z | 2019-03-22T11:51:09Z | CONTRIBUTOR | null | - closes #23823
- 2 tests added
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
Follow h-vetinari's footstep. `maybe_upcast_putmask` was left untouched in #25425. Try to fix the bug so that `maybe_upcast_putmask` can also handle `ndarray` as well as `Series`.
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} | 6 | 2019-02-24T15:57:21Z | 2019-02-28T09:19:05Z | 2019-02-27T14:40:33Z | MEMBER | null | - [x] closes #14197
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ n/a] whatsnew entry
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} | 0 | 2019-02-24T20:21:54Z | 2019-02-28T15:53:30Z | 2019-02-28T15:53:30Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
expected = pd.DataFrame({'a':[5,5]}, index=[1.,2.])
result = pd.read_json(expected.to_json(orient='table'), orient='table')
```
```
In [141]: expected
Out[141]:
a
1.0 5
2.0 5
In [142]: result
Out[142]:
a
1 5
2 5
```
#### Problem description
Round trip should recover the original DataFrame. But the `result` index has been cast from float to integer.
#### Expected Output
The same as `expected`.
#### Output of ``pd.show_versions()``
<details>
AttributeError: module 'tables' has no attribute '__version__'
</details>
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} | 5 | 2019-02-24T20:47:52Z | 2019-02-28T15:53:36Z | 2019-02-28T15:53:31Z | CONTRIBUTOR | null | closes #25433
closes #25435
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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} | 0 | 2019-02-24T21:37:58Z | 2019-02-28T15:53:30Z | 2019-02-28T15:53:30Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
expected = pd.DataFrame({'1.': [1, 2]})
result = pd.read_json(expected.to_json(orient='table'), orient='table')
```
```
In [7]: expected
Out[7]:
1.
0 1
1 2
In [8]: result
Out[8]:
1970-01-01 00:00:01
0 1
1 2
```
#### Problem description
There is a weird column type conversion from string to Timestamp.
#### Expected Output
The result should be equal to expected.
#### Output of ``pd.show_versions()``
<details>
AttributeError: module 'tables' has no attribute '**version**'
</details>
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} | 0 | 2019-02-25T04:32:58Z | 2019-09-03T19:26:22Z | 2019-09-03T19:26:02Z | CONTRIBUTOR | null | xref [PR25339](https://github.com/pandas-dev/pandas/pull/25339)
LaTeX captions and labels are not supported in `longtable` environments produced by `DataFrame.to_latex()`. Having this would mean further automation of workflow when writing LaTeX reports containing tables whose data resides in a dataframe.
`DataFrame.to_latex()` could also be enhanced to produce a nested `table/tabular` environment containing the caption and label provided by the user when `longtable=False`. | {
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} | 7 | 2019-02-25T06:20:07Z | 2019-09-03T19:26:17Z | 2019-09-03T19:26:02Z | CONTRIBUTOR | null | - [x] closes #25436
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25438 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25438/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25438/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25438/events | https://github.com/pandas-dev/pandas/issues/25438 | 414,044,226 | MDU6SXNzdWU0MTQwNDQyMjY= | 25,438 | Dataframe replace empty string casts Int64 columns to float | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 2 | 2019-02-25T10:50:23Z | 2021-06-27T18:35:59Z | null | NONE | null | #### Code Sample
```python
df = pd.DataFrame({
'a': [1, 2, 3, np.nan],
'b': ['some', 'strings', 'here', 'he']
})
df['a'] = df['a'].astype('Int64')
df.replace('', np.nan).dtypes # Column a is now float
```
#### Problem description
The new Int64Dtype does not behave as expected here. If you do a global replacement of empty strings to NaN's, it seems weird that this would have the side effect of casting ints to floats.
#### Expected Output
Expected:
```
a Int64
b object
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 18.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 39.2.0
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.4.11
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.12
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25439 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25439/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25439/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25439/events | https://github.com/pandas-dev/pandas/issues/25439 | 414,246,586 | MDU6SXNzdWU0MTQyNDY1ODY= | 25,439 | FutureWarning when sorting tz-aware datetimeindex | {
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} | 3 | 2019-02-25T18:36:20Z | 2019-03-11T12:41:37Z | 2019-03-11T12:41:37Z | NONE | null | #### Problem
As reported [here](https://stackoverflow.com/questions/54854900/workaround-for-pandas-futurewarning-when-sorting-a-datetimeindex), sorting a tz-aware DatetimeIndex issues warning:
> FutureWarning: Converting timezone-aware DatetimeArray to timezone-naive ndarray with 'datetime64[ns]' dtype
MCVE here:
```python
import pandas as pd
idx = pd.DatetimeIndex(['2017-07-05 07:00:00', '2018-07-05 07:15:00','2017-07-05 07:30:00'])
df = pd.DataFrame({'C1':['a','b','c']},index=idx)
df = df.tz_localize('UTC')
df.sort_index()
```
#### Stack
The stack (Pandas 0.24.1) is:
```
__array__, datetimes.py:358
asanyarray, numeric.py:544
nargsort, sorting.py:257
sort_index, frame.py:4795
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.9-acso
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.2
setuptools: 40.7.2
Cython: 0.28.4
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | [] | closed | false | null | [] | null | 3 | 2019-02-25T18:47:55Z | 2019-02-26T05:50:51Z | 2019-02-26T05:50:51Z | MEMBER | null | Discovered in https://github.com/pandas-dev/pandas/pull/25308#discussion_r259641563, is this supposed to work @jbrockmendel, @TomAugspurger?
cc @jreback
```
In [3]: arg = np.array([np.datetime64('2018-01-01')], dtype=object)
In [4]: pd.arrays.DatetimeArray._from_sequence(arg, dtype='UTC')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in pandas_dtype(dtype)
2036 try:
-> 2037 npdtype = np.dtype(dtype)
2038 except Exception:
TypeError: data type "UTC" not understood
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-4-506f2a230c35> in <module>
----> 1 pd.arrays.DatetimeArray._from_sequence(arg, dtype='UTC')
in _from_sequence(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous, int_as_wall_time)
374 data, dtype=dtype, copy=copy, tz=tz,
375 dayfirst=dayfirst, yearfirst=yearfirst,
--> 376 ambiguous=ambiguous, int_as_wall_time=int_as_wall_time)
377
378 freq, freq_infer = dtl.validate_inferred_freq(freq, inferred_freq,
in sequence_to_dt64ns(data, dtype, copy, tz, dayfirst, yearfirst, ambiguous, int_as_wall_time)
1714 inferred_freq = None
1715
-> 1716 dtype = _validate_dt64_dtype(dtype)
1717
1718 if not hasattr(data, "dtype"):
in _validate_dt64_dtype(dtype)
1996 """
1997 if dtype is not None:
-> 1998 dtype = pandas_dtype(dtype)
1999 if is_dtype_equal(dtype, np.dtype("M8")):
2000 # no precision, warn
in pandas_dtype(dtype)
2041 raise TypeError("data type not understood")
2042 raise TypeError("data type '{}' not understood".format(
-> 2043 dtype))
2044
2045 # Any invalid dtype (such as pd.Timestamp) should raise an error.
TypeError: data type 'UTC' not understood
In [5]: pd.__version__
Out[5]: '0.25.0.dev0+170.g5bf07a99d.dirty'
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25441 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25441/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25441/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25441/events | https://github.com/pandas-dev/pandas/issues/25441 | 414,283,422 | MDU6SXNzdWU0MTQyODM0MjI= | 25,441 | Inconsistent type behavior loading data from json files | {
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} | 4 | 2019-02-25T21:31:26Z | 2019-07-31T21:54:47Z | 2019-07-31T21:54:47Z | MEMBER | null | For PEP8 compliance and better method naming consistency | {
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] | closed | false | null | [] | null | 1 | 2019-02-26T03:59:16Z | 2019-02-26T05:51:00Z | 2019-02-26T05:51:00Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
file_A.shape
writer = pd.ExcelWriter(filename, engine='xlsxwriter', options={'strings_to_urls': False})
file_A.to_excel(writer)
```
#### Problem description
Hi i have an issue that related to pd.ExcelWriter (the code example above). I can't write the output of excel file using that pandas. I Thought this is kind of bug, I have tried to reinstall jupyter notebook but it didn't well and still the same.
#### Expected Output
result.xlsx
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-02-26T06:37:06Z | 2019-03-30T19:17:29Z | 2019-03-30T19:17:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'name': ['A', 'A', 'A', 'B', 'B'], 'a':[1,2,3,4,5], 'date': ['2016', '2017', '2018', '2016', '2017']}, )
df['date'] = pd.to_datetime(df['date'])
#current behaviour
df.groupby('name').idxmin()
>> a date
>> name
>> A 0 1970-01-01 00:00:00.000000000
>> B 3 1970-01-01 00:00:00.000000003
```
#### Problem description
The returning df's dtypes would be the same as that of index.
Thanks.
#### Expected Output
```python
# expected behaviour
df.groupby('name').idxmin().astype('int')
>> a date
>> name
>> A 0 0
>> B 3 3
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 18.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: ja_JP.UTF-8
LOCALE: ja_JP.UTF-8
pandas: 0.23.4
pytest: 4.0.1
pip: 18.1
setuptools: 40.6.2
Cython: 0.29
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: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.11
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
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/25445 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25445/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25445/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25445/events | https://github.com/pandas-dev/pandas/pull/25445 | 414,465,458 | MDExOlB1bGxSZXF1ZXN0MjU2MTY4NDk3 | 25,445 | BUG: repr of np.datetime64('NaT') in Series/DataFrame with dtype object | {
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} | 5 | 2019-02-26T07:17:47Z | 2019-03-30T19:35:33Z | 2019-03-30T17:37:25Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Discovered while investigating #12499
Before:
```
In [1]: pd.Series([np.datetime64('NaT')], dtype=object)
Out[1]:
0 NaN
dtype: object
```
After:
```
In [1]: pd.Series([np.datetime64('NaT')], dtype=object)
Out[1]:
0 NaT
dtype: object
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25446 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25446/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25446/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25446/events | https://github.com/pandas-dev/pandas/issues/25446 | 414,505,214 | MDU6SXNzdWU0MTQ1MDUyMTQ= | 25,446 | [pd.qcut] ValueError: Input array must be 1 dimensional | {
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] | closed | false | null | [] | null | 4 | 2019-02-26T09:14:05Z | 2019-02-26T14:09:25Z | 2019-02-26T14:09:25Z | NONE | null | Sample data for replication:
```
data = pd.DataFrame({'a': list(range(100)),
'b': list(range(100))})
```
This works:
`pd.qcut(data['a'], 3)`
But this does not work:
```python
def test(data, col, cuts=3):
return pd.qcut(data[col], cuts)
test(data, 'a', 3)
```
Pandas version: `'0.24.1'`
#### Problem description
When I wrap a pandas pd.qcut() inside a function, I get an error message:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-17-c8b08c24ec5c> in <module>
8
9
---> 10 test(first, 'BUN', 3)
11
12 #pd.qcut(first['BUN'], 3)
<ipython-input-17-c8b08c24ec5c> in test(data, col, cuts)
5 col = []
6
----> 7 return pd.qcut(data[col], cuts)
8
9
~/miniconda3/envs/wip_conda/lib/python3.6/site-packages/pandas/core/reshape/tile.py in qcut(x, q, labels, retbins, precision, duplicates)
300 array([0, 0, 1, 2, 3])
301 """
--> 302 x_is_series, series_index, name, x = _preprocess_for_cut(x)
303
304 x, dtype = _coerce_to_type(x)
~/miniconda3/envs/wip_conda/lib/python3.6/site-packages/pandas/core/reshape/tile.py in _preprocess_for_cut(x)
512 x = np.asarray(x)
513 if x.ndim != 1:
--> 514 raise ValueError("Input array must be 1 dimensional")
515
516 return x_is_series, series_index, name, x
ValueError: Input array must be 1 dimensional
```
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https://api.github.com/repos/pandas-dev/pandas/issues/25448 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25448/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25448/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25448/events | https://github.com/pandas-dev/pandas/issues/25448 | 414,623,905 | MDU6SXNzdWU0MTQ2MjM5MDU= | 25,448 | Serialize/deserialize a Categorical whose values are taken from an enum | {
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"description": "read_csv... | open | false | null | [] | null | 5 | 2019-02-26T13:57:04Z | 2021-08-12T08:32:41Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
should run as standalone
```python
# Your code here
import pandas as pd
from enum import Enum, IntEnum, auto
import argparse
# Your code here
class ConnectionRoles(Enum):
Client = auto()
Server = auto()
csv_filename = "test.csv"
dtype_role = pd.api.types.CategoricalDtype(categories=list(ConnectionRoles), ordered=True)
df = pd.DataFrame({ "tcpdest": [ConnectionRoles.Server] }, dtype=dtype_role)
print(df.info())
print(df)
df.to_csv(csv_filename)
loaded = pd.read_csv(csv_filename, dtype= {"tcpdest": dtype_role})
print(loaded.info())
print(loaded)
```
which outputs
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 1 columns):
tcpdest 1 non-null category
dtypes: category(1)
memory usage: 177.0 bytes
None
tcpdest
0 ConnectionRoles.Server
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 2 columns):
Unnamed: 0 1 non-null int64
tcpdest 0 non-null category
dtypes: category(1), int64(1)
memory usage: 185.0 bytes
None
Unnamed: 0 tcpdest
0 0 NaN
```
The value ` ConnectionRoles.Server` became nan through the serialization/deserialization process:
#### Problem description
I want to be able to serialize (to_csv) then read (read_csv) a CategoricalDType that takes its values from a python Enum (or IntEnum).
Actually the dtype I use in my project (contrary to the toy example) is:
```
dtype_role = pd.api.types.CategoricalDtype(categories=list(ConnectionRoles), ordered=True)
class ConnectionRoles(Enum):
"""
Used to filter datasets and keep packets flowing in only one direction !
Parser should accept --destination Client --destination Server if you want both.
"""
Client = auto()
Server = auto()
def __str__(self):
# Note that defining __str__ is required to get ArgumentParser's help output to include
# the human readable (values) of Color
return self.name
@staticmethod
def from_string(s):
try:
return ConnectionRoles[s]
except KeyError:
raise ValueError()
def __next__(self):
if self.value == 0:
return ConnectionRoles.Server
else:
return ConnectionRoles.Client
```
I've search the tracker and the most relevant ones (but yet different) might be:
- https://github.com/pandas-dev/pandas/issues/20498
- my past issue https://github.com/pandas-dev/pandas/issues/22262
#### Expected Output
#### Output of ``pd.show_versions()``
I am using v0.23.4 with a patch from master to fix some bug.
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.0
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0+unknown
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.6
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"description": "read_excel, to_excel",
... | open | false | null | [] | null | 23 | 2019-02-26T15:25:27Z | 2021-08-05T14:01:21Z | null | NONE | null |
```
df=pd.read_excel(xl_file, sheet_name=xl_sheet,header=[18,19], usecols='B,D:J,L')
```
#### Problem description
df=pd.read_excel(xl_file, sheet_name=xl_sheet,header=[18,19], usecols='B,D:J,L')
throws the following error after conda update:
ValueError: cannot specify usecols when specifying a multi-index header
The script run perfectly fine before.
#### Expected Output
Dataframe with multi-index columns, as before.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.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: 0.24.1
pytest: 3.3.2
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.27.3
numpy: 1.15.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: None
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 1 | 2019-02-26T15:59:34Z | 2019-02-26T16:24:13Z | 2019-02-26T16:24:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>>import pandas as pd
>>>df = pd.DataFrame(data=[[1,2], [3, 4], [5, 6]], index=[10, 20, 30])
>>>out = df.groupby(level=0).apply(lambda x: print(x))
```
#### Problem description
When `print` is used within the `groupby.apply`, it seems that the function is applied to the first index twice. I am not sure if this behaviour is due to `print` function, or it actually occurs for any given function. The issue may not seem to be a big deal at first; however, when `groupby.apply` is combined with multiprocessing framework, it hampers the efficiency and speedup of the code.
#### Output:
```
0 1
10 1 2
0 1
10 1 2
0 1
20 3 4
0 1
30 5 6
```
#### Expected Output
```
0 1
10 1 2
0 1
20 3 4
0 1
30 5 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.23.4
pytest: 3.8.2
pip: 19.0.2
setuptools: 40.4.3
Cython: 0.28.5
numpy: 1.14.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: 1.8.1
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: 3.0.0
openpyxl: 2.5.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.1
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.12
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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"color": "5319e7"... | closed | false | null | [] | null | 8 | 2019-02-26T17:19:54Z | 2019-11-02T00:49:17Z | 2019-11-02T00:49:16Z | NONE | null | ```python
chunksize = 100000
reader = pd.read_csv(in_file, chunksize=chunksize, encoding='utf-8', engine='python', compression='gzip')
for chunk in reader:
train, valid = np.split(chunk.sample(frac=1).reset_index(drop=True), [int(.8 * len(chunk))])
train.dropna(inplace=True)
if not valid_file.exists():
train.to_csv(train_file, index=False)
else:
train.to_csv(train_file, index=False, mode='a', header=False)
```
#### Problem description
When loading a large data csv file using chunksize dropna inplace doesn't work. In order to make it work you have to
```python
train = train.dropna()
```
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} | 1 | 2019-02-26T19:35:12Z | 2019-10-29T04:48:23Z | 2019-10-23T20:08:08Z | CONTRIBUTOR | null | #### Problem description
Empirically, `pandas.read_excel(foo, na_filter=False)` works as expected, but the na_filter argument doesn't appear to be mentioned in the read_excel docs.
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"description": "Apply, Aggregate, Tra... | open | false | null | [] | null | 3 | 2019-02-26T19:36:37Z | 2020-06-28T06:26:33Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(20).reshape(10,-1), columns=[*'AB'])
def f(x):
print(type(x))
print(df.agg([f]))
```
Output:
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
<class 'int'>
A B
f f
0 None None
1 None None
2 None None
3 None None
4 None None
5 None None
6 None None
7 None None
8 None None
9 None None
#### Problem description
When passing a function as a list in dataframe.agg, the behavior is different than when you pass the function directly into dataframe agg.
#### Expected Output
```pyton
def f(x):
print(type(x))
print(df.agg(f))
```
Output:
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
A None
B None
dtype: object
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 26 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 3.7.3
pip: 10.0.1
setuptools: 40.0.0
Cython: 0.28.5
numpy: 1.15.4
scipy: 1.0.0
pyarrow: None
xarray: 0.11.3
IPython: 6.5.0
sphinx: 1.7.7
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.5.5
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.8
lxml.etree: 4.2.4
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
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} | 10 | 2019-02-26T19:56:42Z | 2019-03-12T09:17:12Z | 2019-02-27T22:26:54Z | MEMBER | null | 4.3.5 - February 24, 2019 - last Python 2.7 Maintenance Release
This is the final Python 2.x release of isort, and includes the following major changes:
Potentially Interface Breaking:
The -r option for removing imports has been renamed -rm to avoid accidental deletions and confusion with the -rc recursive option.
__init__.py has been removed from the default ignore list. The default ignore list is now empty - with all items needing to be explicitly ignored.... | {
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"description": "Related ... | closed | false | null | [] | null | 6 | 2019-02-26T20:05:49Z | 2019-03-04T23:02:52Z | 2019-03-04T16:41:34Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
data = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
data.iloc[:, (1,)] # or data.iloc[:, tuple([1])]
## Below is the stack trace
## I removed personal information in the python path.
IndexingError Traceback (most recent call last)
<ipython-input-3-a2992a163015> in <module>()
----> 1 data.iloc[:, (1,)]
/path/to/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
1492 except (KeyError, IndexError, AttributeError):
1493 pass
-> 1494 return self._getitem_tuple(key)
1495 else:
1496 # we by definition only have the 0th axis
/path/to/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
2141 def _getitem_tuple(self, tup):
2142
-> 2143 self._has_valid_tuple(tup)
2144 try:
2145 return self._getitem_lowerdim(tup)
/path/to/lib/python2.7/site-packages/pandas/core/indexing.pyc in _has_valid_tuple(self, key)
221 raise IndexingError('Too many indexers')
222 try:
--> 223 self._validate_key(k, i)
224 except ValueError:
225 raise ValueError("Location based indexing can only have "
/path/to/lib/python2.7/site-packages/pandas/core/indexing.pyc in _validate_key(self, key, axis)
2072 # a tuple should already have been caught by this point
2073 # so don't treat a tuple as a valid indexer
-> 2074 raise IndexingError('Too many indexers')
2075 elif is_list_like_indexer(key):
2076 # check that the key does not exceed the maximum size of the index
IndexingError: Too many indexers
```
#### Problem description
According to the [current document](https://pandas.pydata.org/pandas-docs/version/0.24.1/reference/api/pandas.DataFrame.iloc.html), `iloc[]` support a list or array of integers as inputs.
Could you add support for tuples?
Or is there a particular reason to not allow tuples as inputs? If so, you may want to consider documenting it in that link.
#### Expected Output
```python
b
0 4
1 5
2 6
```
#### Output of 0.24.1
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.14.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: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 9.0.1
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 5.5.0
sphinx: None
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/25457 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25457/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25457/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25457/events | https://github.com/pandas-dev/pandas/issues/25457 | 414,807,324 | MDU6SXNzdWU0MTQ4MDczMjQ= | 25,457 | Grouped dataframe "name" attribute overrides column access / not well documented | {
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... | open | false | null | [] | null | 11 | 2019-02-26T20:37:09Z | 2020-05-13T02:45:15Z | null | NONE | null | #### Code Sample
```python
>>> df = pd.DataFrame({'val': [9, 10, 3, 6, 2, 3], 'name': list('xxyxyy'), 'group': list('aaabbb')})
>>> df
val name group
0 9 x a
1 10 x a
2 3 y a
3 6 x b
4 2 y b
5 3 y b
```
Works correctly:
```python
>>> df.groupby('group').apply(lambda g: g[g['name'] == 'x'])
val name group
group
a 0 9 x a
1 10 x a
b 3 6 x b
```
Errors out:
```python
>>> df.groupby('group').apply(lambda g: g[g.name == 'x'])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
```
Rest of the traceback is here:
<details>
```
~/venv/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2655 try:
-> 2656 return self._engine.get_loc(key)
2657 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: False
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/venv/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
688 try:
--> 689 result = self._python_apply_general(f)
690 except Exception:
~/venv/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
706 keys, values, mutated = self.grouper.apply(f, self._selected_obj,
--> 707 self.axis)
708
~/venv/lib/python3.7/site-packages/pandas/core/groupby/ops.py in apply(self, f, data, axis)
189 group_axes = _get_axes(group)
--> 190 res = f(group)
191 if not _is_indexed_like(res, group_axes):
<ipython-input-282-522c70a9fa21> in <lambda>(g)
----> 1 df.groupby('group').apply(lambda g: g[g.name == 'x'])
~/venv/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
2926 return self._getitem_multilevel(key)
-> 2927 indexer = self.columns.get_loc(key)
2928 if is_integer(indexer):
~/venv/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2657 except KeyError:
-> 2658 return self._engine.get_loc(self._maybe_cast_indexer(key))
2659 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: False
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/venv/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2655 try:
-> 2656 return self._engine.get_loc(key)
2657 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: False
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-282-522c70a9fa21> in <module>
----> 1 df.groupby('group').apply(lambda g: g[g.name == 'x'])
~/venv/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
699
700 with _group_selection_context(self):
--> 701 return self._python_apply_general(f)
702
703 return result
~/venv/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
705 def _python_apply_general(self, f):
706 keys, values, mutated = self.grouper.apply(f, self._selected_obj,
--> 707 self.axis)
708
709 return self._wrap_applied_output(
~/venv/lib/python3.7/site-packages/pandas/core/groupby/ops.py in apply(self, f, data, axis)
188 # group might be modified
189 group_axes = _get_axes(group)
--> 190 res = f(group)
191 if not _is_indexed_like(res, group_axes):
192 mutated = True
<ipython-input-282-522c70a9fa21> in <lambda>(g)
----> 1 df.groupby('group').apply(lambda g: g[g.name == 'x'])
~/venv/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
2925 if self.columns.nlevels > 1:
2926 return self._getitem_multilevel(key)
-> 2927 indexer = self.columns.get_loc(key)
2928 if is_integer(indexer):
2929 indexer = [indexer]
~/venv/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2656 return self._engine.get_loc(key)
2657 except KeyError:
-> 2658 return self._engine.get_loc(self._maybe_cast_indexer(key))
2659 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2660 if indexer.ndim > 1 or indexer.size > 1:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: False
```
</details>
#### Problem description
When you call a method on a groupby that expects a function, the documentation states that the function should expect a DataFrame. Indeed if you inspect the type, a DataFrame is passed. However, this DataFrame has a new attribute added --- `name` --- and this overwrites the dot accessor for a column `"name"` if it exists. This is a little troubling since doing dotted access will work outside of the groupby and not inside the groupby function.
First, this needs to be documented more explicitly---I found it once in the documentation for *one* of the related functions that the `.name` attribute gets added, but cannot find it again, so I'm not sure which one it was. Edit: It was the `transform()` method's docstring, as shown in [a comment below](https://github.com/pandas-dev/pandas/issues/25457#issuecomment-467998238).
#### Related
https://github.com/pandas-dev/pandas/issues/9545
#### Expected Output
The expected output is what happens when you use the `[]` indexer, instead of dot access.
#### Suggestions
Not necessarily mutually exclusive:
* Give a better error message so that it's known `.name` is an attribute of the group DataFrame
* Improve documentation on all related methods to know the `.name` attribute exists and will override the column dot access
* Only add the `.name` attribute if a column of that name doesn't exist
* Append an underscore to the attribute so there's a much lower chance of conflicts, e.g. `groupdf.name_`
* Change the attribute name entirely for similar lower chance of conflicts, e.g. `groupdf.grouped_value`
* Move into a method call instead, e.g. `groupdf.get_group_name()`
* Add a kwarg to `apply()` / `transform()` which toggles whether to send a second argument into the function, that second argument being the group name
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.beta.4
python-bits: 64
OS: Darwin
OS-release: 17.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 39.0.1
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-02-27T10:06:04Z | 2019-02-28T14:13:58Z | 2019-02-28T12:45:16Z | CONTRIBUTOR | null | Signed-off-by: Philippe Ombredanne <pombredanne@nexb.com>
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} | 2 | 2019-02-27T10:18:10Z | 2019-10-16T12:52:55Z | 2019-10-16T12:52:55Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# works fine
x = pd.Series([0])
x.reindex([0,0])
# ValueError: cannot reindex with a non-unique indexer
x.index = x.index.astype('category')
x.reindex([0,0])
```
#### Problem description
When a series or frame has a categorical index, it cannot be reindexed using an indexer that has duplicate values. This is inconsistent with other indexes, and makes categorical indexes less useful.
#### Expected Output
No error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.15-200.fc28.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.1
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 4.2.1
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.2
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"description": "Related to indexing on s... | open | false | null | [] | null | 2 | 2019-02-27T12:39:52Z | 2020-07-24T11:12:42Z | null | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
category_idx = pd.Index(['A', 'B'])
date_idx = pd.date_range('2018-01', '2018-02', freq='MS')
idx = pd.MultiIndex.from_product([category_idx, date_idx], names=['category', 'date'])
series = pd.Series(np.random.randn(len(category_idx) * len(date_idx)), index=idx)
series
# category date
# A 2018-01-01 -0.060216
# 2018-02-01 0.757948
# B 2018-01-01 0.411784
# 2018-02-01 -0.062216
# dtype: float64
new_date_idx = date_idx.union([date_idx[-1] + date_idx.freq])
new_date_idx
# DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01'], dtype='datetime64[ns]', freq='MS')
series.reindex(index=new_date_idx, level='date')
# Where are the NaNs?
# category date
# A 2018-01-01 -0.060216
# 2018-02-01 0.757948
# B 2018-01-01 0.411784
# 2018-02-01 -0.062216
# dtype: float64
series.loc['A'].reindex(index=new_date_idx)
# 2018-01-01 -0.060216
# 2018-02-01 0.757948
# 2018-03-01 NaN
# Freq: MS, dtype: float64
```
#### Problem description
Passing `level` to `reindex` on a multi-index doesn't reindex the desired level.
#### Expected Output
In the above example, I'd expect to see two new rows, each containing `NaN`. The expected output is analogous to calling `reindex` on a single-level index.
I originally posted this issue as a question to StackOverflow, where a user kindly posted a workaround: https://stackoverflow.com/questions/54904974/extending-a-datetime-index-within-a-multi-index.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.4.3
Cython: None
numpy: 1.15.3
scipy: 1.2.0
pyarrow: 0.12.1
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.6
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 3.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.12
pymysql: 0.9.2
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
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} | 1 | 2019-02-27T17:11:49Z | 2019-03-19T16:07:21Z | null | MEMBER | null | Quite a few PRs lately have worked around the validation script error of descriptions not ending with a period when there are bullet points by adding periods to the end of each bullet point. This looks tacky and is really just a hack to get around the validator failing.
Would rather update the validator to maybe ignore bullet points as part of the description, though @datapythonista may have other thoughts.
xref #20298 | {
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} | 24 | 2019-02-27T18:54:51Z | 2019-05-07T01:00:36Z | 2019-05-07T00:56:44Z | CONTRIBUTOR | null | Unless they are context managers.
- [X] closes #25135
- [X] tests added / passed (not applicable)
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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but the unit isn't supplied.
- [X] closes #15836
- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2019-02-27T20:54:14Z | 2021-06-27T18:45:37Z | null | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/25447#discussion_r260730337
#### Code Sample, a copy-pastable example if possible
```python
import pandas.util.testing as tm
ind = tm.makeCategoricalIndex(100)
ind.view('i8')
```
#### Problem description
```python-traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-5cb1f6f042dd> in <module>()
1 import pandas.util.testing as tm
2 ind = tm.makeCategoricalIndex(100)
----> 3 ind.view('i8')
~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in view(self, cls)
913 # index type here
914 if cls is not None and not hasattr(cls, '_typ'):
--> 915 result = self._data.view(cls)
916 else:
917 result = self._shallow_copy()
TypeError: view() takes 1 positional argument but 2 were given
```
#### Expected Output
```python-traceback
TypeError: Cannot change data-type for object array
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.0
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25465 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25465/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25465/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25465/events | https://github.com/pandas-dev/pandas/pull/25465 | 415,323,087 | MDExOlB1bGxSZXF1ZXN0MjU2ODM2Njk1 | 25,465 | DOC: Fix docstring for read_sql_table | {
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"description": "Code style, linting, ... | closed | false | null | [] | null | 8 | 2019-02-27T20:56:36Z | 2019-03-01T10:14:40Z | 2019-02-28T20:00:31Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
```
################################################################################
###################### Docstring (pandas.read_sql_table) ######################
################################################################################
Read SQL database table into a DataFrame.
Given a table name and a SQLAlchemy connectable, returns a DataFrame.
This function does not support DBAPI connections.
Parameters
----------
table_name : str
Name of SQL table in database.
con : SQLAlchemy connectable or database URI str
SQLite DBAPI connection mode not supported.
schema : str, default None
Name of SQL schema in database to query (if database flavor
supports this). Uses default schema if None (default).
index_col : str or list of str, optional, default: None
Column(s) to set as index(MultiIndex).
coerce_float : bool, default True
Attempts to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point. Can result in loss of Precision.
parse_dates : list or dict, default None
The behavior is as follows:
- List of column names to parse as dates.
- Dict of ``{column_name: format string}`` where format string is
strftime compatible in case of parsing string times or is one of
(D, s, ns, ms, us) in case of parsing integer timestamps.
- Dict of ``{column_name: arg dict}``, where the arg dict corresponds
to the keyword arguments of :func:`pandas.to_datetime`
Especially useful with databases without native Datetime support,
such as SQLite.
columns : list, default None
List of column names to select from SQL table.
chunksize : int, default None
If specified, returns an iterator where `chunksize` is the number of
rows to include in each chunk.
Returns
-------
DataFrame
A SQL table is returned as two-dimensional data structure with labeled
axes.
See Also
--------
read_sql_query : Read SQL query into a DataFrame.
read_sql : Read SQL query or database table into a DataFrame.
Notes
-----
Any datetime values with time zone information will be converted to UTC.
Examples
--------
>>> pd.read_sql_table('table_name', 'postgres:///db_name') # doctest:+SKIP
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.read_sql_table" correct. :)
``` | {
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} | 22 | 2019-02-27T21:04:20Z | 2019-05-07T04:19:07Z | 2019-05-07T01:26:51Z | CONTRIBUTOR | null | In Windows, the `validate_docstrings.py` script fails because an encoding error. It has been fixed here.
PR done in the London python sprints meetup.
CC: @datapythonista | {
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} | 3 | 2019-02-28T04:02:31Z | 2019-02-28T18:17:17Z | 2019-02-28T12:44:44Z | MEMBER | null | Closes #23830
xref #23969 | {
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} | 3 | 2019-02-28T04:02:46Z | 2019-03-13T17:10:37Z | 2019-03-13T17:10:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
data = [{"name": "test","address": [{"add": "add1"}]},{"address": [{"add": "add2"}]}]
df = pd.io.json.json_normalize(data,"address","name",errors="ignore")
df
```
#### Problem description
In Json_normalize if the metadata column is nullable with option errors set as ignore the null values in dataframe are literal nan string and not numpy.nan
#### Expected Output
Expecting to see numpy.nan for null metadata string column values instead of nan string
Pandas version 0.24.1
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} | 4 | 2019-02-28T04:25:05Z | 2019-02-28T18:17:16Z | 2019-02-28T12:42:55Z | MEMBER | null | Closes #23222
xref #23645 | {
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] | closed | false | null | [] | null | 6 | 2019-02-28T05:10:46Z | 2019-09-16T15:14:23Z | 2019-09-16T15:14:23Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
#### Problem description
There is a typo in *full whatsnew* link in [pandas 0.24.1 release page](https://github.com/pandas-dev/pandas/releases/tag/v0.24.1) that point to [this page](http://pandas.pydata.org/pandas-docs/version/0.24.2/whatsnew/v0.24.2.html) which doesn't exist.
#### Expected Output
It should point to:
http://pandas.pydata.org/pandas-docs/version/0.24.1/whatsnew/v0.24.1.html | {
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"id": 7... | open | false | null | [] | null | 4 | 2019-02-28T07:50:50Z | 2021-06-27T18:48:11Z | null | NONE | null | All of these examples for using `agg` work fine:
```
import pandas as pd
df = pd.DataFrame({"A":['A','A','B','B','B'],
"B":[1,2,1,1,2],
"C":[9,8,7,6,5]})
df.groupby('A')[['B','C']].agg({'B':'sum','C':'count'})
df.groupby('A')[['B','C']].agg({'B':['sum','count'],'C':'count'})
df.groupby('A')[['B']].agg('sum')
df.groupby('A')['B'].agg('sum')
```
This one throws a future warning as mentioned [here](https://github.com/pandas-dev/pandas/pull/15931):
```
df.groupby('A')['B'].agg({'B':['sum','count']})
```
This one works just fine:
```
df.groupby('A')[['B','C']].agg({'B':'sum'})
```
But this one throws an error (I'm aware this expression isn't necessary):
```
df.groupby('A')[['B']].agg({'B':'sum'})
```
> SpecificationError: nested dictionary is ambiguous in aggregation
Why does it throw this error?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.1
pytest: 3.9.1
pip: 19.0.1
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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#### Code Sample, a copy-pastable example if possible
Download this file [upload.txt](https://github.com/pandas-dev/pandas/files/2917684/upload.txt)
```python
# Your code here
import pandas as pd
from enum import Enum, IntEnum, auto
import argparse
# I attached the file in the github issue
filename = "upload.txt"
# this field is coded on 64 bits so 'UInt64' looks perfect.
column = "tcp.options.mptcp.sendkey"
with open(filename) as fd:
print("READ CHUNK BY CHUNK")
res = pd.read_csv(
fd,
comment='#',
sep='|',
dtype={column: 'UInt64' },
usecols=[column],
chunksize=1
)
for chunk in (res):
# print("chunk %d" % i)
print(chunk)
fd.seek(0) # rewind
print("READ THE WHOLE FILE AT ONCE ")
res = pd.read_csv(
fd,
comment='#',
sep='|',
usecols=[column],
dtype={"tcp.options.mptcp.sendkey": 'UInt64' }
)
print(res)
```
If I read in chunks, read_csv succeeds, if I try to read the column at once, I get
```
Traceback (most recent call last):
File "test2.py", line 34, in <module>
dtype={"tcp.options.mptcp.sendkey": 'UInt64' }
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/io/parsers.py", line 702, in parser_f
return _read(filepath_or_buffer, kwds)
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/io/parsers.py", line 435, in _read
data = parser.read(nrows)
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/io/parsers.py", line 1139, in read
ret = self._engine.read(nrows)
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/io/parsers.py", line 1995, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 900, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 915, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 992, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 1124, in pandas._libs.parsers.TextReader._convert_column_data
File "pandas/_libs/parsers.pyx", line 1155, in pandas._libs.parsers.TextReader._convert_tokens
File "pandas/_libs/parsers.pyx", line 1235, in pandas._libs.parsers.TextReader._convert_with_dtype
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/core/arrays/integer.py", line 308, in _from_sequence_of_strings
return cls._from_sequence(scalars, dtype, copy)
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/core/arrays/integer.py", line 303, in _from_sequence
return integer_array(scalars, dtype=dtype, copy=copy)
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/core/arrays/integer.py", line 111, in integer_array
values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
File "/nix/store/mhiszrb8cpicjkzgraq796asj2sxpjch-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/core/arrays/integer.py", line 188, in coerce_to_array
values.dtype))
TypeError: object cannot be converted to an IntegerDtype
```
#### Expected Output
I would like the call to read_csv to succeed without having to read in chunks (which seems to have other side effects as well).
#### Output of ``pd.show_versions()``
<details>
I am using v0.23.4 with a patch from master to fix some other bug.
[paste the output of ``pd.show_versions()`` here below this line]
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.0
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0+unknown
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.6
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-02-28T11:03:08Z | 2019-02-28T12:41:08Z | 2019-02-28T12:41:08Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'])
df.drop(columns=['A','not_occurring'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py", line 3697, in drop
errors=errors)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py", line 3111, in drop
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py", line 3143, in _drop_axis
new_axis = axis.drop(labels, errors=errors)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 4404, in drop
'{} not found in axis'.format(labels[mask]))
KeyError: "['not_occurring'] not found in axis"
```
#### Problem description
In the pandas documentation for DataFrame drop (https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.DataFrame.drop.html#pandas.DataFrame.drop), the following is mentioned:
`KeyError: If none of the labels are found in the selected axis`
However, when looking at the provided code snippet, we see that even though there is a label which is found in the selected axis (`'A'`), the KeyError is thrown.
#### Expected Output
```python
df = pd.DataFrame(np.arange(12).reshape(3,4), columns=['A', 'B', 'C', 'D'])
df.drop(columns=['A','not_occurring'])
B C D
0 1 2 3
1 5 6 7
2 9 10 11
```
### Suggested Fix
Although from this issue, it seems like the code is at fault, I would suggest to change the documentation to `KeyError: If one of the labels are not found in the selected axis`. If the core team agrees with this fix, then I would be happy to provide a pull request that does this.
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+162.gc9863865c.dirty
pytest: 4.2.1
pip: 19.0.1
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-02-28T11:41:56Z | 2019-02-28T14:11:26Z | 2019-02-28T12:41:08Z | CONTRIBUTOR | null | - [X] closes #25473
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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} | 8 | 2019-02-28T12:49:55Z | 2021-08-05T11:58:35Z | 2021-08-05T11:58:35Z | NONE | null |
#### Problem description
The present implementation of `df.style.hide_index()` removes the complete index while rendering html.
It would be better to have an optional input of index level and hide only that index level while rendering.
example `df.style.hide_index(level=1)`
It provides more option and flexibility to user.
Sorry to post this as an issue, but I didn't find any option for suggesting new features.
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] | closed | false | null | [] | null | 2 | 2019-02-28T13:44:43Z | 2019-10-20T23:44:16Z | 2019-10-20T23:44:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
def getHourOfTime(myString):
return int(myString[0:2:1])
import pandas as pd
import numpy as np
e = np.random.randint(24, size=100)
e_dataframe = pd.DataFrame(e)
e_dataframe = e_dataframe.rename(index=str, columns={0:'Time_x'})
x=e_dataframe
x['hour'] = e_dataframe['Time_x'].dropna().apply(lambda x: getHourOfTime(str(x)))
display(x['hour'].head(10))
x['TimeCategory']= pd.cut(x['hour'],bins=[0,6, 9, 16, 19, 24],
labels=['MORNING', 'MPH', 'DAY', 'EPH', 'NIGHT'],
include_lowest=True)
x['TimeCategory'] = x['TimeCategory'].replace('MORNING', 'NIGHT')
fdf = x[x['TimeCategory'] != 'MORNING']
display(x[x['TimeCategory'] != 'MORNING'].head(5))
fdf['TimeCategory'].value_counts()
```
#### Problem description
After replace, value_counts() count MORNING category label
DAY 59046
EPH 29039
NIGHT 25687
MPH 22847
MORNING 0
| Time_x | hour | TimeCategory
-- | -- | -- | --
14 | 14 | DAY
23 | 23 | NIGHT
11 | 11 | DAY
1 | 1 | NIGHT
7 | 7 | MPH
I have install all the latest versions of panda, numpy, jupyter notebooks, et.c
#### Expected Output
DAY 59046
EPH 29039
NIGHT 25687
MPH 22847
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
NSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 2 | 2019-02-28T13:55:59Z | 2019-02-28T14:43:49Z | 2019-02-28T14:42:30Z | NONE | null | Backport PR #25266: BUG: Fix regression on DataFrame.replace for regex | {
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} | 0 | 2019-02-28T14:21:34Z | 2019-02-28T15:17:29Z | 2019-02-28T15:17:29Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
python make.py --single DataFrame.join
Traceback (most recent call last):
File "make.py", line 339, in <module>
sys.exit(main())
File "make.py", line 334, in main
args.verbosity, args.warnings_are_errors)
File "make.py", line 46, in __init__
single_doc = self._process_single_doc(single_doc)
File "make.py", line 88, in _process_single_doc
'pandas.DataFrame.head)').format(single_doc))
ValueError: --single=DataFrame.join not understood. Value should be a valid path to a .rst or .ipynb file, or a valid pandas object (e.g. categorical.rst or pandas.DataFrame.head)
```
#### Problem description
Using the code snippet described in the pandas contribution guide (https://pandas-docs.github.io/pandas-docs-travis/development/contributing.html#id48), we get a ValueError. This is fixed by instead using the following command:
```
python make.py --single pandas.DataFrame.join
```
I will create a PR updating the documentation. | {
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} | 5 | 2019-02-28T14:29:07Z | 2019-02-28T15:17:33Z | 2019-02-28T15:17:30Z | CONTRIBUTOR | null | - [X] closes #25478
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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... | closed | false | null | [] | null | 4 | 2019-02-28T15:04:14Z | 2019-03-01T12:57:50Z | 2019-03-01T12:57:50Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
*When in the `doc` folder:*
```
python make.py --single $(pwd)/source/development/contributing.rst
Running Sphinx v1.8.4
loading pickled environment... done
[autosummary] generating autosummary for: index.rst
building [mo]: targets for 0 po files that are out of date
building [html]: targets for 1 source files that are out of date
updating environment: [] 0 added, 1 changed, 0 removed
reading sources... [100%] index
/home/max/git/pandas/doc/source/index.rst:26: WARNING: toctree contains reference to nonexisting document 'home/max/git/pandas/doc/source/development/contributing'
looking for now-outdated files... none found
pickling environment... done
checking consistency... done
preparing documents... done
writing output... [100%] index
generating indices... genindex py-modindex
writing additional pages... search
copying static files... done
copying extra files... done
dumping search index in English (code: en) ... done
dumping object inventory... done
build succeeded, 1 warning.
The HTML pages are in build/html.
```
#### Problem description
Although `sphinx-build` does exit correctly, no html file corresponding to the single required file is created in the `build/html` folder. The `build/html` folder is as follows:
```
.
├── genindex.html
├── index.html
├── objects.inv
├── py-modindex.html
├── search.html
├── searchindex.js
├── _sources
│ └── index.rst.txt
└── _static
├── ajax-loader.gif
├── banklist.html
├── basic.css
├── ...
```
The page opened in the web browser also gives back a 404.
From my first look around in `make.py`, I do not see anywhere where we are using the single parameter, except in the `init` function of `DocBuilder`. I will investigate further.
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} | 2 | 2019-02-28T19:23:04Z | 2019-03-10T21:59:14Z | 2019-03-10T21:59:14Z | MEMBER | null | see also https://github.com/pandas-dev/pandas/issues/17372#issuecomment-325759975
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'A': ["x", "y", "z"], 'B': [1, 2, 3]})
df['A'].plot()
```
#### Problem description
```python-traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-06628911498f> in <module>
1 import pandas as pd
2 df = pd.DataFrame({'A': ["x", "y", "z"], 'B': [1, 2, 3]})
----> 3 df['A'].plot()
~\Anaconda3\lib\site-packages\pandas\plotting\_core.py in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2739 colormap=colormap, table=table, yerr=yerr,
2740 xerr=xerr, label=label, secondary_y=secondary_y,
-> 2741 **kwds)
2742 __call__.__doc__ = plot_series.__doc__
2743
~\Anaconda3\lib\site-packages\pandas\plotting\_core.py in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2000 yerr=yerr, xerr=xerr,
2001 label=label, secondary_y=secondary_y,
-> 2002 **kwds)
2003
2004
~\Anaconda3\lib\site-packages\pandas\plotting\_core.py in _plot(data, x, y, subplots, ax, kind, **kwds)
1802 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
1803
-> 1804 plot_obj.generate()
1805 plot_obj.draw()
1806 return plot_obj.result
~\Anaconda3\lib\site-packages\pandas\plotting\_core.py in generate(self)
256 def generate(self):
257 self._args_adjust()
--> 258 self._compute_plot_data()
259 self._setup_subplots()
260 self._make_plot()
~\Anaconda3\lib\site-packages\pandas\plotting\_core.py in _compute_plot_data(self)
371 if is_empty:
372 raise TypeError('Empty {0!r}: no numeric data to '
--> 373 'plot'.format(numeric_data.__class__.__name__))
374
375 self.data = numeric_data
TypeError: Empty 'DataFrame': no numeric data to plot
```
#### Expected Output
```python-traceback
TypeError: no numeric data to plot
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 4 | 2019-02-28T19:47:48Z | 2019-03-04T08:42:58Z | 2019-03-01T12:57:50Z | CONTRIBUTOR | null | - [X] closes #25480
- [X] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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} | 4 | 2019-02-28T20:01:17Z | 2019-03-05T12:24:07Z | 2019-03-03T01:46:08Z | MEMBER | null | xref #24332
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} | 1 | 2019-02-28T20:28:53Z | 2019-04-12T13:12:04Z | 2019-04-12T13:12:04Z | MEMBER | null | see also #5289
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
data = {'A': [np.nan, np.nan, np.nan, 0, 1, 2, 3, 4, 5, 6],
'B': [0, 1, 2, np.nan, np.nan, np.nan, 3, 4, 5, 6],
'C': np.arange(10, dtype=np.float64),
'D': [0, 1, 2, 3, 4, 5, np.nan, np.nan, np.nan, np.nan]}
dates = pd.bdate_range('1/1/2011', periods=10)
float_frame = pd.SparseDataFrame(data, index=dates, default_kind='block')
N = len(float_frame)
float_frame['foo'] = np.random.randn(N - 1)
```
#### Problem description
```python-traceback
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-8-0a4f051f0d7a> in <module>
8 float_frame = pd.SparseDataFrame(data, index=dates, default_kind='block')
9 N = len(float_frame)
---> 10 float_frame['foo'] = np.random.randn(N - 1)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __setitem__(self, key, value)
3117 else:
3118 # set column
-> 3119 self._set_item(key, value)
3120
3121 def _setitem_slice(self, key, value):
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _set_item(self, key, value)
3192
3193 self._ensure_valid_index(value)
-> 3194 value = self._sanitize_column(key, value)
3195 NDFrame._set_item(self, key, value)
3196
~\Anaconda3\lib\site-packages\pandas\core\sparse\frame.py in _sanitize_column(self, key, value, **kwargs)
418 else:
419 if len(value) != len(self.index):
--> 420 raise AssertionError('Length of values does not match '
421 'length of index')
422 clean = sp_maker(value)
AssertionError: Length of values does not match length of index
```
#### Expected Output
```python-traceback
ValueError: Length of values does not match length of index
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 2 | 2019-02-28T21:21:37Z | 2019-03-03T01:44:23Z | 2019-03-03T01:44:23Z | NONE | null | #### Code Sample:
```python
import numpy as np
import pandas as pd
# construct IntervalIndex from timestamps
# --> Length of index is > 100, the default leaf_size
# --> index is heavily skewed towards Timstamp.max
idx = pd.IntervalIndex.from_arrays(pd.date_range('2018-01-01', '2018-12-31', freq='D'), [pd.Timestamp.max]*365)
# type conversion from IntervalIndex._engine
left = idx._maybe_convert_i8(idx.left)
right = idx._maybe_convert_i8(idx.right)
# pivot point calculation from IntervalNode constructor, when n_elements > leaf_size
pivot = np.median(left + right) / 2
print(pd.Timestamp(pivot))
# >>> 1848-02-11 00:06:21.572612096
# This is below the minimum left-side value, which should not be
print(pd.Timestamp(np.median(idx.mid.asi8)))
# >>> 2140-05-21 23:53:38.427387904
# This is the correct midpoint
# NB: with my own data, accessing `mid` throws OverflowError: Overflow in int64 addition
# proposed alternate logic for midpoint
alternate = np.median(left/2 + right/2)
print(pd.Timestamp(alternate)
# >>> 2140-05-21 23:53:38.427387904
# Matches the correct midpoint, and should never over/under flow
```
#### Description
Current behavior causes a hard crash in the python process under certain conditions:
- Length of the index is > 100, the default leaf_size of the IntervalTree
- The intervals are heavily skewed towards the upper bound of the type (for signed types I believe this can also be the lower bound, but I haven't verified it)
Having an index length greater than the leaf_size parameter, causes the the IndexNode constructor to create child nodes. Currently the default leaf_size is 100 and is not exposed to the user API. Instantiating an IntervalTree directly and setting the leaf_size > index length does not lead to a crash.
By emulating the `IntervalNode.classify_intervals` method in the REPL, I found that the entire index was assigned to the right-hand child node, because the pivot was calculated on an array of overflowed integers, and that the constructor would never stop recursing.
As an *additional* note, I also encountered an `OverflowError` when accessing the `mid` attribute of the `IntervalIndex` with my own data, but this does not occur in the code sample above. I traced it back through the `length` attribute, where left bounds are subtracted from right bounds.
```python
idx.right - idx.left
# >>> Traceback (most recent call last):
# >>> File "<input>", line 1, in <module>
# >>> File "~\pandas\core\indexes\datetimelike.py", line 501, in __sub__
# >>> result = self._data.__sub__(maybe_unwrap_index(other))
# >>> File "~\pandas\core\arrays\datetimelike.py", line 1275, in __sub__
# >>> result = self._sub_datetime_arraylike(other)
# >>> File "~\pandas\core\arrays\datetimes.py", line 724, in _sub_datetime_arraylike
# >>> arr_mask=self._isnan)
# >>> File "~\pandas\core\algorithms.py", line 938, in checked_add_with_arr
# >>> raise OverflowError("Overflow in int64 addition")
# >>> OverflowError: Overflow in int64 addition
```
#### Expected Output
Changing the pivot calculation from
`self.pivot = np.median(left + right) / 2`
to
`self.pivot = np.median(left/2 + right/2)`
should protect against over and underflows.
#### Output of ``pd.show_versions()``
<details>
<pre>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
------------------
pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 40.2.0
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
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: 1.1.13
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</pre>
</details>
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} | 1 | 2019-02-28T22:00:57Z | 2019-03-01T17:57:42Z | 2019-03-01T17:57:39Z | MEMBER | null | This manifested itself in #25419 - when trying to build documentation the version of pandas that gets inserted into the globals of this module may have inadvertently been picked up from another install on the system. This prioritizes the version of pandas dictated by command line args | {
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} | 12 | 2019-02-28T22:21:50Z | 2019-06-30T20:53:06Z | 2019-06-30T20:53:06Z | MEMBER | null | From a discussion on Gitter with @TomAugspurger when following the pandas contributing guide the step:
```sh
conda env create -f environment.yml
```
Causes pandas to be installed in the environment from conda as a result of dependency resolution with third party libraries (ex: statsmodels, pyarrow, seaborn).
To prevent undesired conflicts between the local development version and the installed version we should update the documentation to have an explicit removal of the bundled version as such:
```sh
conda uninstall --force pandas
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] | closed | false | null | [] | null | 9 | 2019-02-28T23:31:01Z | 2019-05-03T05:30:44Z | 2019-05-03T05:30:44Z | CONTRIBUTOR | null | closes #19129
closes #22525
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"de... | closed | false | null | [] | null | 6 | 2019-02-28T23:38:08Z | 2019-03-01T19:06:48Z | 2019-03-01T17:40:02Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
| {
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https://api.github.com/repos/pandas-dev/pandas/issues/25490 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25490/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25490/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25490/events | https://github.com/pandas-dev/pandas/pull/25490 | 415,878,589 | MDExOlB1bGxSZXF1ZXN0MjU3MjcyNDc3 | 25,490 | DOC: Add conda uninstall pandas to contributing guide | {
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} | 3 | 2019-02-28T23:58:46Z | 2019-03-03T20:30:19Z | 2019-03-03T20:30:19Z | CONTRIBUTOR | null | closes https://github.com/pandas-dev/pandas/issues/25487
add command
`conda uninstall --force pandas` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25491 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25491/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25491/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25491/events | https://github.com/pandas-dev/pandas/pull/25491 | 415,902,902 | MDExOlB1bGxSZXF1ZXN0MjU3MjkwOTg2 | 25,491 | DOC: Reword Series.interpolate docstring for clarity | {
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} | 10 | 2019-03-01T01:44:41Z | 2019-03-01T17:21:11Z | 2019-03-01T17:21:03Z | CONTRIBUTOR | null | - [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The description of one group of methods in the `Series.interpolate` docstring currently reads:
> ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘spline’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=4). These use the numerical values of the index.
In this item, it is unclear whether "These" in the final sentence refers to the 'polynomial' and 'spline' methods, or all eight methods in the group. This ambiguity was the cause of confusion in issue #21662, for example. This PR reorders the sentences for improved clarity. | {
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} | 0 | 2019-03-01T02:56:24Z | 2019-03-01T12:47:30Z | 2019-03-01T12:47:30Z | MEMBER | null | As noted by @TomAugspurger [here](https://github.com/pandas-dev/pandas/pull/25491#issuecomment-468519258) looks like builds started failing today. I think this has to do with the upcoming DST transition.
cc @mroeschke
```sh
=================================== FAILURES ===================================
_______________ TestDatetimeIndex.test_stringified_slice_with_tz _______________
[gw0] darwin -- Python 3.5.6 /Users/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.datetimes.test_datetime.TestDatetimeIndex object at 0x13accba58>
def test_stringified_slice_with_tz(self):
# GH#2658
import datetime
start = datetime.datetime.now()
> idx = date_range(start=start, freq="1d", periods=10, tz='US/Eastern')
pandas/tests/indexes/datetimes/test_datetime.py:105:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/core/indexes/datetimes.py:1551: in date_range
closed=closed, **kwargs)
pandas/core/arrays/datetimes.py:454: in _generate_range
tz, ambiguous=ambiguous, nonexistent=nonexistent)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise pytz.NonExistentTimeError(stamp)
E pytz.exceptions.NonExistentTimeError: 2019-03-10 02:01:53.911138
pandas/_libs/tslibs/conversion.pyx:1100: NonExistentTimeError
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25493 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25493/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25493/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25493/events | https://github.com/pandas-dev/pandas/issues/25493 | 415,925,793 | MDU6SXNzdWU0MTU5MjU3OTM= | 25,493 | module 'pandas.tseries' has no attribute 'index' | {
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python:3.6.8
tstables 0.0.15
pandas:0.24.1
#### code
h5.close()
import tstables as tstab
class ts_desc(tb.IsDescription):
timestamp = tb.Int64Col(pos=0)
ts1 = tb.Float64Col(pos=1)
ts2 = tb.Float64Col(pos=2)
ts3 = tb.Float64Col(pos=3)
h5 = tb.open_file(path + 'tstab.h5', 'w')
ts = h5.create_ts('/', 'ts', ts_desc)
%time ts.append(df)
#### Problem description
AttributeError Traceback (most recent call last)
<timed eval> in <module>
c:\users\admin\.virtualenvs\python_for_finance_mastering_data_driven_f-hf8uuk7i\lib\site-packages\tstables\tstable.py in append(self, rows, convert_strings)
237 if rows.empty:
238 return # Do nothing if we are appending nothing
--> 239 if rows.index.__class__ != pandas.tseries.index.DatetimeIndex:
240 raise ValueError('when rows is a DataFrame, the index must be a DatetimeIndex.')
241
AttributeError: module 'pandas.tseries' has no attribute 'index'
I can not solve it, i hope your help, thanks.
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} | 1 | 2019-03-01T03:59:37Z | 2019-03-01T14:58:49Z | 2019-03-01T14:58:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
tttt = pd.DataFrame([5,6,7])
tttt.apply(str)
```
#### Problem description
Output is very strange:
0 0 5\n1 6\n2 7\nName: 0, dtype: int64
dtype: object
#### Expected Output
DataFrame of strings
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-139-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 18.0
setuptools: 24.3.0
Cython: 0.28.3
numpy: 1.13.0
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.5.0
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.9999999
sqlalchemy: 1.2.10
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25495 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25495/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25495/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25495/events | https://github.com/pandas-dev/pandas/issues/25495 | 415,947,681 | MDU6SXNzdWU0MTU5NDc2ODE= | 25,495 | Unexpected dtype when using .loc to set Categorical value for column in 1-row DataFrame | {
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} | 2 | 2019-03-01T05:27:46Z | 2020-01-28T15:29:53Z | 2020-01-28T15:29:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'Alpha': [u'a'], 'Numeric': [0]})
In [3]: df.loc[:,'Alpha']
Out[3]:
0 a
Name: Alpha, dtype: object
In [4]: codes = pd.Categorical(df['Alpha'], categories = [u'a',u'b',u'c'])
In [5]: codes
Out[5]:
[a]
Categories (3, object): [a, b, c]
In [6]: df.loc[:,'Alpha'] = codes
In [7]: df.loc[:,'Alpha']
Out[7]:
0 a
Name: Alpha, dtype: object
```
#### Problem description
When I try to set the column of a one-row DataFrame to a `pandas.core.arrays.categorical.Categorical`, it is returned as a `pandas.core.series.Series` of `dtype('O')` rather than a `pandas.core.series.Series` of `CategoricalDtype(categories=[u'a', u'b', u'c'], ordered=False)`. I get the latter return value when I set the column using `df['Alpha'] = codes` or `df.Alpha = codes`. I can't replicate this inconsistency with DataFrames containing more than one row.
#### Expected Output
```python
Out[7]:
0 a
Name: Alpha, dtype: category
Categories (3, object): [a, b, c]
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.6.3
Cython: None
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
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
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-03-01T06:11:51Z | 2019-03-01T16:10:39Z | 2019-03-01T12:47:31Z | MEMBER | null | - [x] closes #25492
- [x] tests added / passed
This test shouldn't need to depend on `datetime.now`
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... | closed | false | null | [] | null | 4 | 2019-03-01T08:50:26Z | 2020-12-13T09:56:52Z | 2020-12-12T11:34:50Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
td = pd.Timedelta('1 days')
dti = pd.date_range('20130101', periods=3, name='bar')
td - dti
```
#### Problem description
0.25.0.dev0+179.g0a61ecdf6
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexes\datetimelike.py", line 507, in __rsub__
result = self._data.__rsub__(maybe_unwrap_index(other))
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\arrays\datetimelike.py", line 1325, in __rsub__
return -(self - other)
TypeError: bad operand type for unary -: 'DatetimeArray'
```
#### Expected Output
same as 0.23.4
```python-traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-2a4f19a48f3e> in <module>
3 td = pd.Timedelta('1 days')
4 dti = pd.date_range('20130101', periods=3, name='bar')
----> 5 td - dti
~\Anaconda3\lib\site-packages\pandas\core\indexes\datetimelike.py in __rsub__(self, other)
949 from pandas import DatetimeIndex
950 return DatetimeIndex(other) - self
--> 951 return -(self - other)
952 cls.__rsub__ = __rsub__
953
~\Anaconda3\lib\site-packages\pandas\core\ops.py in invalid_op(self, other)
174 def invalid_op(self, other=None):
175 raise TypeError("cannot perform {name} with this index type: "
--> 176 "{typ}".format(name=name, typ=type(self).__name__))
177
178 invalid_op.__name__ = name
TypeError: cannot perform __neg__ with this index type: DatetimeIndex
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 0a61ecdf6b4ea61a67afb4e3862df79adc07053a
python: 3.7.2.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: en_US.UTF-8
LOCALE: None.None
pandas: 0.25.0.dev0+179.g0a61ecdf6
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.15.4
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-03-01T08:53:55Z | 2019-03-06T01:44:31Z | 2019-03-03T01:44:23Z | MEMBER | null | - [X] closes #25485
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
Credit to @kingsykes for identifying the issue and determining the fix. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25499 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25499/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25499/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25499/events | https://github.com/pandas-dev/pandas/issues/25499 | 416,001,346 | MDU6SXNzdWU0MTYwMDEzNDY= | 25,499 | BUG: Overflow in IntervalIndex/IntervalArray mid/length | {
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... | open | false | null | [] | null | 0 | 2019-03-01T08:54:17Z | 2021-06-27T18:51:32Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
xref #25485
```python
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '0.25.0.dev0+180.g016141d'
In [2]: iv = pd.Interval(pd.Timestamp.min, pd.Timestamp.max)
In [3]: ii = pd.IntervalIndex([iv])
In [4]: ii.length
---------------------------------------------------------------------------
OverflowError: Overflow in int64 addition
In [5]: ii.mid
---------------------------------------------------------------------------
OverflowError: Overflow in int64 addition
```
Note that these operations work on the `Interval` object itself, albeit with the length being cast to `datetime.timedelta`:
```python
In [6]: iv.length
Out[6]: datetime.timedelta(213503, 84873, 709550)
In [7]: iv.mid
Out[7]: Timestamp('1970-01-01 00:00:00')
```
More generally, this can silently occur with integer endpoints. For example with `mid`:
```python
In [8]: ii64 = np.iinfo(np.int64)
In [9]: iv2 = pd.Interval(ii64.max - 1, ii64.max)
In [10]: ii2 = pd.IntervalIndex([iv2])
In [11]: ii2.mid
Out[11]: Float64Index([-1.5], dtype='float64')
In [12]: iv2.mid
Out[12]: 9.223372036854776e+18
```
And with `length`:
```python
In [13]: iv3 = pd.Interval(ii64.min, ii64.max)
In [14]: ii3 = pd.IntervalIndex([iv3])
In [15]: ii3.length
Out[15]: Int64Index([-1], dtype='int64')
In [16]: iv3.length
Out[16]: 18446744073709551615
```
Note that all examples above have the same behavior with `IntervalArray` in place of `IntervalIndex`.
#### Problem description
`IntervalIndex` and `IntervalArray` implementations can cause an `OverflowError`, with the overflow sometimes occurring silently.
#### Expected Output
It might be possible to for `Out[5]` to return the correct value with a different implementation.
I'm not sure how much can be done for the other errors given that the correct values are out of bounds for their given dtype. For the integer examples it may be possible to get the correct answer by having the output be an array of `uint64` dtype , but I'm not sure off the top of my head how that would be implemented.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 016141d057cd828b42cfa8804d0c37f712b6ed24
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.29-galliumos
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+180.g016141d
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.28.3
numpy: 1.14.5
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: None
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25500/events | https://github.com/pandas-dev/pandas/issues/25500 | 416,064,987 | MDU6SXNzdWU0MTYwNjQ5ODc= | 25,500 | (api) function names: pct_change, diff | {
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"default": f... | open | false | null | [] | null | 3 | 2019-03-01T11:40:29Z | 2021-06-27T18:52:46Z | null | NONE | null | Hi, first of all thanks for the amazing library everyone!
This is a minor issue I'm sure, but I find it impossible to remember the name of the `.pct_change()` function.
Partly this is because of confusion with the existing .diff() function which is easy to remember - I always try things like:
- pctdiff()
- pct_diff()
- percent_diff()
Normally after a couple of goes I admit defeat and Google it yet again :)
So this is a request for an additional function with the name `.pct_diff()` (or whatever spelling is preferred) that replicates/calls `.pct_change()`, in order to standardise the API.
Thanks! | {
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"... | open | false | null | [] | null | 0 | 2019-03-01T12:23:17Z | 2021-06-27T18:54:51Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import sys
if sys.version_info[0] < 3:
from StringIO import StringIO
else:
from io import StringIO
import pandas as pd
import csv
escape_char_defined = '\\'
quote_defined = '"'
separator = "|"
sample_data = []
for i in range(1,11):
sample_data.append(i*escape_char_defined + quote_defined)
initial_df = pd.DataFrame(sample_data,columns=['column'])
csv_text = initial_df.to_csv(sep=separator,columns=None,header=None,index=False,doublequote=False,quoting=csv.QUOTE_ALL,quotechar=quote_defined,escapechar=escape_char_defined,encoding='utf-8')
csv_text = StringIO(csv_text)
final_df = pd.read_csv(csv_text,sep=separator,escapechar=escape_char_defined,quoting=csv.QUOTE_ALL,header=None,doublequote=False,encoding='utf-8')
if not final_df.equals(initial_df):
raise Exception("Dataframes are not equal!")
```
#### Problem description
When defining several strings with an escape char before the quotechar, the same CSV generated by pandas is not read properly when transforming back to a pandas dataframe.
#### Expected Output
The dataframe written should be equal to the dataframe read.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.1
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.5.12
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.1.18
pymysql: None
psycopg2: None
jinja2: 2.8.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 10 | 2019-03-01T13:42:30Z | 2019-04-12T13:13:07Z | 2019-04-12T13:12:04Z | MEMBER | null | - [ ] closes #25484
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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xref #24332 | {
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} | 2 | 2019-03-01T14:43:42Z | 2019-03-01T17:47:46Z | 2019-03-01T16:52:07Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/25452#issuecomment-468654194
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} | 1 | 2019-03-01T15:06:16Z | 2019-03-05T21:11:49Z | 2019-03-05T21:11:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pytz
import pandas
series = pd.Series(pd.Timestamp('2017-08-05 00:00:00+0100', tz=pytz.FixedOffset(60)))
series.at[1] = pd.Timestamp('2017-08-05 00:00:00+0100', tz=pytz.FixedOffset(60))
# this produces
TypeError: Argument 'arr' has incorrect type (expected numpy.ndarray, got DatetimeArray)
```
#### Problem description
I would expect to be able to operate on a series of timestamps
#### Expected Output
no error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: 0.11.3
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: 0.9.0
pandas_datareader: None
</details>
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} | 1 | 2019-03-01T16:09:21Z | 2020-10-30T22:48:54Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import time
squares = set(a**2 for a in range(100000000))
series = pd.Series(range(100))
start = time.time()
apply_result = series.apply(lambda x: x in squares)
apply_end = time.time()
isin_result = series.isin(squares)
isin_end = time.time()
assert((apply_result==isin_result).all())
print("pandas.Series.apply() took {} seconds and pandas.Series.isin() took {} seconds.".format(apply_end - start, isin_end - apply_end))
```
Output:
> pandas.Series.apply() took 0.0044422149658203125 seconds and pandas.Series.isin() took 72.23143887519836 seconds.
#### Problem description
When a set is passed to `pandas.Series.isin`, the set is [converted to a list](https://github.com/pandas-dev/pandas/blob/37da2c100fd09cbe8f98c78c642a978c3fae98c9/pandas/core/algorithms.py#L394), before being converted back to a hash table. Consequently, the run time is linear in the size of the set, which is not ideal because one of the main reasons to use a set is that membership can be tested in constant time.
#### Suggested improvements
The quick and dirty workaround is to use `pandas.Series.apply` (as in the above code sample) instead of `pandas.Series.isin`. I'm not familiar enough with pandas internals to know whether there are edge cases where this would fail or whether it would be a bad idea to incorporate this workaround into `isin` directly. I would suggest, however, that at a minimum the documentation for `isin` be updated to mention that a set will be converted to a list and that this has performance implications, so that users can choose an alternative approach. (I am happy to contribute the documentation if this is the preferred solution.)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 3.0.5
pip: 19.0.3
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.16.2
scipy: 0.18.1
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
feather: None
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml.etree: 3.7.2
bs4: 4.5.3
html5lib: 0.9999999
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-03-01T16:33:27Z | 2020-04-01T04:22:02Z | null | NONE | null | The following code fails on 0.24.1, doesn't fail on 0.23.4 but result was wrong.
```python
from datetime import datetime, timezone, timedelta
start = datetime(2019, 1, 1, tzinfo=timezone.utc)
df = pd.DataFrame.from_records(
{
"date": [start + timedelta(days=i) for i in range(5)],
"value": list(range(5)),
"ref_date": [start + timedelta(days=i) for i in range(5)],
}
)
df.set_index("date").resample("1H").interpolate("linear")
```
Things work again if:
- one makes `start` tz-naive;
- one removes column `ref_date`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25509 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25509/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25509/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25509/events | https://github.com/pandas-dev/pandas/issues/25509 | 416,209,839 | MDU6SXNzdWU0MTYyMDk4Mzk= | 25,509 | Dir fails on dataframes with pathological column names | {
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} | 7 | 2019-03-01T17:42:53Z | 2020-03-19T19:50:39Z | 2020-03-19T19:50:39Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'\ud83d': []})
_ = dir(df)
```
```python-traceback
---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-3-4e949fe17c82> in <module>
----> 1 _ = dir(df)
~/miniconda/envs/dev/lib/python3.7/site-packages/pandas/core/accessor.py in __dir__(self)
37 """
38 rv = set(dir(type(self)))
---> 39 rv = (rv - self._dir_deletions()) | self._dir_additions()
40 return sorted(rv)
41
~/miniconda/envs/dev/lib/python3.7/site-packages/pandas/core/generic.py in _dir_additions(self)
5110 If info_axis is a MultiIndex, it's first level values are used.
5111 """
-> 5112 additions = {c for c in self._info_axis.unique(level=0)[:100]
5113 if isinstance(c, string_types) and isidentifier(c)}
5114 return super(NDFrame, self)._dir_additions().union(additions)
~/miniconda/envs/dev/lib/python3.7/site-packages/pandas/core/indexes/base.py in unique(self, level)
1999 if level is not None:
2000 self._validate_index_level(level)
-> 2001 result = super(Index, self).unique()
2002 return self._shallow_copy(result)
2003
~/miniconda/envs/dev/lib/python3.7/site-packages/pandas/core/base.py in unique(self)
1312 else:
1313 from pandas.core.algorithms import unique1d
-> 1314 result = unique1d(values)
1315
1316 return result
~/miniconda/envs/dev/lib/python3.7/site-packages/pandas/core/algorithms.py in unique(values)
360
361 table = htable(len(values))
--> 362 uniques = table.unique(values)
363 uniques = _reconstruct_data(uniques, dtype, original)
364 return uniques
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.StringHashTable.unique()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.StringHashTable._unique()
UnicodeEncodeError: 'utf-8' codec can't encode character '\ud83d' in position 0: surrogates not allowed
```
#### Problem description
Dir fails on dataframes with pathalogical column names
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 3.10.1
pip: 18.1
setuptools: 40.6.2
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.2.0
sphinx: 1.8.4
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.1.6
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"description": "rolling,... | open | false | null | [] | null | 0 | 2019-03-01T17:52:38Z | 2020-05-08T19:41:10Z | null | NONE | null | With a DatetimeIndex I can specify the rolling window using an offset alias, but if I want to skip the first (incomplete) window, I would need to calculate the number of periods in the window. Rolling is hence using different units for the window and min_period functionality. I would like to be able to skip all periods in the first n windows.
Possible implementations could be:
min_periods='7D'
min_periods=window
skip_windows=n
skip_window=True.
n=1 is probably good enough for most use cases.
#### Code Sample, a copy-pastable example if possible
```python
# Generate example dataframe:
idx = pd.date_range("2019-03-01", periods=10000, freq='5T')
df = pd.DataFrame(np.sin(np.arange(0,100,0.01)), index=idx)
# Plot data
plt.plot(df)
# Plot rolling mean with vertical offset for visual separation
plt.plot(df.rolling('7D').mean() + 0.2)
# Plot rolling mean with time offset equal to 1 window
periods = pd.to_timedelta('7D')//df.index.freq
plt.plot(df.rolling('7D', min_periods=periods).mean())
plt.show()
```
#### Problem description
'min_periods' accepts only integer values. A min_periods value less than the number of periods in the window is not representative as there are too few observations. The documentation is very confusing with respect to time series since the "offset" apparently does not refer to an offset alias: "For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window."
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.1
pytest: 3.5.1
pip: 18.1
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.2
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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"description": "on-merge: backport to 0.24.x",
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} | 4 | 2019-03-01T18:12:44Z | 2019-03-02T21:08:09Z | 2019-03-02T21:08:09Z | CONTRIBUTOR | null | looks like our builds are failing as we are now picking up numpy 1.16.1; I belive 1.16.2 fixes this, so once this is fully pushed should be ok.
These failures manifest with pytables. https://travis-ci.org/pandas-dev/pandas/jobs/500453356
cc @pandas-dev/pandas-core
let's hold off merging for now / we could simply pin numpy=1.15.* as well (again for now), if someone wants to do this. | {
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} | 2 | 2019-03-01T19:23:06Z | 2019-06-01T14:45:07Z | 2019-06-01T14:45:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# Doesn't work
pd.to_datetime(pd.Series([pd.np.nan, '19750501', '19820001', '19770501']), format='%Y%m%d', errors='coerce')
pd.to_datetime(pd.Series(['19750501', pd.np.nan, '19820001', '19770501']), format='%Y%m%d', errors='coerce')
# Works
pd.to_datetime(pd.Series(['19750501', '19820001', '19770501']), format='%Y%m%d', errors='coerce')
# Works
pd.to_datetime(pd.Series(['19750501', '19820001', pd.np.nan, '19770501']), format='%Y%m%d', errors='coerce')
```
#### Problem description
with errors='ignore' or 'coerce', pandas should be able to ignore the wrong datetime '19820001' in it. However, if there is NaN before the wrong datetime, pandas returns error "OverflowError: signed integer is less than minimum"
It only happens when format %Y%m%d
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.17
pymysql: 0.9.3
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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