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https://api.github.com/repos/pandas-dev/pandas/issues/9402 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9402/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9402/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9402/events | https://github.com/pandas-dev/pandas/issues/9402 | 56,397,484 | MDU6SXNzdWU1NjM5NzQ4NA== | 9,402 | DataFrame to_latex does not present or pass through column_format to formatter | {
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`column_format` keyword arguments to `df.to_latex` should honour the relevant options available and implemented in `DataFrameFormatter` and it's `to_latex` method
The reason why this is annoying is that default `frmt` behaviour is to justify based on the column datatype. For cases where the inde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9403 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9403/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9403/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9403/events | https://github.com/pandas-dev/pandas/pull/9403 | 56,401,566 | MDExOlB1bGxSZXF1ZXN0Mjg1NjY3Nzc= | 9,403 | ENH: - Fixed column_format propagation to DataFrameFormatter in to_latex | {
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... | 3 | 2015-02-03T16:32:51Z | 2015-07-28T21:52:18Z | 2015-07-28T21:52:18Z | NONE | null | closes #9402
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https://api.github.com/repos/pandas-dev/pandas/issues/9404 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9404/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9404/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9404/events | https://github.com/pandas-dev/pandas/issues/9404 | 56,410,958 | MDU6SXNzdWU1NjQxMDk1OA== | 9,404 | Panel.to_excel fails when axis 0 contains unicode strings | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2015-02-03T17:40:13Z | 2018-07-06T20:21:07Z | 2017-07-11T09:56:04Z | NONE | null | Hello,
while exporting a panel with unicode strings in the items axis `to_excel` fails with a `UnicodeEncodeError`. The following code reproduces the error:
``` python
import pandas as pd
p = pd.Panel({u'Smørrebrød': pd.DataFrame({})})
p.to_excel('nope.xlsx')
```
I suspect that changing `name = str(item)` to `name = ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9405 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9405/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9405/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9405/events | https://github.com/pandas-dev/pandas/issues/9405 | 56,413,420 | MDU6SXNzdWU1NjQxMzQyMA== | 9,405 | 0.15.2 to_datetime broken | {
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] | closed | false | null | [] | null | 7 | 2015-02-03T17:59:17Z | 2015-02-04T13:48:39Z | 2015-02-03T22:42:55Z | NONE | null | It looks like the support for the new 'exact' keyword has borked to_datetime() for format strings other than '%Y%m%d' as it is passed to array_strptime which doesn't seem to support it:
```
ts = 201502031753
pd.to_datetime(ts, format='%Y%m%d%H%M')
----------------------------------------------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9406 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9406/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9406/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9406/events | https://github.com/pandas-dev/pandas/issues/9406 | 56,418,612 | MDU6SXNzdWU1NjQxODYxMg== | 9,406 | BUG: NaN level values in stack() and unstack() | {
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"followers_url": "https://api.github.com/users/j... | 5 | 2015-02-03T18:42:11Z | 2021-05-10T21:45:32Z | 2021-05-10T21:45:32Z | CONTRIBUTOR | null | As described in https://github.com/pydata/pandas/pull/9023#issuecomment-72707057, the way `DataFrame.stack()` and `DataFrame.unstack()` treat `NaN` indices is rather odd/inconsistent. Despite passing `test_unstack_nan_index()` in `test_frame.py`, I observe the following (this is from 0.15.2, but I think it's unchanged... | {
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"de... | closed | false | null | [] | null | 1 | 2015-02-03T20:24:32Z | 2015-02-03T22:42:29Z | 2015-02-03T22:42:17Z | NONE | null | Ravel doesn't seem to flatten lists in a series.
``` python
import pandas as pd
df = pd.DataFrame({ 'data':[[1,2],3]})
df['data'].ravel()
Out[3]: array([[1, 2], 3], dtype=object)
df.data.values.ravel()
Out[4]: array([[1, 2], 3], dtype=object)
```
Or, in iPython Notebook
![screen shot 2015-02-03 at 1 55 54 pm](ht... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9408/events | https://github.com/pandas-dev/pandas/issues/9408 | 56,446,496 | MDU6SXNzdWU1NjQ0NjQ5Ng== | 9,408 | date_range performance | {
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_0.11.0:_
```
%timeit pandas.date_range(start='1/1/1995', end='2/3/2015', freq='B')
1000 loops, best of 3: 412 us per loop
```
_0.15.2:_
```
%timeit pandas.date_r... | {
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... | 3 | 2015-02-03T23:09:29Z | 2015-07-28T21:51:06Z | 2015-07-28T21:51:06Z | NONE | null | closes issue #9404
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https://api.github.com/repos/pandas-dev/pandas/issues/9410 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9410/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9410/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9410/events | https://github.com/pandas-dev/pandas/issues/9410 | 56,458,718 | MDU6SXNzdWU1NjQ1ODcxOA== | 9,410 | Error: “pandas.hashtable.PyObjectHashTable.get_item” | {
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First, I import the data with no issues:
import pandas as pd
hourly = pd.read_csv("fremont_bridge_data.csv", index_col='Date', parse_dates=True)
weekly = hourly.resample('w','sum')
But when trying to access, I run into some odd behavior. This works fi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9411 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9411/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9411/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9411/events | https://github.com/pandas-dev/pandas/pull/9411 | 56,469,499 | MDExOlB1bGxSZXF1ZXN0Mjg2MDc1OTk= | 9,411 | test sql table name | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-02-04T02:02:53Z | 2015-02-10T16:58:17Z | 2015-02-08T11:26:44Z | CONTRIBUTOR | null | Closes #9393
simple test for tables names that need to be quoted.
This is related to issue [here](https://github.com/pydata/pandas/issues/9393) that is fixed in current master.
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https://api.github.com/repos/pandas-dev/pandas/issues/9412 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9412/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9412/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9412/events | https://github.com/pandas-dev/pandas/issues/9412 | 56,554,767 | MDU6SXNzdWU1NjU1NDc2Nw== | 9,412 | Vectorizing pandas Date Functions | {
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"gi... | [] | closed | false | null | [] | null | 2 | 2015-02-04T16:58:14Z | 2015-02-04T17:29:32Z | 2015-02-04T17:29:32Z | NONE | null | pandas has great methods for manipulating index dates, which works well for timeseries data. But for other kinds of data, to manipulate a date column you have to use lambda (or set that column to be the index). For example, a lot of the beginners I'm training work with transactional data that looks something like this... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9413 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9413/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9413/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9413/events | https://github.com/pandas-dev/pandas/issues/9413 | 56,564,911 | MDU6SXNzdWU1NjU2NDkxMQ== | 9,413 | BUG: pandas rolling_quantile does not use interpolation | {
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"closed_at": "2017-10-28T18:54:27Z",
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```
>> row = 10
>> col = 5
>> idx = pd.date_range(20100101,periods=row,freq='B')
>> a = pd.DataFrame(np.random.rand(row*col).reshape((row,-1)),index=idx)
>> a
0 1 2 3 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9414 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9414/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9414/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9414/events | https://github.com/pandas-dev/pandas/pull/9414 | 56,587,500 | MDExOlB1bGxSZXF1ZXN0Mjg2NzQ5MjU= | 9,414 | hdfs workflow for cookbook | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9415 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9415/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9415/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9415/events | https://github.com/pandas-dev/pandas/pull/9415 | 56,591,009 | MDExOlB1bGxSZXF1ZXN0Mjg2NzcyMTE= | 9,415 | BUG: setup.py does not allow equal filenames in *different* directories | {
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"d... | closed | false | null | [] | null | 4 | 2015-02-04T21:34:41Z | 2015-02-11T14:45:10Z | 2015-02-11T14:27:56Z | CONTRIBUTOR | null | Is it possible to write tests for setup.py?
## Setup
```
touch pandas/period.pyx
```
and put this in your `setup.py`'s ext_data:
``` python
period=dict(pyxfile='period'),
```
## Before PR
``` bash
python setup.py build_ext --inplace
rm -rf pandas/period.c
python setup.py build_ext --inplace
python setup.py clea... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9416 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9416/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9416/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9416/events | https://github.com/pandas-dev/pandas/issues/9416 | 56,596,430 | MDU6SXNzdWU1NjU5NjQzMA== | 9,416 | Series.shift() doesn't work for categorical type | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-04T22:17:39Z | 2015-07-18T00:03:27Z | 2015-07-18T00:03:27Z | NONE | null | Not sure if this is intentional, but Series.shift() won't run with categorical dtypes:
``` python
ser = pd.Series(['a', 'b', 'c', 'd'], dtype="category")
ser.shift(1)
Traceback (most recent call last):
File "<ipython-input-15-1a7536b0af06>", line 1, in <module>
ser.shift(1)
File "/.../pandas/core/generic.py"... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9417 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9417/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9417/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9417/events | https://github.com/pandas-dev/pandas/pull/9417 | 56,597,972 | MDExOlB1bGxSZXF1ZXN0Mjg2ODE2MjA= | 9,417 | improved error message for invalid chart types | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-02-04T22:30:22Z | 2015-08-15T12:37:55Z | 2015-02-08T13:35:47Z | CONTRIBUTOR | null | Closes #9400
`pd.Series([2,3,4]).plot("quiver")` now gives an improved error message:
```
Users/ch/miniconda/envs/pd_dev34/lib/python3.4/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2269 klass = _plot_klass[kind]
2270 else:
-> 2271 raise ValueError(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9418 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9418/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9418/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9418/events | https://github.com/pandas-dev/pandas/pull/9418 | 56,673,826 | MDExOlB1bGxSZXF1ZXN0Mjg3MjY1MzI= | 9,418 | DOC: Fix release note format | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9420 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9420/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9420/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9420/events | https://github.com/pandas-dev/pandas/issues/9420 | 56,698,582 | MDU6SXNzdWU1NjY5ODU4Mg== | 9,420 | precision issue with rolling_std | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9421 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9421/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9421/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9421/events | https://github.com/pandas-dev/pandas/issues/9421 | 56,703,567 | MDU6SXNzdWU1NjcwMzU2Nw== | 9,421 | Complete rewrite of bottleneck package | {
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"c... | closed | false | null | [] | null | 2 | 2015-02-05T17:43:49Z | 2016-01-07T00:31:10Z | 2016-01-07T00:31:10Z | NONE | null | I have completely rewritten the bottleneck package. Since it is an optional dependency of pandas I thought you might want to test it before I release.
Release notes are here: https://github.com/kwgoodman/bottleneck/blob/master/RELEASE.rst
Branch: master
Please report results here: https://github.com/kwgoodman/bottle... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9422 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9422/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9422/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9422/events | https://github.com/pandas-dev/pandas/issues/9422 | 56,707,245 | MDU6SXNzdWU1NjcwNzI0NQ== | 9,422 | API: sum of Series of all NaN should return 0 or NaN ? | {
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"color": "AD7FA8"... | closed | true | null | [] | {
"closed_at": "2017-10-28T18:54:27Z",
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The question is what the sum of a Series of all NaNs should return (which is equivalent to an empty Series after skipping the NaNs): NaN or 0?
```
In [1]: s = Series([np.nan])
In [2]: s.sum(skipna=True) # skipping NaNs is the default
Out[2]: nan or 0 <---- DISCUSSION POINT
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9423 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9423/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9423/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9423/events | https://github.com/pandas-dev/pandas/issues/9423 | 56,716,550 | MDU6SXNzdWU1NjcxNjU1MA== | 9,423 | to_datetime parsing odd strings to dates | {
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``` python
pd.to_datetime('0067000000M2M3HAAV')
Timestamp('2015-02-05 00:02:03')
```
Not sure how this matches to 2015! But this is fine:
``` python
pd.to_datetime('0067000000M2LkvAAF')... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9424 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9424/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9424/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9424/events | https://github.com/pandas-dev/pandas/issues/9424 | 56,738,943 | MDU6SXNzdWU1NjczODk0Mw== | 9,424 | read_csv clobbers values of columns with duplicate names | {
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"... | closed | false | null | [] | {
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I don't expect this is the correct behavior, although it's always possible I'm doing something wrong. Importing data using the `names` keyword will clobber the values of columns where the name is duplicated. For example:
``` python
from StringIO import StringIO
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https://api.github.com/repos/pandas-dev/pandas/issues/9425 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9425/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9425/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9425/events | https://github.com/pandas-dev/pandas/issues/9425 | 56,742,311 | MDU6SXNzdWU1Njc0MjMxMQ== | 9,425 | 'infer_freq' can return timerule (WOM-5MON) that 'to_offset' does not support | {
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"followers_url": "https://api.github.... | 3 | 2015-02-05T22:47:55Z | 2015-06-02T19:25:19Z | 2015-05-30T21:28:14Z | NONE | null | ``` python
import pandas as pd
index = [pd.Timestamp('2014-03-31 00:00:00'),
pd.Timestamp('2014-06-30 00:00:00'),
pd.Timestamp('2015-03-30 00:00:00'),
pd.Timestamp('2015-06-29 00:00:00')]
freq = pd.infer_freq(index) # WOM-5MON
pd.datetools.to_offset(freq) # ValueError: Could not evaluate WO... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9426 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9426/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9426/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9426/events | https://github.com/pandas-dev/pandas/issues/9426 | 56,742,972 | MDU6SXNzdWU1Njc0Mjk3Mg== | 9,426 | Merge fails when dataframe contains categoricals | {
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"followers_url": "https://api.github.com/users/jr... | 13 | 2015-02-05T22:54:07Z | 2015-05-20T11:59:03Z | 2015-03-06T03:18:10Z | NONE | null | Trying to perform a left merge between two dataframes using a column of type object. If I include categoricals in the right dataframe, I get the following error. Trying to reproduce with a toy dataset but no luck so far.
``` python
out = pd.merge(left, right, how='left', left_on='left_id', right_on='right_id')
Traceba... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9427 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9427/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9427/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9427/events | https://github.com/pandas-dev/pandas/pull/9427 | 56,748,535 | MDExOlB1bGxSZXF1ZXN0Mjg3NzMxMzk= | 9,427 | ENH: Don't infer WOM-5MON if we don't support it (#9425) | {
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"followers_url": "https://api.github.... | 3 | 2015-02-05T23:47:57Z | 2015-06-02T19:26:59Z | 2015-05-30T21:28:14Z | NONE | null | closes #9425
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https://api.github.com/repos/pandas-dev/pandas/issues/9428 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9428/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9428/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9428/events | https://github.com/pandas-dev/pandas/issues/9428 | 56,753,148 | MDU6SXNzdWU1Njc1MzE0OA== | 9,428 | DataFrame populated with the letter "n" when no data passed and dtype is specified as str | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-02-06T00:36:42Z | 2015-07-25T14:52:34Z | 2015-07-25T14:52:34Z | CONTRIBUTOR | null | This is discussed on Stack Overflow [here](http://stackoverflow.com/questions/28354207/why-is-an-empty-dataframe-of-dtype-str-filled-of-n/28355909).
When constructing a DataFrame with the `dtype` as `str` but not passing in any data (just an index), the resulting DataFrame is filled with the letter `n`:
``` python
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https://api.github.com/repos/pandas-dev/pandas/issues/9429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9429/events | https://github.com/pandas-dev/pandas/pull/9429 | 56,753,327 | MDExOlB1bGxSZXF1ZXN0Mjg3NzU4NjA= | 9,429 | PERF: performance improvements in multi-key groupby | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-02-06T00:38:58Z | 2015-02-07T13:05:53Z | 2015-02-07T12:44:31Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_multi_index | 1008.6970 | 1861.1110 | 0.5... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9430 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9430/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9430/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9430/events | https://github.com/pandas-dev/pandas/issues/9430 | 56,754,361 | MDU6SXNzdWU1Njc1NDM2MQ== | 9,430 | ValueError in io.data Options get_all_data function. Could not convert string to float. | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-02-06T00:52:41Z | 2015-04-07T09:07:40Z | 2015-04-07T09:07:33Z | NONE | null | When making the request
```
opt = Options('PCLN', 'yahoo')
data = opt.get_all_data()
```
I get the error:
ValueError Traceback (most recent call last)
<ipython-input-8-f49f9e288226> in <module>()
1 opt = Options('PCLN', 'yahoo')
----> 2 data = opt.get_all_data()
/usr/local/lib/p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9431 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9431/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9431/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9431/events | https://github.com/pandas-dev/pandas/issues/9431 | 56,761,312 | MDU6SXNzdWU1Njc2MTMxMg== | 9,431 | Unique on the datetimeindex / datetime64 array | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-02-06T02:21:56Z | 2015-08-03T22:03:11Z | 2015-08-03T22:03:11Z | CONTRIBUTOR | null | http://stackoverflow.com/a/28357483/1240268
```
In [11]: dti = pd.DatetimeIndex([pd.Timestamp('2015-02-05 22:24:00+0000', tz='UTC')])
In [12]: dti.unique()
Out[12]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-02-05 22:24:00+00:00]
Length: 1, Freq: None, Timezone: UTC
In [13]: pd.unique(dti)
Out[13]: array([14... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9432 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9432/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9432/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9432/events | https://github.com/pandas-dev/pandas/issues/9432 | 56,788,432 | MDU6SXNzdWU1Njc4ODQzMg== | 9,432 | BUG: NotImplementedError not handled correctly | {
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... | 3 | 2015-02-06T09:16:11Z | 2019-10-06T01:29:03Z | 2019-10-06T01:29:03Z | NONE | null | #### Lets make a DataFrame with at least three rows (does not fail when only two rows are present).
`df = pandas.DataFrame([[1], [2], [3]])`
#### Selecting rows using query works as expected:
`selected_df = df.query('index == [1, 2]')`
#### However, performing traditional indexing fails because Pandas is trying to p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9433 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9433/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9433/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9433/events | https://github.com/pandas-dev/pandas/issues/9433 | 56,790,794 | MDU6SXNzdWU1Njc5MDc5NA== | 9,433 | Error in div operation with DataFrame contains negative and 0 values | {
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] | closed | false | null | [] | null | 2 | 2015-02-06T09:42:33Z | 2015-02-07T00:26:07Z | 2015-02-06T13:35:04Z | CONTRIBUTOR | null | ``` python
df1 = pd.DataFrame([-1, 0, 1])
df2 = pd.DataFrame([1, 1, 1])
df2/df1
0
0 -inf
1 inf
2 1.00000
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9434 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9434/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9434/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9434/events | https://github.com/pandas-dev/pandas/issues/9434 | 56,813,523 | MDU6SXNzdWU1NjgxMzUyMw== | 9,434 | BUG: Timestamp from "y-m-d H" string | {
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... | closed | false | null | [] | null | 1 | 2015-02-06T13:48:33Z | 2015-02-06T13:51:34Z | 2015-02-06T13:51:16Z | NONE | null | I have datetime strings of the form `"1996-4-7 0"`, where the `0` is supposed to be equivalent to `00:00` (i.e. hour 0). I want to turn them into timestamps, but this causes ValueErrors:
``` python
pd.Timestamp('1996-4-7 0') # Result: ValueError
```
These work, on the other hand:
``` python
pd.Timestamp('1996-4-7... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9435 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9435/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9435/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9435/events | https://github.com/pandas-dev/pandas/issues/9435 | 56,834,472 | MDU6SXNzdWU1NjgzNDQ3Mg== | 9,435 | index_col in read_csv and read_table ignores dtype argument | {
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... | 17 | 2015-02-06T16:35:48Z | 2021-03-08T12:47:12Z | null | CONTRIBUTOR | null | xref #11728 for the multi-index case
xref https://github.com/pydata/pandas/issues/14379 for converters
``` python
import pandas as pd
import numpy as np
from pandas.compat import StringIO
data = """Internets,Spaceships
01,a
02,b
03,c
04,d
05,e
06,f
"""
# No leading zeroes in the index because it ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9436 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9436/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9436/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9436/events | https://github.com/pandas-dev/pandas/pull/9436 | 56,838,298 | MDExOlB1bGxSZXF1ZXN0Mjg4MjQyMTA= | 9,436 | Fix typo in np.uint8 type in pandas.io.parsers | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9437 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9437/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9437/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9437/events | https://github.com/pandas-dev/pandas/pull/9437 | 56,871,062 | MDExOlB1bGxSZXF1ZXN0Mjg4NDM4MDA= | 9,437 | DOC: Clarify definition of kurtosis used | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-02-06T21:32:39Z | 2015-06-04T20:46:54Z | 2015-02-10T14:16:52Z | CONTRIBUTOR | null | Added explation that kurtosis returned is Fisher's kurtosis, also
known as excess kurtosis.
Also cleans up the kurtosis function to remove some unused computations.
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https://api.github.com/repos/pandas-dev/pandas/issues/9438 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9438/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9438/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9438/events | https://github.com/pandas-dev/pandas/issues/9438 | 56,873,297 | MDU6SXNzdWU1Njg3MzI5Nw== | 9,438 | read_csv and unicode characters in filename (python 2.7, pandas 15.2) | {
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```
import pandas
df = pandas.read_csv(u"C:/成功例Q309~Metadata.tsv")
```
does not work, and gives the output:
```
IOError: File C:/???Q309.ppt~Metadata.tsv does not exist
```
It seems similar in nature to this issue: https://github.com/pydata/pandas/issues/9315 however #9315 was reportedly fixed in 14.2 wit... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9439 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9439/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9439/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9439/events | https://github.com/pandas-dev/pandas/pull/9439 | 56,878,968 | MDExOlB1bGxSZXF1ZXN0Mjg4NDg3OTc= | 9,439 | ENH: Add StringMethods.isnumeric and isdecimal | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9440 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9440/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9440/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9440/events | https://github.com/pandas-dev/pandas/pull/9440 | 56,906,527 | MDExOlB1bGxSZXF1ZXN0Mjg4NjI0Mjg= | 9,440 | PERF: Move Period class and related functions to Cython module | {
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I wanted to start with implementing frequencies with multipliers (see issue #7811), but the code for Period was all over the place. So, I moved all code directly related to Period to a Cython module. Changed a bit of the logic to make calls from Cytho... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9441 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9441/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9441/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9441/events | https://github.com/pandas-dev/pandas/pull/9441 | 56,912,490 | MDExOlB1bGxSZXF1ZXN0Mjg4NjQ2NDE= | 9,441 | BUG: scatter_matrix draws incorrect axis | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-02-07T14:12:40Z | 2015-04-01T03:27:26Z | 2015-03-31T21:06:09Z | MEMBER | null | Closes #5662. Refactored a little.
Made examples to confirm 1st and 2nd row's yaxis are identical.
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(100, 3))
df[0] = df[1]
pd.tools.plotting.scatter_matrix(df)
```
![figure_1](https://cloud.githubusercontent.com/assets/1696302/6092365/63f4ce... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9442 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9442/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9442/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9442/events | https://github.com/pandas-dev/pandas/issues/9442 | 56,921,839 | MDU6SXNzdWU1NjkyMTgzOQ== | 9,442 | Strange Behavior of mean() with timedelta64 | {
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When a dataframe with a timedelta64 is very large, the mean() function does not work as expected. In the following code, the mean is incorrect but all the other stats are fine:
``` python
import numpy as np
import pandas as pd
dates1 = pd.date_range(start='20000101T000000', end='20150101T230000', freq='1... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9443 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9443/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9443/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9443/events | https://github.com/pandas-dev/pandas/issues/9443 | 56,953,916 | MDU6SXNzdWU1Njk1MzkxNg== | 9,443 | Series.value_counts doesn't respect dropna = False for categorical series | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-02-08T14:50:01Z | 2015-03-08T16:13:04Z | 2015-03-08T16:13:04Z | CONTRIBUTOR | null | Right:
```
$ python -c 'import pandas; print pandas.Series([1, 2, None, 1, 1, 3, None, 3]).value_counts(dropna = False)'
1 3
3 2
NaN 2
2 1
dtype: int64
```
Wrong, because there is no row for NaN:
```
$ python -c 'import pandas; print pandas.Series([1, 2, None, 1, 1, 3, None, 3], dtype = "category")... | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-02-08T15:40:42Z | 2015-02-11T14:19:34Z | 2015-02-11T14:19:34Z | CONTRIBUTOR | null | Example:
```
import pandas
def geta():
i = pandas.MultiIndex.from_tuples([(1, 2, 'a', 0),
(1, 2, 'a', 1),
(1, 1, 'b', 0),
(1, 1, 'b', 1),
(2, 1, 'b', 0),
(2, 1, 'b',... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9445 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9445/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9445/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9445/events | https://github.com/pandas-dev/pandas/pull/9445 | 56,965,565 | MDExOlB1bGxSZXF1ZXN0Mjg4ODU3NDM= | 9,445 | PERF: performance improvement in MultiIndex.sortlevel | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-08T20:10:45Z | 2015-03-05T12:48:28Z | 2015-02-16T12:33:49Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
multiindex_sortlevel_int64 | 664.1977 | 2443.2590 | 0.27... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9446 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9446/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9446/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9446/events | https://github.com/pandas-dev/pandas/issues/9446 | 56,978,131 | MDU6SXNzdWU1Njk3ODEzMQ== | 9,446 | DOC: HDF access limitations and Comparison of SQL/HDF | {
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... | 3 | 2015-02-09T01:18:04Z | 2015-02-10T20:46:54Z | null | NONE | null | I think that the documentation for HDFStore could be more explicit about the limitations of accessing data from an HDF file. For example, it is not at all clear from the docs that when selecting several columns of data from a saved table that the entire table needs to be read into memory. Specifically, one might be tem... | {
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"id": 233160... | closed | false | null | [] | null | 5 | 2015-02-09T10:36:16Z | 2019-10-26T04:00:31Z | 2019-10-26T04:00:31Z | MEMBER | null | At some time [last year](https://github.com/pydata/pandas/issues/4646#issue-18447522),
```
pd.DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]]).groupby(0).apply(lambda x: pd.Series(np.arange(len(x)), x.index))
```
would return
```
0 0
1 0
2 1
3 2
4 1
dtype: int64
```
In 0.14, it returns
```
0
1 ... | {
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... | 2 | 2015-02-09T10:59:59Z | 2020-05-07T03:35:38Z | null | MEMBER | null | For now just some observations:
- The docstring of different funtions about `float_format`:
- the documentation of [`to_csv`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html) (as well as `to_excel`) states: `string, Format string for floating point numbers`
- Example of this u... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9449 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9449/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9449/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9449/events | https://github.com/pandas-dev/pandas/issues/9449 | 57,017,107 | MDU6SXNzdWU1NzAxNzEwNw== | 9,449 | to_clipboard issue with float_format | {
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... | 2 | 2015-02-09T11:11:11Z | 2019-10-21T19:34:16Z | null | MEMBER | null | Related to #9448, using the following example:
```
In [143]: df = pd.DataFrame(np.random.randn(5,2), columns=['A', 'B'])
In [144]: df
Out[144]:
A B
0 -1.151315 -2.174735
1 -1.031375 -1.957344
2 -0.619368 -0.835133
3 2.562381 -0.294245
4 2.432353 0.032805
```
`to_clipboard` is said to pass the `k... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9450 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9450/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9450/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9450/events | https://github.com/pandas-dev/pandas/pull/9450 | 57,021,146 | MDExOlB1bGxSZXF1ZXN0Mjg5MTQ1MjU= | 9,450 | ENH Read mutiple excel sheets in single API call | {
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"followers_url": "https://api.github.com/users/jr... | 32 | 2015-02-09T11:53:24Z | 2015-02-24T00:52:59Z | 2015-02-23T12:52:09Z | CONTRIBUTOR | null | Enables reading of multiple excel sheets in a single API call, reducing read time substantially.
Essentially, 2O(n) becomes O(1) + O(n).
## Before
``` python
dfs = {}
for sheet in [‘Sheet1’,’Sheet2’,’Sheet3’]:
#We have to open the file 3 times = Super Slow
dfs[sheet] = pd.read_excel("TestData.xlsx",sheetname... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9451 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9451/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9451/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9451/events | https://github.com/pandas-dev/pandas/pull/9451 | 57,025,527 | MDExOlB1bGxSZXF1ZXN0Mjg5MTcxNTY= | 9,451 | BUG: handle NotImplementedError in Index comparisons | {
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... | 5 | 2015-02-09T12:40:34Z | 2015-05-09T16:03:59Z | 2015-05-09T16:03:59Z | NONE | null | Fixes issue #9432
A very specific fix that is not anticipated to impact performance.
The test performed is safe and documents that the origin of the
problem is actually numpy. When this numpy bug is fixed it will
be easy to remove this code.
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https://api.github.com/repos/pandas-dev/pandas/issues/9452 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9452/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9452/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9452/events | https://github.com/pandas-dev/pandas/issues/9452 | 57,032,125 | MDU6SXNzdWU1NzAzMjEyNQ== | 9,452 | BUG/COMPAT: io.common.get_filepath_or_buffer should use pass encoding parameter | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-02-09T13:45:35Z | 2015-03-06T03:20:52Z | 2015-03-06T03:20:52Z | CONTRIBUTOR | null | Ran into this when downloading a file from s3 with py3.
[This line](https://github.com/pydata/pandas/blob/master/pandas/io/common.py#L157) needs to pass the encoding parameter to the boto method `.get_contents_as_string`, else it returns a stream of bytes which trips up StringIO on python 3.
Simple fix. Will try to g... | {
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"id": 233160... | open | false | null | [] | null | 3 | 2015-02-09T15:05:08Z | 2019-03-08T20:26:58Z | null | MEMBER | null | ```
df = pd.DataFrame([[1, 2, 3], [2, 3, 3], [1, 4, 3], [1, 5, 4], [2, 6, 4]])
def onlycol0(group):
return group[[group.columns[0]]]
g = df.groupby(0)
g.transform(onlycol0)
```
raises an "AssertionError: invalid dtype determination in get_concat_dtype".
```
def onemorecol(group):
group['new'] = 2
return g... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9454 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9454/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9454/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9454/events | https://github.com/pandas-dev/pandas/issues/9454 | 57,045,655 | MDU6SXNzdWU1NzA0NTY1NQ== | 9,454 | factorize fails for list of tuples | {
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Factorize indeed works for a list of tuples as long as the lens of all the tuples are not identical, but fails the moment all tuples have ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9455 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9455/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9455/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9455/events | https://github.com/pandas-dev/pandas/issues/9455 | 57,097,969 | MDU6SXNzdWU1NzA5Nzk2OQ== | 9,455 | ERR: better error message on invalid on with multi-index columns | {
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```
> a_df
object_uid item_uid
0 0FlIRitxKX 0D1dPxep2C
1 0FLPRriacw 0DoYxRkSdr
2 0FUh6FQ4mQ 0DsOBGsYl9
3 0FH1hIfNCL 0DoYxRkSdr
4 0FZVBzi2DX 0DoYxRkSdr
5 0FAJ5vfLQs 0DoYxRkSdr
6 0F7lEk65qa 0DoYxRkSdr
7 0Fqe3TIN7Q 0DoYxRkSdr
8 0FrSgt4Vut 0DsO... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9456 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9456/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9456/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9456/events | https://github.com/pandas-dev/pandas/issues/9456 | 57,172,776 | MDU6SXNzdWU1NzE3Mjc3Ng== | 9,456 | BUG: need better inference for path in Series construction | {
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"followers_url": "https://api.github.... | 12 | 2015-02-10T13:13:29Z | 2015-08-20T15:23:45Z | 2015-06-10T20:01:43Z | CONTRIBUTOR | null | This hits a path in `Series.__init__` which I think needs some better inference
https://github.com/pydata/pandas/blob/master/pandas/core/series.py#L178
```
In [1]: d = {numpy.datetime64('2015-01-07T02:00:00.000000000+0200'): 42544017.198965244,
...: numpy.datetime64('2015-01-08T02:00:00.000000000+0200'): 4051... | {
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"de... | closed | false | null | [] | null | 3 | 2015-02-10T19:23:00Z | 2015-02-10T21:13:04Z | 2015-02-10T20:40:10Z | NONE | null | I am trying to merge multiple dataframe with consecutive merge operations, I want to add a suffix to the name of the newly merged column names. A simplified version of my code looks like this:
```
from pandas import *
f0 = DataFrame(columns=['data'], data=[1,2,3], index=['a','b','c'])
f1 = DataFrame(columns=['data'], ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9460 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9460/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9460/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9460/events | https://github.com/pandas-dev/pandas/pull/9460 | 57,244,587 | MDExOlB1bGxSZXF1ZXN0MjkwNDkzNDY= | 9,460 | Groupby sort | {
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https://github.com/pydata/pandas/issues/9444
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https://api.github.com/repos/pandas-dev/pandas/issues/9461 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9461/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9461/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9461/events | https://github.com/pandas-dev/pandas/pull/9461 | 57,247,168 | MDExOlB1bGxSZXF1ZXN0MjkwNTA5OTk= | 9,461 | Fix bug in multiindex series groupby where sort argument is ignored | {
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One test added. Test fails without the fix.
closes https://github.com/pydata/pandas/issues/9444
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"color": "fbc... | closed | false | null | [] | {
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"followers_url": "https://api.github... | 6 | 2015-02-10T22:46:50Z | 2018-11-03T13:12:50Z | 2018-11-03T13:12:50Z | CONTRIBUTOR | null | I was poking around in tdi.py with pdb and I noticed this code at [line 255 of current master](https://github.com/pydata/pandas/blob/0efd4b3c8d2b0308441a6960327a2447bd419a1b/pandas/tseries/tdi.py#L255):
``` python
@classmethod
def _simple_new(cls, values, name=None, freq=None, **kwargs):
--> if not getattr... | {
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```
In [20]: df = pd.DataFrame(np.ones((3,4)), columns=pd.MultiIndex.from_product([['A', 'B'],['a', 'b']]))
In [21]: s = pd.Series({'a':1, 'b':2})
In [22]: df
Out[22]:
A B
a b a b
0 1 1 1 1
1 1 1 1 1
2 1 1 1 1
In [23]: s
Out[23]:
a ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9464 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9464/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9464/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9464/events | https://github.com/pandas-dev/pandas/pull/9464 | 57,308,373 | MDExOlB1bGxSZXF1ZXN0MjkwODYyNTk= | 9,464 | BUG: subplots with layout kw may show unnecessary warning | {
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Occurrence condition:
- `subplots=True`.
- `layout` is specified larger than the number of required subplot axes.
In this case, `legend` is called against blank axes and results in warning.
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One idea to prevent this is to override ``tm.TestCase.assertEquals` to raise more explicit warning or exception.
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https://api.github.com/repos/pandas-dev/pandas/issues/9466 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9466/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9466/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9466/events | https://github.com/pandas-dev/pandas/issues/9466 | 57,318,914 | MDU6SXNzdWU1NzMxODkxNA== | 9,466 | loc very slow on sorted, non-unique index with list of labels ar argument | {
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"color": "a1... | closed | false | null | [] | {
"closed_at": "2019-01-26T00:51:26Z",
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"followers_url": "https://api.github... | 6 | 2015-02-11T14:00:43Z | 2018-10-01T12:08:59Z | 2018-10-01T12:08:59Z | MEMBER | null | ```
In [1]: import pandas, numpy
In [2]: df = pandas.DataFrame(numpy.random.random((100000, 4)))
In [3]: %timeit df.loc[55555]
10000 loops, best of 3: 118 µs per loop
In [4]: %timeit df.loc[[55555]]
1000 loops, best of 3: 324 µs per loop
```
... makes sense to me.
```
In [5]: df.index = list(range(99999)) + [55555... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9467 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9467/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9467/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9467/events | https://github.com/pandas-dev/pandas/issues/9467 | 57,362,106 | MDU6SXNzdWU1NzM2MjEwNg== | 9,467 | BUG/CLN: SparseSeries __getitem__ uses questionable, buggy logic | {
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https://github.com/pydata/pandas/blob/bb9c311f36fbf508d11f2eabe3fdbdd010abf8c0/pandas/sparse/series.py#L361
However, some non-scalar input is hashable... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9468 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9468/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9468/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9468/events | https://github.com/pandas-dev/pandas/issues/9468 | 57,368,417 | MDU6SXNzdWU1NzM2ODQxNw== | 9,468 | BUG: Series.resample across daylight saving boundary causes segfault. | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-02-11T19:56:17Z | 2015-03-11T12:10:55Z | 2015-03-11T12:10:55Z | NONE | null | The following snippet of code is causing a segfault. Here it's failing at head, but I've seen it fail in the 0.15.2 release as well, with both NumPy 1.7.1 and NumPy 1.9.1.
Interestingly this doesn't happen with Pandas 0.13.1.
```
>>> import pandas
>>> import pytz
>>> pandas.show_versions()
INSTALLED VERSIONS
---------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9469 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9469/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9469/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9469/events | https://github.com/pandas-dev/pandas/issues/9469 | 57,375,627 | MDU6SXNzdWU1NzM3NTYyNw== | 9,469 | Categorical[idx].codes != Categorical.codes[idx] | {
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"followers_url": "https://api.github.com/users/jr... | 15 | 2015-02-11T20:53:55Z | 2015-02-12T20:15:50Z | 2015-02-12T19:23:50Z | CONTRIBUTOR | null | I'm unable to isolate this, but maybe this rings a bell for someone. It took me a long time to find the source of error in my code. I don't think this is intentional, and if so, is pretty dangerous.
```
[~/]
[22]: y[idx].codes[:15]
[22]: array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4], dtype=int8)
[~/]
[23]: y.co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9470 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9470/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9470/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9470/events | https://github.com/pandas-dev/pandas/pull/9470 | 57,380,477 | MDExOlB1bGxSZXF1ZXN0MjkxMzA0Mzg= | 9,470 | BUG: Bug in Categorical.__getitem__/__setitem__ with listlike input getting incorrect result from indexer coercion (GH9469) | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2015-02-11T21:32:22Z | 2015-02-12T19:23:50Z | 2015-02-12T19:23:50Z | CONTRIBUTOR | null | closes #9469
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https://api.github.com/repos/pandas-dev/pandas/issues/9471 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9471/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9471/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9471/events | https://github.com/pandas-dev/pandas/issues/9471 | 57,384,454 | MDU6SXNzdWU1NzM4NDQ1NA== | 9,471 | Add method='nearest' to fillna | {
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... | 3 | 2015-02-11T22:03:16Z | 2021-04-12T05:16:20Z | null | MEMBER | null | This should be a pretty straightforward followup on #9258 that will allow us to also support `align` with `method='nearest'`.
fillna internally relies on interpolate which already supports `method='nearest' so this _may_ just work (it will needs lots of tests).
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https://api.github.com/repos/pandas-dev/pandas/issues/9472 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9472/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9472/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9472/events | https://github.com/pandas-dev/pandas/pull/9472 | 57,409,018 | MDExOlB1bGxSZXF1ZXN0MjkxNDgxNDM= | 9,472 | ENH: Series.idxnonzero() to return the index of nonzero entries | {
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When filtering data, a `nonzero` pattern is actually very handy; yet panda’s version returns the iloc (for compatibility with numpy), which prevent to use the result on a differently-shaped dataframe, defeating the power of pandas inde... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9473 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9473/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9473/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9473/events | https://github.com/pandas-dev/pandas/pull/9473 | 57,416,819 | MDExOlB1bGxSZXF1ZXN0MjkxNTI2MTA= | 9,473 | BUG: fix common.is_hashable for NumPy scalars on Python 3 | {
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This now relies entirely on the result of calling `hash` on the argument.
Note: I had to change the test for old style classes on Python 2 -- these are
now considered hashable by `is_hashable`, because they don't fail `hash`.
CC @aevri
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https://api.github.com/repos/pandas-dev/pandas/issues/9474 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9474/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9474/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9474/events | https://github.com/pandas-dev/pandas/issues/9474 | 57,482,089 | MDU6SXNzdWU1NzQ4MjA4OQ== | 9,474 | Pandas + py2exe error: Parent module '' not loaded, cannot perform relative import | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2015-02-12T16:21:30Z | 2016-09-08T10:08:50Z | 2016-09-08T10:08:41Z | NONE | null | I have a very simple program in python that works perfectly within Spyder or Eclipse, but fails when executed in the command line after it was pacakged with py2exe.
OS is a French Windows 7 and I am using:
Python 3.4.1 from Anaconda 2.1.0 (64-bit)
Pandas 0.15.2 np19py34_0
cython 0.21.2
The error I get is the fol... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9475 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9475/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9475/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9475/events | https://github.com/pandas-dev/pandas/pull/9475 | 57,484,076 | MDExOlB1bGxSZXF1ZXN0MjkxOTM3ODY= | 9,475 | BUG: binary operator method alignment with integer level (GH9463) | {
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@jreback also related to #6682 (I removed 'join compat' introduced in that PR, as this worked only for level names, and passing `level=level` works for both level names and integer level numbers)
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https://api.github.com/repos/pandas-dev/pandas/issues/9476 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9476/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9476/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9476/events | https://github.com/pandas-dev/pandas/issues/9476 | 57,487,052 | MDU6SXNzdWU1NzQ4NzA1Mg== | 9,476 | Py2exe error: Parent module '' not loaded, cannot perform relative import | {
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---
Py2exe error: Parent module '' not loaded, cannot perform relative import
I am using Windows, Python 3.3.5, pandas 0.15.2, and py2exe 0.9.2.2.
I used py2exe to generate a single-file executable from a Python script that uses pandas 0.15.2. When I tried to run it, I got the following error: `Paren... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9477 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9477/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9477/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9477/events | https://github.com/pandas-dev/pandas/issues/9477 | 57,514,661 | MDU6SXNzdWU1NzUxNDY2MQ== | 9,477 | .apply converting string to date | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-12T20:27:08Z | 2015-02-24T13:49:22Z | 2015-02-24T13:49:22Z | CONTRIBUTOR | null | The .apply on DataFrame is converting my string to a date. I have posted this on StackOverflow:
http://stackoverflow.com/questions/28487105/pandas-on-apply-passing-wrong-value
I'm not sure if I'm doing something wrong or it is a bug, but it seems quite strange behavior.
Example [here](http://stackoverflow.com/quest... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9478 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9478/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9478/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9478/events | https://github.com/pandas-dev/pandas/issues/9478 | 57,519,652 | MDU6SXNzdWU1NzUxOTY1Mg== | 9,478 | DateTimeIndex values are assigned across entire df when using .loc | {
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What I noticed is that if your index is a datetimeindex then assigning the values is not respecting the column selection and is blatting all rows.
I am using pand... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9480 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9480/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9480/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9480/events | https://github.com/pandas-dev/pandas/pull/9480 | 57,538,094 | MDExOlB1bGxSZXF1ZXN0MjkyMjgwNzg= | 9,480 | BUG: bug in sort for grouping with a categorical columns (GH8868) | {
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... | closed | false | null | [] | null | 3 | 2015-02-12T23:44:58Z | 2015-02-21T13:03:58Z | 2015-02-18T11:27:50Z | CONTRIBUTOR | null | Closes #8868
hack. I can add tests if this passes the CI and we think this should be taken further.
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Im looking to set the rolling rank on a dataframe. Having posted, discussed and analysed the code it looks like the suggested way would be to use the pandas Series.rank function as an argument in rolling_apply.... | {
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xref #7282
`numexpr/bottleneck` are ok by default
you can check with something like this:
```
[jreback-~/pandas] python -v -c 'import pandas' > & out
[jreback-~/pandas] grep 'import ' out | more
```
- [x] ~~`matplotlib` (not sure if this is possible to remove by default)~~ The converters are set, not so... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9484 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9484/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9484/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9484/events | https://github.com/pandas-dev/pandas/pull/9484 | 57,643,943 | MDExOlB1bGxSZXF1ZXN0MjkyOTI2NDE= | 9,484 | Default values for dropna to "False" (issue 9382) | {
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"description": "r... | closed | false | null | [] | null | 18 | 2015-02-13T19:40:00Z | 2015-06-03T00:09:00Z | 2015-05-09T16:00:02Z | CONTRIBUTOR | null | PLEASE REVIEW: This is my commit to a major project, and would appreciate a quick once over!
As per discussion in Issue 9382, changes all HDF functions from having default of dropping all rows with NA in all non-index rows.
closes #9382
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https://api.github.com/repos/pandas-dev/pandas/issues/9485 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9485/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9485/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9485/events | https://github.com/pandas-dev/pandas/issues/9485 | 57,653,717 | MDU6SXNzdWU1NzY1MzcxNw== | 9,485 | Unexpected output of table pivoting when there is little data; a TimeGrouper object is in the index. | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-02-13T21:03:29Z | 2017-07-08T22:18:32Z | 2017-07-08T22:18:26Z | NONE | null | ``` python
# coding: utf-8
import pandas as pd
import numpy as np
frame = pd.read_csv("table.csv", engine="python", parse_dates=['since'])
print frame
d = pd.pivot_table(frame, index=pd.TimeGrouper(key='since', freq='1d'), values=["value"], columns=['id'], aggfunc=np.sum, fill_value=0)
print d
print "^that is not what... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9486 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9486/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9486/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9486/events | https://github.com/pandas-dev/pandas/pull/9486 | 57,656,372 | MDExOlB1bGxSZXF1ZXN0MjkzMDA1OTI= | 9,486 | Updated whatsnew for 16.0 for change to hdf dropna default | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9487 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9487/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9487/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9487/events | https://github.com/pandas-dev/pandas/pull/9487 | 57,666,511 | MDExOlB1bGxSZXF1ZXN0MjkzMDcyNjM= | 9,487 | CLN: import packers/gbq/html only on-demand (GH9482) | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-02-13T23:09:53Z | 2015-02-24T11:14:35Z | 2015-02-24T11:14:34Z | CONTRIBUTOR | null | closes #9482
so a 30% reduction in import time
```
[jreback-~/pandas] time python -c 'import pandas'
0.788u 0.120s 0:00.91 98.9% 0+0k 0+7io 0pf+0w
[jreback-~/pandas] time python -c 'import pandas'
0.782u 0.115s 0:00.91 97.8% 0+0k 0+0io 0pf+0w
[jreback-~/pandas] time python -c 'import pandas'
0.790u 0.121s 0:0... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9488 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9488/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9488/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9488/events | https://github.com/pandas-dev/pandas/issues/9488 | 57,669,690 | MDU6SXNzdWU1NzY2OTY5MA== | 9,488 | 3D ndarray (NxNxN) to DataFrame (NxNxN rows and 4 columns = 3 indices + 1 value) | {
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] | closed | false | null | [] | null | 8 | 2015-02-13T23:49:27Z | 2015-02-16T15:18:57Z | 2015-02-16T15:02:28Z | NONE | null | I have a ndarray (between 4 or 6 dimensions), but let's talk about 3D data.
so if I have an index like:
``` python
data[i,j,k] = 30
```
How can I create a excel sheet where the first 3 colunns are the indices like:
``` python
>>> frame
i j k X
0 0 0 1023
...
0 0 255 3
...
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https://api.github.com/repos/pandas-dev/pandas/issues/9489 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9489/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9489/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9489/events | https://github.com/pandas-dev/pandas/issues/9489 | 57,691,232 | MDU6SXNzdWU1NzY5MTIzMg== | 9,489 | Strange behaviour with nested "groupby"s | {
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"id": ... | closed | false | null | [] | null | 1 | 2015-02-14T12:13:15Z | 2015-02-15T23:12:35Z | 2015-02-15T23:12:35Z | MEMBER | null | If I execute the following code:
```
import pandas
df = pandas.DataFrame([['test', -2], ['b', -2], ['b', -2]], columns=['surname', 'new_id'])
def process_by_last_name(group):
if len(group) == 1:
if "%" in str(group['new_id']):
import ipdb; ipdb.set_trace()
return group['new_id']
g... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9490 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9490/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9490/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9490/events | https://github.com/pandas-dev/pandas/issues/9490 | 57,691,948 | MDU6SXNzdWU1NzY5MTk0OA== | 9,490 | Allow method keyword for DataFrame.corrwith() | {
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... | 1 | 2015-02-14T12:42:47Z | 2021-04-12T05:18:36Z | null | NONE | null | Now it is not possible to choose method when using corrwith() function, which defaults to pearson (I assume). Would be great to see the same methods that are allowed in corr().
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https://api.github.com/repos/pandas-dev/pandas/issues/9491 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9491/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9491/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9491/events | https://github.com/pandas-dev/pandas/pull/9491 | 57,694,413 | MDExOlB1bGxSZXF1ZXN0MjkzMjA2NDU= | 9,491 | BUG: unstack with nulls & Timedelta/DateTime index | {
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on branch:
```
>>> df
a b c
0 2014-02-01 -1 days 100
1 NaT NaT 101
2 2014-02-03 1 days 102
3 NaT 2 days 103
4 2014-02-05 NaT 104
5 2014-02-06 4 days 105
>>> df.pivot('a', 'b', 'c').fillna('-')
b ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9492 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9492/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9492/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9492/events | https://github.com/pandas-dev/pandas/pull/9492 | 57,698,725 | MDExOlB1bGxSZXF1ZXN0MjkzMjIzNjY= | 9,492 | WIP/TST: Added test of unstacking two levels with NaN. | {
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Should delete the `print()` statements before merging...
This is the output I get:
```
======================================================================
FAIL: test_unstack_nan_index (pandas.tes... | {
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"id": 104865385,
"name": "IO Stata",
"node_id": "MDU6TGFiZWwxMDQ4NjUzODU=",
"url": "https://api.github.com/repos/pandas-dev/pandas/labels/IO%20Stata"
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] | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 16 | 2015-02-15T04:30:17Z | 2015-11-12T23:44:19Z | 2015-03-05T23:26:19Z | CONTRIBUTOR | null | This PR adds a get_chunk method to StataReader to allow files to be read incrementally. This is quite useful when processing large files that can barely fit into memory.
The interface is modeled after the analogous method in TextFileReader.
There are some limitations when incrementally converting categoricals in pre... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9494 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9494/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9494/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9494/events | https://github.com/pandas-dev/pandas/issues/9494 | 57,743,907 | MDU6SXNzdWU1Nzc0MzkwNw== | 9,494 | Chainable Series.set_name() | {
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},
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"description": "p... | closed | false | null | [] | {
"closed_at": "2016-03-12T16:19:08Z",
"closed_issues": 469,
"created_at": "2015-07-19T20:18:45Z",
"creator": {
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"followers_url": "https://api.github.com/users/jr... | 11 | 2015-02-15T20:12:25Z | 2016-02-11T13:09:26Z | 2016-02-11T02:42:04Z | NONE | null | It's sometimes convenient to assign/replace the name of a Series, especially if the result is going to be a DataFrame column, for example:
```
df.groupby([]).size().set_name('GRP_SIZE').reset_index()
```
This should be as simple as:
```
def set_name(self, name):
self.name = name
return self
```
Comments?
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https://api.github.com/repos/pandas-dev/pandas/issues/9495 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9495/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9495/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9495/events | https://github.com/pandas-dev/pandas/issues/9495 | 57,744,468 | MDU6SXNzdWU1Nzc0NDQ2OA== | 9,495 | Horizontal barplot | {
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```
pd.Series([1,2], index=pd.Index([1,2], name='x')).plot(kind='bar', orientation='horizontal')
```
yelds an:
```
AssertionError: incompatible sizes: argument 'left' must be length ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9496 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9496/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9496/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9496/events | https://github.com/pandas-dev/pandas/issues/9496 | 57,746,742 | MDU6SXNzdWU1Nzc0Njc0Mg== | 9,496 | CLN: consolidate Iterator interface | {
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"description": "IO issue... | closed | false | null | [] | {
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... | 1 | 2015-02-15T21:24:45Z | 2020-05-02T04:08:38Z | 2020-05-02T04:08:37Z | CONTRIBUTOR | null | In several of the IO interfaces, we allow `chunksize=` and `iterator` to allow chunk reading. These all use sepearate non-conforming base classes.
We should create a base class `core/base.py/Iterator` that inherits from `collections.Iterator`. Then incorporate as much of this combined functionaility into this base cla... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9497 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9497/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9497/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9497/events | https://github.com/pandas-dev/pandas/issues/9497 | 57,753,602 | MDU6SXNzdWU1Nzc1MzYwMg== | 9,497 | BUG: DataFrame.unstack() with two levels containing NaN | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-02-16T00:19:15Z | 2015-03-05T23:25:33Z | 2015-03-05T23:25:33Z | CONTRIBUTOR | null | With latest master code (including #9061 and #9292), `DataFrame.unstack()` produces incorrect results when `level` contains two levels one of which contains `NaN`:
```
df = pd.DataFrame({'A': list('aaaabbbb'),'B':range(8), 'C':range(8)})
df.iloc[3, 1] = np.NaN
dfs = df.set_index(['A', 'B'])
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9499 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9499/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9499/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9499/events | https://github.com/pandas-dev/pandas/pull/9499 | 57,813,404 | MDExOlB1bGxSZXF1ZXN0MjkzNzgyODE= | 9,499 | TST: fix read_pickle for win32 compat | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9500/events | https://github.com/pandas-dev/pandas/pull/9500 | 57,856,421 | MDExOlB1bGxSZXF1ZXN0Mjk0MDM2NTE= | 9,500 | BLD: revise setup.py to clean properly on windows platforms | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9501 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9501/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9501/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9501/events | https://github.com/pandas-dev/pandas/pull/9501 | 57,865,335 | MDExOlB1bGxSZXF1ZXN0Mjk0MDgzNDI= | 9,501 | BUG: Incorrect dtypes inferred on datetimelike looking series & on xs slices (GH9477) | {
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