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https://api.github.com/repos/pandas-dev/pandas/issues/11704 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11704/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11704/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11704/events | https://github.com/pandas-dev/pandas/issues/11704 | 118,942,708 | MDU6SXNzdWUxMTg5NDI3MDg= | 11,704 | ENH: implement __iter__ for window objects | {
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might be slightly non-trivial as the current impl does NOT use an explict iterator, rather does a sliding window in cython and marginal calcs.
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https://api.github.com/repos/pandas-dev/pandas/issues/11705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11705/events | https://github.com/pandas-dev/pandas/pull/11705 | 118,959,525 | MDExOlB1bGxSZXF1ZXN0NTE4NjY2NDA= | 11,705 | BUG GH11693 Support NaT series concatenation | {
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Closes #11693
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https://api.github.com/repos/pandas-dev/pandas/issues/11706 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11706/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11706/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11706/events | https://github.com/pandas-dev/pandas/pull/11706 | 118,969,302 | MDExOlB1bGxSZXF1ZXN0NTE4NzE0MDc= | 11,706 | BUG: GH11697 where memory allocation failed in rolling_median | {
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thanks @ajspeck
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"... | closed | false | null | [] | null | 16 | 2015-11-26T12:12:59Z | 2017-11-05T20:46:24Z | 2015-11-29T17:30:39Z | NONE | null | Hi,
I think there is a bug in `to_pickle`
```
>>> data = pd.read_hdf(DG.load_path + 'time_series/PSI_TS_after_alignment_and_reshaping.h5', 'table')
>>> data.shape
(1006, 288095)
>>> data.to_pickle(DG.load_path + 'time_series/PSI_TS_after_alignment_and_reshaping2.pkl')
Traceback (most recent call last):
File "<stdin... | {
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Timestamp('2015-11-18 16:30:00+0530', tz='Asia/Kolkata')
It should have returned as Timestamp('2015-11-18 15:30:00+0530', tz='Asia/Kolkata') as no daylight saving is applicable in India.
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-11-26T18:28:51Z | 2015-11-29T19:51:39Z | 2015-11-29T17:32:18Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/11699
xref https://github.com/pydata/pandas/issues/11189#issuecomment-152613636
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https://api.github.com/repos/pandas-dev/pandas/issues/11710 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11710/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11710/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11710/events | https://github.com/pandas-dev/pandas/issues/11710 | 119,114,627 | MDU6SXNzdWUxMTkxMTQ2Mjc= | 11,710 | to_hdf broken for timezone aware datetime64[ns] columns in pandas 0.17.0 | {
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See the following example:
``` python
import pandas as pd
import numpy as np
from datetime import datetime
print "pandas version", pd.__version__
df1 = pd.DataFrame(data=np.random.randn(5,3), index=pd.DatetimeIndex(start=d... | {
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Deleting the last sheet manually fix the problem.
Maybe Pandas should be improved to be robust to this common case.
Also I post this to raise awareness of this issue in case someone is scratching his head wond... | {
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"... | closed | false | null | [] | null | 5 | 2015-11-27T13:42:00Z | 2015-11-30T11:36:16Z | 2015-11-30T11:36:16Z | CONTRIBUTOR | null | to_clipboard uses ascii encoding. And it cannot be changed.
```
>>> import pandas as pd
>>> pd.__version__
'0.17.0'
>>> pd.DataFrame(["Hello world"]).to_clipboard()
>>> pd.DataFrame(["Здравствуй, мир"]).to_clipboard()
...
UnicodeEncodeError: 'ascii' codec can't encode characters in position 22-31: ordinal not in range... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-11-27T15:54:03Z | 2015-12-01T15:15:06Z | 2015-12-01T15:15:06Z | CONTRIBUTOR | null | SAS xport files allow floats to be stored with fewer than 8 bytes, with implicit zeros in the high order bytes. Currently we only handle full width (8 byte) floats. This PR adds support for truncated floats.
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https://api.github.com/repos/pandas-dev/pandas/issues/11714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11714/events | https://github.com/pandas-dev/pandas/pull/11714 | 119,242,408 | MDExOlB1bGxSZXF1ZXN0NTIwMjIyMjY= | 11,714 | ENH: Add support for s3 in read_excel #11447 | {
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Updated excel.py to use pandas.io.common.get_filepath_or_buffer (handles s3 urls) instead of urlopen, only when _is_s3_url returns true. This was the minimally invasive change.
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https://api.github.com/repos/pandas-dev/pandas/issues/11715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11715/events | https://github.com/pandas-dev/pandas/pull/11715 | 119,246,669 | MDExOlB1bGxSZXF1ZXN0NTIwMjQ0Nzg= | 11,715 | Fix to BUG GH11698. added default value of mask | {
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Please review.
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] | closed | false | null | [] | null | 9 | 2015-11-28T13:08:39Z | 2017-02-07T23:58:04Z | 2015-11-29T17:28:21Z | NONE | null | I am reading a very large csv file (the NYC taxi dataset at https://storage.googleapis.com/tlc-trip-data/2015/), only two columns:
index_col=False,skipinitialspace=True,usecols=['pickup_longitude', 'pickup_latitude'], chunksize=...
I load it progressively by varying-size chunks, and use 2 threads to do the progressive ... | {
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There was a bug deeply hidden in `pandas/core/index.py`, where it was assumed that the `.get_loc` method of an index would only return an integer or a slice, while it can also return a mask.
Simply correcting that bug has two conseq... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11718 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11718/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11718/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11718/events | https://github.com/pandas-dev/pandas/issues/11718 | 119,310,274 | MDU6SXNzdWUxMTkzMTAyNzQ= | 11,718 | Timestamp subtraction of NaT with timezones | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-11-28T18:03:48Z | 2016-02-11T23:35:54Z | 2016-02-11T23:35:54Z | CONTRIBUTOR | null | In pandas 0.17.1, a TypeError is returned when trying to subtract a timezone-aware timestamp from a NaT timestamp:
``` python
In [1]: import pandas as pd
In [2]: pd.Timestamp(None, tz='utc') - pd.Timestamp('now', tz='utc')
Traceback (most recent call last):
File "<ipython-input-2-5e0738cec5fa>", line 1, in <module... | {
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Regardless of the arguments specified, the return of `DataFrame.join` always has the left side's index values and name. In the case of `how='right'`, there should be `np.nan` in the index when the right side does not 'match' a row on the left.
In this case:
``` python
>>> pd.DataFrame([], columns=['gene... | {
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... | 15 | 2015-11-30T06:20:28Z | 2020-04-10T02:46:42Z | null | CONTRIBUTOR | null | Copying dataframe with to_clipboard and Qt gives different results: when pasted to Excel there's one more empty line. It may be unexpected and overwrite important data.
When looking into clipboard with Free Clipboard Viewer we can see that Qt uses CR+LF linebreaks:
![image](https://cloud.githubusercontent.com/assets/5... | {
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] | closed | false | null | [] | null | 12 | 2015-11-30T09:32:43Z | 2015-11-30T16:07:16Z | 2015-11-30T15:58:29Z | NONE | null | I have a similiar issue with BUG #11697 but not same.
This issue is on win64. But my macos is ok.
Version:
python:2.7.10
pandas:0.17.1
numpy: 1.10.1
I use the @ajspeck example.
I got the same error
```
File "pandas\algos.pyx", line 1586, in pandas.algos.roll_median_c (pandas\algos.c:36408)
MemoryError: skiplist_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11723/events | https://github.com/pandas-dev/pandas/issues/11723 | 119,480,807 | MDU6SXNzdWUxMTk0ODA4MDc= | 11,723 | Aliases for column names | {
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"f... | 11 | 2015-11-30T12:17:38Z | 2021-05-25T17:29:48Z | null | NONE | null | When I work with Pandas DataFrames, I prefer to keep the full column names for clarity. So when I print out the `head`, or use describe, I get a meaningful table. However, this also means I have column names like "Time of Sale" that become annoying to type out.
A nice compromise seems like it would be to have short "... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11724/events | https://github.com/pandas-dev/pandas/issues/11724 | 119,489,962 | MDU6SXNzdWUxMTk0ODk5NjI= | 11,724 | Dropping rows from a DataFrame with MultiIndex does not remove index entries | {
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] | closed | false | null | [] | null | 3 | 2015-11-30T13:18:39Z | 2015-11-30T14:30:47Z | 2015-11-30T13:49:39Z | NONE | null | Hello
I am not sure, maybe this is the expected behaviour, but it was a big surprise for me (and a source of some bugs).
As the title states, dropping rows (whether inplace or not) does not delete corresponding index entries (when the frame has a multiIndex). If one later uses index values directly it causes really u... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11725/events | https://github.com/pandas-dev/pandas/issues/11725 | 119,491,615 | MDU6SXNzdWUxMTk0OTE2MTU= | 11,725 | Surprising results of pandas.to_datetime() | {
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... | closed | false | null | [] | null | 4 | 2015-11-30T13:29:09Z | 2015-11-30T14:38:25Z | 2015-11-30T13:51:42Z | CONTRIBUTOR | null | I just used `pd.to_datetime` on a string column and inspected the head of the dataframe and it looked good. Unfortunately, I had german dates (day.month.year) with high days in the first rows. These were converted correctly, but then I got dates (of the next month) with low days and the conversion switched to american ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11726/events | https://github.com/pandas-dev/pandas/issues/11726 | 119,491,894 | MDU6SXNzdWUxMTk0OTE4OTQ= | 11,726 | pd.SparseDataFrame().duplicated() ValueError when default_fill_value is present in dataframe | {
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```
In [68]: a=np.zeros(shape=(5,5))
In [69]: pd.DataFrame(a).apply(lambda x: pd.SparseArray(x, fill_value=0)).drop_duplicates()
Out[69]:
0 1 2 3 4
0 NaN NaN NaN NaN NaN
```
~~~
It seems as if pd.SparseDataFrame().duplicated consistently fails when ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11727/events | https://github.com/pandas-dev/pandas/issues/11727 | 119,500,564 | MDU6SXNzdWUxMTk1MDA1NjQ= | 11,727 | BUG: mpl_style 'default' gives AttributeError: Unknown property color_cycle with latest matplotlib | {
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... | closed | false | null | [] | {
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```
In [1]: matplotlib.__version__
Out[1]: '1.5.0'
In [2]: df = pd.DataFrame(np.random.randn(5,5))
In [4]: df.plot()
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x7bc5c50>
In [5]: pd.set_option('display.mpl_style', 'defaul... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11728/events | https://github.com/pandas-dev/pandas/issues/11728 | 119,565,693 | MDU6SXNzdWUxMTk1NjU2OTM= | 11,728 | read_csv with MultiIndex on columns doesn't respect level's dtypes | {
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"color": "009... | closed | false | null | [] | null | 3 | 2015-11-30T19:45:43Z | 2015-12-03T00:05:10Z | 2015-11-30T21:20:16Z | NONE | null | I want to store a dataframe with hierarchical column index in a CSV. The index contains both string and integer levels. If I'm correct this should be done like that:
```
import numpy as np
import pandas as pd
n = 10
p = 6
names = ['alphabetic', 'numeric']
levels = [['a', 'b'], [0, 1, 2]]
column_index = pd.MultiIndex.f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11729/events | https://github.com/pandas-dev/pandas/issues/11729 | 119,598,654 | MDU6SXNzdWUxMTk1OTg2NTQ= | 11,729 | Add option to hide index in pandas.Series.to_string | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-11-30T22:45:16Z | 2015-12-12T14:04:00Z | 2015-12-12T14:04:00Z | NONE | null | In the Dataframes.to_string function there is a option to not print the index:
index : bool, optional
```
whether to print index (row) labels, default True
```
Can this be added to the Series function to_string?
http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.Series.to_string.html
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https://api.github.com/repos/pandas-dev/pandas/issues/11730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11730/events | https://github.com/pandas-dev/pandas/issues/11730 | 119,638,734 | MDU6SXNzdWUxMTk2Mzg3MzQ= | 11,730 | pandas.applymap converts None to NaN even though I didn't ask it to? | {
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"color": "e102d8"... | closed | false | null | [] | null | 7 | 2015-12-01T04:13:36Z | 2015-12-01T14:11:51Z | 2015-12-01T12:48:26Z | NONE | null | I have a dataframe with some empty strings and some NaNs. I want to convert both of those to None.
I'm calling applymap to convert the empty strings into None, which works; but it has the side-effect of converting all the existing Nones into NaN!
Using pandas version 0.16:
```
import pandas
import numpy
p = pandas... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11731/events | https://github.com/pandas-dev/pandas/issues/11731 | 119,686,960 | MDU6SXNzdWUxMTk2ODY5NjA= | 11,731 | CSV with timedeltas generated with and old version of pandas cannot be parsed anymore | {
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"des... | closed | false | null | [] | null | 1 | 2015-12-01T10:23:17Z | 2015-12-01T11:47:54Z | 2015-12-01T11:47:38Z | NONE | null | I have been using an older version of pandas (0.13.1) and serialized several timedeltas in a data frame. These timedeltas ended up like this in the generated CSV file:
```
-1 days +23:30:00.000000000
```
This format cannot be parsed anymore. It seems that the + sign in front of the hours causes the parsing to fail:
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https://api.github.com/repos/pandas-dev/pandas/issues/11732 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11732/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11732/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11732/events | https://github.com/pandas-dev/pandas/issues/11732 | 119,704,980 | MDU6SXNzdWUxMTk3MDQ5ODA= | 11,732 | API: change .resample to be a groupby-like API | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-12-01T12:17:44Z | 2016-02-02T15:16:18Z | 2016-02-02T15:16:18Z | CONTRIBUTOR | null | similar to #11603
this would transform:
`s.resample('D',how='max')`
to
`s.resample('D').max()`
This would be a breaking API change, as the default is `how='mean'`, meaning, that `s.resample('D')` returns the `mean` of the resampled data. However it would be visible at the very least and not simply change working ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11733 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11733/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11733/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11733/events | https://github.com/pandas-dev/pandas/issues/11733 | 119,707,481 | MDU6SXNzdWUxMTk3MDc0ODE= | 11,733 | BUG: read_excel with multi-indexed column ignores index_col=None | {
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"followers_url": "https://api.github... | 18 | 2015-12-01T12:29:45Z | 2018-11-18T21:42:59Z | 2018-11-18T21:42:59Z | MEMBER | null | From SO: http://stackoverflow.com/questions/34020061/excel-to-pandas-dataframe-using-first-column-as-index
@chris-b1 another one on the multi-index excel issues .. :-)
Small test case: content of excel file:
| A | A | B | B |
| --- | --- | --- | --- |
| key | val | key | val |
| 1 | 2 | 3 | 4 |
| 1 | 2 | 3 | 4 |
gi... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11734/events | https://github.com/pandas-dev/pandas/issues/11734 | 119,725,939 | MDU6SXNzdWUxMTk3MjU5Mzk= | 11,734 | ValueError with 'T' as column heading | {
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] | closed | false | null | [] | null | 2 | 2015-12-01T14:18:32Z | 2015-12-01T14:36:01Z | 2015-12-01T14:25:51Z | NONE | null | With a tab separated file with 'T' as a column heading a ValueError is generated when using this column label to identify the column. This does not happen with other letters including 'F'
```
test.tsv
A B T F
1 0 0 0
0 1 1 1
1 1 1 1
```
```
In [1]: import pandas as pd
In [2]: df = pd.DataFra... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11735/events | https://github.com/pandas-dev/pandas/issues/11735 | 119,754,614 | MDU6SXNzdWUxMTk3NTQ2MTQ= | 11,735 | Bar plot does not respect axes.prop_cycle in matplotlib 1.5 | {
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``` python
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
data = pd.DataFrame({'1': [1, 2, 3], '2': [3, 5, 1]})
data.plot(kind='bar')
plt.savefig('test.png', dpi=300)
```
Results in
![test](https://cloud.githubusercontent.com/assets/5488440/115... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11736/events | https://github.com/pandas-dev/pandas/issues/11736 | 119,769,329 | MDU6SXNzdWUxMTk3NjkzMjk= | 11,736 | Regression: DatetimeIndex ignores timezones | {
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"followers_url": "https://api.github... | 6 | 2015-12-01T17:37:17Z | 2018-07-30T10:30:01Z | 2018-07-30T10:30:01Z | NONE | null | In 0.16.2, `DatetimeIndex` functions used to handle the timezone correctly:
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'x': ["2015-05-27 19:05:17.178000-02:00"]})
In [3]: pd.DatetimeIndex(df.x)
Out[3]: DatetimeIndex(['2015-05-27 19:05:17.178000-02:00'], dtype='datetime64[ns]', freq=None, tz='tzoffset(N... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11737 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11737/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11737/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11737/events | https://github.com/pandas-dev/pandas/issues/11737 | 119,878,225 | MDU6SXNzdWUxMTk4NzgyMjU= | 11,737 | Add option to specified the index's name in pd.Series.rename. | {
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"color": "00... | closed | false | null | [] | null | 1 | 2015-12-02T06:43:10Z | 2015-12-02T11:37:43Z | 2015-12-02T11:37:04Z | NONE | null | Suppose I have a series:
``` python
In [31]: pd.__version__
Out[31]: u'0.17.1'
In [32]: ser = pd.Series(range(4), index=['Aa', 'Ab', 'Ba', 'Bb'])
In [33]: ser
Out[33]:
Aa 0
Ab 1
Ba 2
Bb 3
dtype: int64
```
I can change the index as following:
``` python
In [34]: ser.rename(lambda i: tuple(i))
Out[34]: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11738 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11738/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11738/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11738/events | https://github.com/pandas-dev/pandas/issues/11738 | 119,920,855 | MDU6SXNzdWUxMTk5MjA4NTU= | 11,738 | BUG: end_time for Period with multiplied freq | {
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"... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-02T11:17:47Z | 2015-12-07T11:11:21Z | 2015-12-07T11:11:21Z | CONTRIBUTOR | null | end_time of a Period with multiplied freq is the square of the multiplication
``` python
>>> pd.__version__
u'0.17.0'
>>> p = pd.Period('2015-08-01', freq='3D')
>>> p.end_time, p.to_timestamp(how='E')
(Timestamp('2015-08-09 23:59:59.999999999'), Timestamp('2015-08-03 00:00:00'))
>>> p = pd.Period('2015-08-01', freq=... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11739/events | https://github.com/pandas-dev/pandas/issues/11739 | 119,976,640 | MDU6SXNzdWUxMTk5NzY2NDA= | 11,739 | to_csv(header=True) produces error: | {
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] | closed | false | null | [] | null | 2 | 2015-12-02T16:10:09Z | 2015-12-02T16:18:04Z | 2015-12-02T16:15:58Z | NONE | null | According to the latest doc `header` parameter can be
> boolean or list of string, default True
However, the following code
```
import pandas as pd
pd.read_csv('test.csv', header=True)
```
Produces this error:
> TypeError: Passing a bool to header is invalid. Use header=None for no header or header=int or list-li... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11740 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11740/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11740/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11740/events | https://github.com/pandas-dev/pandas/issues/11740 | 119,979,803 | MDU6SXNzdWUxMTk5Nzk4MDM= | 11,740 | Drop fails when supplied with an object that implements __getitem__ | {
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"description": "Data where the values ar... | open | false | null | [] | null | 6 | 2015-12-02T16:23:10Z | 2021-04-21T05:07:42Z | null | CONTRIBUTOR | null | When you supply `drop` with an item that implements `__getitem__`, `drop` doesn't drop it. It does when supplied with the item within a list.
That's because instead of checking `is_list_like` ([here](https://github.com/pydata/pandas/blob/master/pandas/core/common.py#L2329)), `drop` calls `_index_labels_to_array` which... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11741 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11741/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11741/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11741/events | https://github.com/pandas-dev/pandas/issues/11741 | 119,983,283 | MDU6SXNzdWUxMTk5ODMyODM= | 11,741 | Unexpected behavior with groupby on single-row dataframe? | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-02T16:37:39Z | 2016-01-19T15:50:56Z | 2016-01-17T03:43:25Z | NONE | null | Do single-row dataframes (not series) get special treatment in some way? It seems you can do arbitrary groupby operations on them on non-existent columns without errors.
```
df1 = pd.DataFrame(np.random.randn(1,4), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randn(2,4), columns=list('ABCD'))
In [3]: df1.groupb... | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-02T16:53:20Z | 2016-04-01T13:13:27Z | 2016-04-01T13:13:27Z | CONTRIBUTOR | null | If I group a `DataFrame` by a column of dates, the return type varies depending on whether I just group or whether I also apply a frequency in the `Grouper`.
Grouping without resampling dates returns a `DataFrame` when I apply a function which returns a labeled `Series`, or a `Series` if the function returns a scalar... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11743 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11743/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11743/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11743/events | https://github.com/pandas-dev/pandas/issues/11743 | 120,015,042 | MDU6SXNzdWUxMjAwMTUwNDI= | 11,743 | DataFrame.query gives false positives | {
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``` python
import pandas as pd
testdf = pd.DataFrame(pd.np.random.randint(100, size=(10000, 2)))
testdf.columns = ['a', 'b']
missing_value = -2000
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http://w... | {
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``` python
df['foo']['bar'] = quux
# becomes
df.__getitem__(... | {
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"followers_url": "https://api.github.com/users/j... | 5 | 2015-12-02T23:59:46Z | 2021-05-12T13:27:55Z | 2021-05-12T13:27:55Z | CONTRIBUTOR | null | in py3, the following should correctly raise an `IndexError`
```
In [1]: s = Series(range(5),index=list('aacde'))
In [2]: s[5]
IndexError
```
however
a positional indexer that is in the range should work
`s[3]`
Further more, this must be a monotonic increasing duplicated index to trigger
```
In [8]: Series(range(5... | {
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] | closed | false | null | [] | null | 1 | 2015-12-03T00:38:30Z | 2015-12-03T00:41:51Z | 2015-12-03T00:41:16Z | NONE | null | According to the [API](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.ptp.html?highlight=ptp#pandas.Series.ptp), the `ptp` method of Series should be able to handle 'index' as an argument for the `axis` keyword. But when I try the following code:
```
x = pd.Series(range(15,20), index=range(5,10))
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``` python
import pandas as pan
import datetime as dt
time_f = '%Y... | {
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Please Review .
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https://api.github.com/repos/pandas-dev/pandas/issues/11751 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11751/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11751/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11751/events | https://github.com/pandas-dev/pandas/pull/11751 | 120,099,461 | MDExOlB1bGxSZXF1ZXN0NTI0OTQ3MzE= | 11,751 | Consolidate list-like checking to is_list_like | {
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] | closed | false | null | [] | null | 6 | 2015-12-03T05:52:31Z | 2016-01-06T17:35:57Z | 2016-01-06T17:21:07Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/11740
Let me know what tests would be helpful for these types of 'cleaning' PRs, and whether a "what's new" is needed.
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https://api.github.com/repos/pandas-dev/pandas/issues/11752 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11752/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11752/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11752/events | https://github.com/pandas-dev/pandas/issues/11752 | 120,190,434 | MDU6SXNzdWUxMjAxOTA0MzQ= | 11,752 | ENH: avro to/from serialization | {
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"f... | 11 | 2015-12-03T15:01:26Z | 2019-12-22T16:56:05Z | 2019-12-22T16:56:05Z | CONTRIBUTOR | null | discussed in #3525
shiny new fast version of avro might be interesting: https://github.com/MaxPoint/cyavro/issues/1
by @mariusvniekerk
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https://api.github.com/repos/pandas-dev/pandas/issues/11753 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11753/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11753/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11753/events | https://github.com/pandas-dev/pandas/issues/11753 | 120,190,628 | MDU6SXNzdWUxMjAxOTA2Mjg= | 11,753 | Series.ptp function does not correctly use axis='index' argument | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11754 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11754/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11754/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11754/events | https://github.com/pandas-dev/pandas/issues/11754 | 120,236,046 | MDU6SXNzdWUxMjAyMzYwNDY= | 11,754 | DataFrame.iat indexing with duplicate columns | {
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# error
In [27]: pd.DataFrame([[1, 1]], columns=['x','x']).iat[0,0]
TypeError: len() of unsized object
# ok
In [26]: pd.DataFrame([[1, 1]], columns=['x','y']).iat[0,0]
Out[26]: 1
```
I have some weird issue in a DataFrame I'm creating from a row-based array.
Using python3 and pandas 0.17.1 (from debian unstable),... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11755 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11755/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11755/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11755/events | https://github.com/pandas-dev/pandas/issues/11755 | 120,272,953 | MDU6SXNzdWUxMjAyNzI5NTM= | 11,755 | concat of tz series and no tz results in attribute error. | {
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"followers_url": "https://api.github.com/users/jr... | 9 | 2015-12-03T21:23:04Z | 2016-02-10T18:45:38Z | 2016-02-10T18:45:38Z | CONTRIBUTOR | null | ``` python
import pandas as pd
x = pd.Series(pd.date_range('20151124 10:00', '20151124 11:00', freq = '1h', tz = "UTC") )
y = pd.Series(pd.date_range('2012-01-01', '2012-01-03'))
pd.concat([x,y])
```
AttributeError: 'numpy.ndarray' object has no attribute 'tz_localize'
Is it a bug or not supported ?
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https://api.github.com/repos/pandas-dev/pandas/issues/11756 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11756/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11756/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11756/events | https://github.com/pandas-dev/pandas/pull/11756 | 120,275,492 | MDExOlB1bGxSZXF1ZXN0NTI1OTYxMjA= | 11,756 | DOC: pd.read_csv doc-string clarification #11555 | {
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Updated IO Tools documentation for read_csv() and read_table() to be consistent with the doc-string.
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https://api.github.com/repos/pandas-dev/pandas/issues/11757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11757/events | https://github.com/pandas-dev/pandas/issues/11757 | 120,402,446 | MDU6SXNzdWUxMjA0MDI0NDY= | 11,757 | BUG: .date and timezones | {
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```
In [1]: s = Series(pd.to_datetime([1396670200, 1405733045, 1448248441],unit='s')).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')
In [2]: s
Out[2]:
0 2014-04-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11758 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11758/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11758/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11758/events | https://github.com/pandas-dev/pandas/issues/11758 | 120,403,641 | MDU6SXNzdWUxMjA0MDM2NDE= | 11,758 | to_datetime returns NaT for epoch with errors='coerce' | {
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Parsing unix epoch timestamps give NaT with `errors='coerce'` while they can be parsed correctly without it:
``` python
>>> pd.to_datetime(1420043460, errors='coerce', unit='s')
NaT
>>> pd.to_datetime(1420043460, unit='s')
Timestamp('2014-12-31 16:31:00')
```
pandas: 0.17.1
numpy: 1.10.1
Python 2.7.7
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... | 10 | 2015-12-04T13:55:40Z | 2018-02-10T18:02:11Z | 2018-02-10T18:02:10Z | CONTRIBUTOR | null | ```
In [19]: df = DataFrame({'a':['A1', 'A1', 'A1'], 'b':['B1','B1','B2'], 'c':1})
In [20]: df.set_index('a').groupby('b').rank(method='first')
Out[20]:
c
a
A1 1
A1 2
A1 1
In [21]: df.set_index('a').groupby('c').rank(method='first')
-------------------------------------------------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11760/events | https://github.com/pandas-dev/pandas/issues/11760 | 120,416,618 | MDU6SXNzdWUxMjA0MTY2MTg= | 11,760 | COMPAT: force int when doing unit conversions in pd.to_datetime | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2015-12-04T14:53:02Z | 2016-05-08T14:06:06Z | 2016-05-08T14:05:59Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/34078114/convert-pandas-time-series-unix-epoch-to-datetime/34078557#34078557)
xref to #11758
We should force int conversions when `unit` is passed (as by definition these are int-like things).
```
In [21]: s = pd.Series(["1440643875", "1440644191", "1440645638", "14409987... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11761 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11761/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11761/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11761/events | https://github.com/pandas-dev/pandas/issues/11761 | 120,435,065 | MDU6SXNzdWUxMjA0MzUwNjU= | 11,761 | DataFrame.info() issue with duplicated columns | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-12-04T16:21:11Z | 2015-12-07T11:33:53Z | 2015-12-07T11:33:53Z | NONE | null | When duplicated columns exists, DataFrame.info() returns broken output:
```
pd.DataFrame([[1, 1]], columns=['x','x']).info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1 entries, 0 to 0
Data columns (total 2 columns):
x 1 non-null x int64
x int64
dtype: object
x 1 non-null x int64
x int64
dtyp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11762/events | https://github.com/pandas-dev/pandas/issues/11762 | 120,435,935 | MDU6SXNzdWUxMjA0MzU5MzU= | 11,762 | pandas.merge issue with duplicated column names | {
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... | 9 | 2015-12-04T16:25:23Z | 2021-04-21T05:16:09Z | 2021-04-21T05:16:08Z | NONE | null | ```
df1 = pd.DataFrame([[1, 1]], columns=['x','x'])
df2 = pd.DataFrame([[1, 1]], columns=['x','y'])
pd.merge(df1, df2, on='x')
```
Results in:
```
Traceback (most recent call last):
File "test.py", line 5, in <module>
m = pd.merge(df1, df2, on='x')
File "/usr/lib/python3/dist-packages/pandas/tools/merge.py", ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11764 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11764/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11764/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11764/events | https://github.com/pandas-dev/pandas/issues/11764 | 120,471,430 | MDU6SXNzdWUxMjA0NzE0MzA= | 11,764 | read_html doesn't have infer_types parameter | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11765 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11765/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11765/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11765/events | https://github.com/pandas-dev/pandas/pull/11765 | 120,533,759 | MDExOlB1bGxSZXF1ZXN0NTI3NDgyNDE= | 11,765 | DOC: Remove infer_types from the documentation of read_html GH 11764 | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-12-05T06:07:49Z | 2015-12-07T06:42:49Z | 2015-12-05T15:13:23Z | CONTRIBUTOR | null | Remove infer_types from read_html.
Closes #11764
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Side-note, the API could probably be a lot cleaner here. Accept `*args` instead of a list e.g.
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-12-05T17:53:05Z | 2015-12-08T01:44:52Z | 2015-12-08T01:44:52Z | CONTRIBUTOR | null | Unfinished at the moment. Just wanted to push something up since there are other PRs that could build on this.
This moves the logic of "what value should I display for this cell?" from the Template to the `Styler` class itself. The two biggest benefits are
1. User extensibility: Users provide formatting functions (via... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11769 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11769/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11769/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11769/events | https://github.com/pandas-dev/pandas/issues/11769 | 120,583,529 | MDU6SXNzdWUxMjA1ODM1Mjk= | 11,769 | COMPAT: msgpack on windows 3.5 failing oddly | {
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in [here](https://github.com/pydata/pandas/commit/5b84e49e44cb378cee211436f0966e53d89b08b1)
2.7 fully passes and 3.5 does except for some of these odd MemoryErrors, must be some sort of recursion issue, see build [here](https://ci.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11770 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11770/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11770/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11770/events | https://github.com/pandas-dev/pandas/pull/11770 | 120,606,699 | MDExOlB1bGxSZXF1ZXN0NTI3NzI0MTU= | 11,770 | MNT: simplify AutoDateFormatter | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-06T04:18:03Z | 2015-12-08T16:14:35Z | 2015-12-08T15:20:46Z | CONTRIBUTOR | null | - import constants from matplolib.dates
- do not re-implement AutoDateFormatter functionality that
is already upstream
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https://api.github.com/repos/pandas-dev/pandas/issues/11771 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11771/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11771/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11771/events | https://github.com/pandas-dev/pandas/pull/11771 | 120,611,522 | MDExOlB1bGxSZXF1ZXN0NTI3NzM4ODQ= | 11,771 | BUG: [GH 11738] Fix for end_time when multiple of a frequency is requested | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-06T06:13:56Z | 2015-12-07T11:05:39Z | 2015-12-07T03:06:22Z | CONTRIBUTOR | null | closes #11738
1. Added a new test with the multiple of a frequency.
2. Rearranged some tests to improve readability of the code.
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https://api.github.com/repos/pandas-dev/pandas/issues/11773 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11773/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11773/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11773/events | https://github.com/pandas-dev/pandas/issues/11773 | 120,632,261 | MDU6SXNzdWUxMjA2MzIyNjE= | 11,773 | read_hdf not supporting pathlib.Path | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-06T12:46:27Z | 2016-05-16T12:09:51Z | 2016-05-16T12:09:51Z | CONTRIBUTOR | null | According to the Enhancements text for 17.1:
> pd.read_\* functions can now also accept pathlib.Path, or py._path.local.LocalPath objects for the filepath_or_buffer argument. (GH11033)
all read_\* functions should support pathlib.Path now. It works for me with `read_csv` but not for `read_hdf`.
Interestingly, I fin... | {
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Minutes needed to be added to the hour offset rather than subtracted.
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https://api.github.com/repos/pandas-dev/pandas/issues/11775 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11775/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11775/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11775/events | https://github.com/pandas-dev/pandas/issues/11775 | 120,643,698 | MDU6SXNzdWUxMjA2NDM2OTg= | 11,775 | replace on Series with timezone-aware data breaks | {
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``` python
# non-tz aware
In [23]: ser = pd.Series([pd.NaT, pd.Timestamp('2015/01/01')])
In [24]: ser
Out[24]:
0 NaT
1 2015-01-01
dtype: datetime64[ns]
In [25]: ser.replace(pd.NaT, pd.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11776 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11776/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11776/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11776/events | https://github.com/pandas-dev/pandas/issues/11776 | 120,649,062 | MDU6SXNzdWUxMjA2NDkwNjI= | 11,776 | pd.to_numeric produces misleading results on DataFrame | {
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This may be related to the discussion here https://github.com/pydata/pandas/issues/11221 as this function currently doesn't support anything more than 1-d.
```
In [1]: import pandas as... | {
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xref #11427 - once this is merged the failures there should pass - there is also a little more documentation about general offset semantics in that PR.
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1. Added a new test with the multiple of a frequency.
2. Rearranged some tests to improve readability of the code.
3. Added bug fix to whatsnew
4. Added a comment in the test
Didn't follow the contributing guidelines carefully in PR #11771 and hence this new PR.
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My initial chart is produced like this:
```
combined['fraud_count'].unstack(-2).unstack(-1).fillna(0).plot()
```
which looks very neat and perfect, as expected.
In the next step, I am trying to use the secondary_y feature like this:
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So to raise @TomAugspurger idea there in an issue: let's just deprecate it?
@sinhrks @jreback
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This will make it more consistent with the option config style
need to deprecate the original and add new.
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-12-07T16:12:34Z | 2016-01-19T20:02:06Z | 2016-01-19T20:02:06Z | CONTRIBUTOR | null | The following script causes a segfault on my machine
``` python
from io import BytesIO
from multiprocessing.pool import ThreadPool
import pandas as pd
# Make many fake CSV files in memory
bytes = ['\n'.join(['%d,%d,%d' % (i,i,i) for i in range(10000)]).encode()
for j in range(100)]
files = [BytesIO(b) for b ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11787 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11787/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11787/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11787/events | https://github.com/pandas-dev/pandas/issues/11787 | 120,819,729 | MDU6SXNzdWUxMjA4MTk3Mjk= | 11,787 | DEPR: mad (mean absolute difference) functions | {
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... | 12 | 2015-12-07T16:57:28Z | 2021-04-21T05:18:56Z | null | CONTRIBUTOR | null | The generic function `.mad()` calculates the mean absolute difference of a set of data, but in some cases the median absolute difference is more appropriate. In `R`, the `mad()` function accepts a `center` argument to specify how the average absolute difference should be calculated. I propose to add the same to the _pa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11788 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11788/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11788/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11788/events | https://github.com/pandas-dev/pandas/pull/11788 | 120,825,423 | MDExOlB1bGxSZXF1ZXN0NTI4NzQzNDE= | 11,788 | ENH: allow saving of meta-data via CArrays to support wide tables | {
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closes #6245
Here is the code as it stands now. Appending MultiIndex axes now works. The one test that still fails is test_append_frame_column_oriented: writing the DateTimeIndex as columns fails because the index is converted to an array of timestamps so np.array creates an object array. If it ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11789 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11789/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11789/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11789/events | https://github.com/pandas-dev/pandas/pull/11789 | 120,834,927 | MDExOlB1bGxSZXF1ZXN0NTI4ODA0MzI= | 11,789 | DOC: Add context to pd.to_timedelta examples | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-12-07T18:15:20Z | 2015-12-15T19:46:03Z | 2015-12-09T15:20:41Z | CONTRIBUTOR | null | Update the reStructuredText tutorial on timedeltas to include
context, similar to edits suggested for #11785.
Also, add a space after various commas (PEP8), and periods/colons
at the end of sentences for consistency.
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https://api.github.com/repos/pandas-dev/pandas/issues/11790 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11790/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11790/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11790/events | https://github.com/pandas-dev/pandas/pull/11790 | 120,839,608 | MDExOlB1bGxSZXF1ZXN0NTI4ODM0NDE= | 11,790 | BUG: GH11786 Thread safety issue with read_csv | {
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Fixed an issue with thread safety when calling read_csv with a StringIO object.
The issue was caused by a misplaced PyGilSate_Ensure()
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https://api.github.com/repos/pandas-dev/pandas/issues/11791 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11791/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11791/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11791/events | https://github.com/pandas-dev/pandas/issues/11791 | 120,886,965 | MDU6SXNzdWUxMjA4ODY5NjU= | 11,791 | pickling error when doing using dataframe with multiprocessing | {
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] | closed | false | null | [] | null | 8 | 2015-12-07T22:47:45Z | 2015-12-08T15:48:30Z | 2015-12-08T15:36:09Z | NONE | null | When using python 3.4.3 or 3.5.0 and pandas 0.16.2, this simple example works
```
import pandas as pd
import multiprocessing as mp
def squareIt(row):
return row[0]**2, row[1]**2
df = pd.DataFrame({'a': range(100), 'b': range(100)})
with mp.Pool(2) as pool:
sq = pool.imap(squareIt, df.itertuples(False), chun... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11792 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11792/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11792/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11792/events | https://github.com/pandas-dev/pandas/issues/11792 | 120,897,731 | MDU6SXNzdWUxMjA4OTc3MzE= | 11,792 | bug in Series.replace with timezone-aware datetime columns | {
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... | closed | false | null | [] | {
"closed_at": "2019-01-26T00:51:26Z",
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"followers_url": "https://api.github... | 2 | 2015-12-07T23:58:06Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | CONTRIBUTOR | null | Seeing the following behavior in 0.17.1 with the patch from https://github.com/pydata/pandas/pull/11715 applied (would raise without the patch):
``` python
In [29]: ser = pd.Series([pd.NaT, pd.Timestamp('2015/01/01', tz='UTC')])
# works
In [30]: ser.replace(pd.NaT, pd.Timestamp.min)
Out[30]:
0 1677-09-22 00:12:43... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11793 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11793/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11793/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11793/events | https://github.com/pandas-dev/pandas/issues/11793 | 120,917,966 | MDU6SXNzdWUxMjA5MTc5NjY= | 11,793 | Segfault in pd.read_csv() using chunksize parameter | {
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``` python
import pandas as pd
import sys
for df in pd.read_csv(sys.argv[1], chunksize=1000):
print(df[['sum']].sum())
```
and I am attaching [small.csv.gz](https://github.com/pydata/pandas/files/54833/small.csv.gz) as the smallest data set I know reproduces this segfault. Running `pytho... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11794 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11794/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11794/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11794/events | https://github.com/pandas-dev/pandas/issues/11794 | 120,969,691 | MDU6SXNzdWUxMjA5Njk2OTE= | 11,794 | bug in deep copy of Series with timezone-aware datetime entries | {
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"followers_url": "https://api.github.com/users/jr... | 0 | 2015-12-08T09:36:53Z | 2015-12-08T20:14:58Z | 2015-12-08T20:14:58Z | CONTRIBUTOR | null | deep copies of Series with timezone-aware datetimes are shallow in 0.17.1:
``` python
In [12]: import pandas as pd
In [13]: pd.__version__
Out[13]: u'0.17.1'
In [14]: ser = pd.Series([pd.Timestamp('2012/01/01', tz='UTC')])
In [15]: ser2 = ser.copy(deep=True)
In [18]: ser2[0] = pd.Timestamp('1999/01/01', tz='UTC')
# ... | {
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"description": "pandas testing functions or related to the test suite",
"id": 127685,
"name": "Testing",
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"d... | closed | false | null | [] | null | 4 | 2015-12-08T14:03:45Z | 2015-12-08T15:12:56Z | 2015-12-08T14:45:34Z | CONTRIBUTOR | null | ERROR: type should be string, got "https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=arm64&ver=0.17.1-2&stamp=1449515678\nso this one is not even big endian afaik\n\n```\nERROR: test_timestamp_compare (pandas.tests.test_frame.TestDataFrame)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_frame.py\", line 5749, in test_timestamp_compare\n expected = left_f(df, Timestamp('20010109'))\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/ops.py\", line 1116, in f\n return res.fillna(True).astype(bool)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/generic.py\", line 2632, in astype\n dtype=dtype, copy=copy, raise_on_error=raise_on_error, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 2864, in astype\n return self.apply('astype', dtype=dtype, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 2823, in apply\n applied = getattr(b, f)(**kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 430, in astype\n values=values, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 2001, in _astype\n return super(DatetimeBlock, self)._astype(dtype=dtype, **kwargs)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/internals.py\", line 472, in _astype\n values = com._astype_nansafe(values.ravel(), dtype, copy=True)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/core/common.py\", line 2433, in _astype_nansafe\n (arr.dtype, dtype))\nTypeError: cannot astype a datetimelike from [datetime64[ns]] to [bool]\n\n======================================================================\nFAIL: test_NaT_cast (pandas.tseries.tests.test_timeseries.TestSeriesDatetime64)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tseries/tests/test_timeseries.py\", line 3516, in test_NaT_cast\n assert_series_equal(result, expected)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 946, in assert_series_equal\n check_less_precise, obj='{0}'.format(obj))\n File \"pandas/src/testing.pyx\", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)\n File \"pandas/src/testing.pyx\", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 819, in raise_assert_detail\n raise AssertionError(msg)\nAssertionError: Series are different\n\nSeries values are different (100.0 %)\n[left]: [1970-01-01T00:00:00.000000000+0000]\n[right]: [NaT]\n\n======================================================================\nFAIL: test_where_datetime (pandas.tests.test_series.TestSeries)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_series.py\", line 2191, in test_where_datetime\n assert_series_equal(rs, expected)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 946, in assert_series_equal\n check_less_precise, obj='{0}'.format(obj))\n File \"pandas/src/testing.pyx\", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)\n File \"pandas/src/testing.pyx\", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 819, in raise_assert_detail\n raise AssertionError(msg)\nAssertionError: Series are different\n\nSeries values are different (50.0 %)\n[left]: [1970-01-01T00:00:00.000000010+0000, 1970-01-01T00:00:00.000000000+0000]\n[right]: [1970-01-01T00:00:00.000000010+0000, NaT]\n\n======================================================================\nFAIL: test_where_timedelta (pandas.tests.test_series.TestSeries)\n----------------------------------------------------------------------\nTraceback (most recent call last):\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_series.py\", line 2212, in test_where_timedelta\n assert_series_equal(rs, expected)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 946, in assert_series_equal\n check_less_precise, obj='{0}'.format(obj))\n File \"pandas/src/testing.pyx\", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)\n File \"pandas/src/testing.pyx\", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)\n File \"/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py\", line 819, in raise_assert_detail\n raise AssertionError(msg)\nAssertionError: Series are different\n\nSeries values are different (50.0 %)\n[left]: [10 nanoseconds, 0 nanoseconds]\n[right]: [10 nanoseconds, NaT]\n```\n" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11796/events | https://github.com/pandas-dev/pandas/pull/11796 | 121,021,053 | MDExOlB1bGxSZXF1ZXN0NTI5ODg1MjY= | 11,796 | BUG: bug in deep copy of datetime tz-aware objects, #11794 | {
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... | closed | false | null | [] | {
"closed_at": "2016-03-12T16:19:08Z",
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cleanups in copy / remove warnings
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https://api.github.com/repos/pandas-dev/pandas/issues/11797 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11797/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11797/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11797/events | https://github.com/pandas-dev/pandas/issues/11797 | 121,090,383 | MDU6SXNzdWUxMjEwOTAzODM= | 11,797 | Wrong behavior due to Pandas : dict with the same key based on tuple extracted from DataFrame | {
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] | closed | false | null | [] | null | 6 | 2015-12-08T20:18:29Z | 2015-12-09T06:04:09Z | 2015-12-08T20:34:34Z | CONTRIBUTOR | null | Can you explain the behavior of the following :
<!-- language: lang-python -->
```
rows = pd.DataFrame([
(0, np.nan, np.nan, 'toto'),
(1, np.nan, np.nan, 'tata')
])
dico = {}
for row in rows.itertuples():
k = (row[2], row[3])
v = row[4]
print('hash(k) : {0}'.format(hash(k)))
v_prev = dico.se... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11798/events | https://github.com/pandas-dev/pandas/pull/11798 | 121,119,877 | MDExOlB1bGxSZXF1ZXN0NTMwNTIwNTc= | 11,798 | Fix import-time DeprecationWarning on Python 3.5 | {
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] | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-12-08T22:57:18Z | 2015-12-10T17:04:35Z | 2015-12-10T01:30:31Z | CONTRIBUTOR | null | Fixes the following:
```
$ python3.5 -Wall
Python 3.5.0 (default, Sep 18 2015, 09:31:01)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-11)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas.tslib
pandas/__init__.py:7: DeprecationWarning: bad escape \s
from pandas import hashtabl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11799/events | https://github.com/pandas-dev/pandas/issues/11799 | 121,140,700 | MDU6SXNzdWUxMjExNDA3MDA= | 11,799 | Option to suppress automatic conversion of tuples to MultiIndex | {
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"descript... | closed | false | null | [] | null | 14 | 2015-12-09T01:33:49Z | 2015-12-13T20:53:46Z | 2015-12-09T14:20:59Z | NONE | null | Right now we have:
```
>>> pd.DataFrame({(1,2):pd.Series([2.3])}).columns
MultiIndex(levels=[[1], [2]],
labels=[[0], [0]])
>>> pd.Index([(1,2)])
MultiIndex(levels=[[1], [2]],
labels=[[0], [0]])
```
Could we have an option to suppress this behavior? One p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11800/events | https://github.com/pandas-dev/pandas/issues/11800 | 121,191,101 | MDU6SXNzdWUxMjExOTExMDE= | 11,800 | Series.apply loses timezone information | {
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"description": "Duplicate issue or pull ... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2015-12-09T09:05:56Z | 2016-02-06T18:19:36Z | 2015-12-12T14:05:13Z | CONTRIBUTOR | null | apply seems to lose timezone awareness. Would expect the following to be invariant:
``` python
In [9]: import pandas as pd
In [10]: pd.__version__
Out[10]: u'0.17.1'
In [11]: ser = pd.Series([pd.Timestamp('2015/01/01', tz='UTC'), pd.Timestamp('2015/01/02', tz='UTC')])
In [12]: ser
Out[12]:
0 2015-01-01 00:00:00+... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11801/events | https://github.com/pandas-dev/pandas/issues/11801 | 121,221,110 | MDU6SXNzdWUxMjEyMjExMTA= | 11,801 | MultiIndex DataFrame saving to csv and loading breaks its structure. | {
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"description": "r... | closed | false | null | [] | null | 3 | 2015-12-09T11:59:48Z | 2015-12-09T14:11:36Z | 2015-12-09T13:45:19Z | NONE | null | When you save MultiIndex DataFrame to csv and then loading it with no additional parameters pandas not able to read multiindices properly and reconstructed dataframe is incorrect, however all MultiIndices are stored. Creating MultiIndex DataFrame:
``` python
>>> import pandas as pd
>>> arrays = [np.array(['bar', 'bar'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11802/events | https://github.com/pandas-dev/pandas/issues/11802 | 121,252,448 | MDU6SXNzdWUxMjEyNTI0NDg= | 11,802 | Sorting within groupby causes duplication of index level | {
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... | 1 | 2015-12-09T14:28:14Z | 2021-04-21T05:21:43Z | null | CONTRIBUTOR | null | When I run this code on 0.17.0 on Python 3.5.0
```
import pandas as pd
d = pd.DataFrame(data=[[2, 1]])
print(d.index)
print((d.
groupby(level=0).
apply(lambda d:
d.sort_values(0)
)
).index)
```
I get
```
Int64Index([0], dtype='int64')
MultiIndex(levels=[[0], [0]],
labels=[[0], [0]])
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11803 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11803/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11803/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11803/events | https://github.com/pandas-dev/pandas/issues/11803 | 121,281,019 | MDU6SXNzdWUxMjEyODEwMTk= | 11,803 | read_csv. when file has many np.object dtype columns consumes a lot of memory | {
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"description": "IO issues that don't fit into a more specific label",
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"... | closed | false | null | [] | null | 5 | 2015-12-09T16:09:58Z | 2015-12-10T14:46:40Z | 2015-12-10T12:06:50Z | NONE | null | This is related to the old memory issues that pandas had when reading csv files. Now the situation is much better, but I've found that when the csv file has many string columns the memory consumption is increased a lot. Having numeric dtypes is handled correctly.
In pandas/io/parsers.py - TextFileReader.read method:
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