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https://api.github.com/repos/pandas-dev/pandas/issues/11403 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11403/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11403/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11403/events | https://github.com/pandas-dev/pandas/pull/11403 | 112,684,964 | MDExOlB1bGxSZXF1ZXN0NDgzOTE2MTY= | 11,403 | BUG: drop_duplicates drops non-duplicate rows in the presence of integer columns | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11404 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11404/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11404/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11404/events | https://github.com/pandas-dev/pandas/pull/11404 | 112,694,891 | MDExOlB1bGxSZXF1ZXN0NDgzOTg0MTE= | 11,404 | CLN: period boundscheck warnings | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11405 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11405/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11405/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11405/events | https://github.com/pandas-dev/pandas/issues/11405 | 112,702,302 | MDU6SXNzdWUxMTI3MDIzMDI= | 11,405 | pd.merge fails on datetime columns with tzinfo | {
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```
import pandas as pd
from datetime import datetime
from dateutil.tz import gettz
import sys, os
import traceback as tbm
# works
a = pd.DataFrame({'created' : [datetime(2015,10,10),
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https://api.github.com/repos/pandas-dev/pandas/issues/11406 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11406/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11406/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11406/events | https://github.com/pandas-dev/pandas/issues/11406 | 112,720,340 | MDU6SXNzdWUxMTI3MjAzNDA= | 11,406 | BUG: QuarterBegin doesn't match other offsets for n=0 | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-10-22T02:42:39Z | 2015-12-10T12:26:21Z | 2015-12-10T12:26:21Z | CONTRIBUTOR | null | xref #8435 (different issue though)
normally the semantic is to roll forward to the next period for `n=0`
``` python
In [15]: pd.Timestamp('2014-3-2') + pd.offsets.MonthBegin(n=0)
Out[15]: Timestamp('2014-04-01 00:00:00')
In [16]: pd.Timestamp('2014-3-2') + pd.offsets.QuarterBegin(n=0)
Out[16]: Timestamp('2014-03-01 ... | {
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... | 7 | 2015-10-22T03:05:25Z | 2021-04-20T05:50:47Z | 2021-04-20T05:50:46Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/33272411/is-there-an-online-source-of-complete-documentation-on-pandas-objects-classes-b)
People just jump to the API w/o actually reading ANY Introduction where this is convered in the first sentence
so API docs should be more complete
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https://api.github.com/repos/pandas-dev/pandas/issues/11408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11408/events | https://github.com/pandas-dev/pandas/issues/11408 | 112,742,240 | MDU6SXNzdWUxMTI3NDIyNDA= | 11,408 | Pandas exporting to Excel (xls, xlsx) with multilevel columns | {
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I want collect few tables into the one with pandas. I use the code presented below:
``` python
import pandas as pd
import itertools
import types
df = None
for frame in ['x', 'y']:
df_ = pd.read_excel(u'%s.xlsx' % frame)
df_ = df_.set_index([u'time'])
parameters = list(df_.columns)
tuples ... | {
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I noticed a strange behavior involving important errors in my scripts.
``` python
a = pd.DataFrame(np.nan, index=[1,2], columns=[3,4]).astype(np.float64)
b = pd.DataFrame(np.nan, index=[1,2], columns=[3,4]).astype(np.float32)
c = pd.DataFrame(np.nan, index=[1,2], columns=[3,4])
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"followers_url": "https://api.github.com/users/jr... | 6 | 2015-10-22T13:00:14Z | 2015-11-17T14:57:15Z | 2015-11-17T14:57:15Z | CONTRIBUTOR | null | Hello,
I noticed a bug about exporting DataFrame with columns containing localized datetime. I might be due to some another required libraries updated...
Example :
``` python
a = pd.to_datetime(['20120201', '20130201'])
b = pd.to_datetime(['20120201', '20130201'])
dfa = pd.DataFrame([a])
dfb = pd.DataFrame([b])
dfb[... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11412 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11412/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11412/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11412/events | https://github.com/pandas-dev/pandas/issues/11412 | 112,810,376 | MDU6SXNzdWUxMTI4MTAzNzY= | 11,412 | min_itemsize not working on MultiIndex columns for Series, with format="table" | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-10-22T14:03:45Z | 2016-12-05T23:43:12Z | 2015-10-22T14:22:17Z | MEMBER | null | If I do
```
ddf = pd.DataFrame([['a', 'b', 1],
['a', 'b', 2]],
columns=['A', 'B', 'C']).set_index(['A', 'B'])
```
and then
```
ddf['C'].to_hdf('/tmp/store.hdf', 'test',
format="table",
min_itemsize={'A' : 3})
```
I get the following:
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11413 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11413/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11413/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11413/events | https://github.com/pandas-dev/pandas/issues/11413 | 112,830,623 | MDU6SXNzdWUxMTI4MzA2MjM= | 11,413 | Why isn't zip compression included for read_csv? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11414 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11414/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11414/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11414/events | https://github.com/pandas-dev/pandas/issues/11414 | 112,857,612 | MDU6SXNzdWUxMTI4NTc2MTI= | 11,414 | Inconsistent Nan Key Error using Pandas Apply | {
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"description": "Apply, Aggregate, Transf... | open | false | null | [] | null | 6 | 2015-10-22T18:01:47Z | 2020-06-28T04:39:03Z | null | CONTRIBUTOR | null | I'm recoding multiple columns in a dataframe and have come across a strange result that I can't quite figure out. I'm probably not recoding in the most efficient manner possible, but it's mostly the error that I'm hoping someone can explain.
```
s1 = pd.DataFrame([np.nan, '1', '2', '3', '4', '5', columns='col1')
s2 = ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11415 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11415/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11415/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11415/events | https://github.com/pandas-dev/pandas/issues/11415 | 112,898,609 | MDU6SXNzdWUxMTI4OTg2MDk= | 11,415 | Unclear what chunksize represents in to_sql() docs | {
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"description": "to_sql, read_sql, rea... | closed | false | null | [] | null | 3 | 2015-10-22T21:47:47Z | 2021-04-20T05:54:59Z | 2021-04-20T05:54:59Z | NONE | null | From http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html
> **chunksize** _: int, default None_
> If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once.
"this size"? Sounds like it could be the max size in bytes. How about "th... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11416 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11416/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11416/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11416/events | https://github.com/pandas-dev/pandas/pull/11416 | 112,919,571 | MDExOlB1bGxSZXF1ZXN0NDg1MzEwMTI= | 11,416 | ENH: namedtuple's fields as columns | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-10-23T00:44:21Z | 2015-10-24T01:11:48Z | 2015-10-23T20:30:33Z | CONTRIBUTOR | null | Resolves https://github.com/pydata/pandas/issues/11181
Is this testing OK? Or do we need to test with differing lengths of tuples etc?
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https://api.github.com/repos/pandas-dev/pandas/issues/11417 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11417/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11417/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11417/events | https://github.com/pandas-dev/pandas/issues/11417 | 112,942,663 | MDU6SXNzdWUxMTI5NDI2NjM= | 11,417 | using sort_values for sorting according to row | {
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```
728 562 599 642 592 686 602 744 640 678
```
309 96 29 99 63 73 48 81 48 13 25
337 40 25 41 47 14 33 54 63 33 45
395 64 95 63 90 52 82 66 26 26 20
264 38 56 73 17 98 56 80 77 44 49
I want to sort it h... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11418 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11418/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11418/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11418/events | https://github.com/pandas-dev/pandas/issues/11418 | 113,000,973 | MDU6SXNzdWUxMTMwMDA5NzM= | 11,418 | TST: Travis-CI failing due to URL not found | {
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> try:
> $ conda install -n root _license
> Fetching package metadata: .......Error: Could not find URL: http://conda.binstar.org/pandas/linux-64/
I have no clue what to do to fix this.
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https://api.github.com/repos/pandas-dev/pandas/issues/11419 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11419/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11419/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11419/events | https://github.com/pandas-dev/pandas/issues/11419 | 113,025,453 | MDU6SXNzdWUxMTMwMjU0NTM= | 11,419 | Segmentation fault happens when read_csv loads a big csv file | {
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... | closed | false | null | [] | null | 5 | 2015-10-23T14:02:35Z | 2015-10-26T11:47:40Z | 2015-10-26T11:13:47Z | NONE | null | Today I decided to upgrade pandas from 0.16.2 to 0.17.0, and I started to get a segmentation fault when reading a big csv file with the `read_csv` method. If I simply type the following code below in the python terminal, loading an 160Mb csv file, my script crashes displaying a segmentation fault message. Although, if ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11420 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11420/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11420/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11420/events | https://github.com/pandas-dev/pandas/issues/11420 | 113,069,323 | MDU6SXNzdWUxMTMwNjkzMjM= | 11,420 | Dropping a column not working in 0.17 | {
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] | closed | false | null | [] | null | 7 | 2015-10-23T17:57:44Z | 2015-10-23T18:16:52Z | 2015-10-23T18:13:40Z | NONE | null | I'm executing the following code:
```
import pandas as pd
df = pd.read_csv('2015-09-25.csv', header=None,\
names=['ID', 'ORIG', 'LAT', 'LONG', 'CITY', 'STATE', 'TIMESTAMP'], \
parse_dates=['TIMESTAMP'], \
usecols=['ORIG','LAT', 'LONG', 'CITY', 'STATE'])
df1 = df[df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11421 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11421/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11421/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11421/events | https://github.com/pandas-dev/pandas/issues/11421 | 113,084,345 | MDU6SXNzdWUxMTMwODQzNDU= | 11,421 | Add `na=` parameter to str.isalnum() and other tests | {
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"color": "0052cc"... | closed | false | null | [] | null | 3 | 2015-10-23T19:25:04Z | 2015-10-23T20:00:43Z | 2015-10-23T20:00:34Z | NONE | null | I have a use case where I want to select all the rows in a column that are not numbers:
`lc.loc[lc.id.str.isalpha()]`
Some rows in the `id` column are nan so I get a value error:
`ValueError: cannot index with vector containing NA / NaN values`
The work around is to use:
`lc[lc.id.str.contains('[A-z]', na=False)]`... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11422 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11422/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11422/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11422/events | https://github.com/pandas-dev/pandas/pull/11422 | 113,093,297 | MDExOlB1bGxSZXF1ZXN0NDg2MjYwNjQ= | 11,422 | BUG: implement .sort_index(...inplace=True) for #11402 | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-10-23T20:26:25Z | 2015-10-27T11:11:42Z | 2015-10-27T11:11:42Z | CONTRIBUTOR | null | closes #11402
Adds inplace sort_index() for Series.
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https://api.github.com/repos/pandas-dev/pandas/issues/11423 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11423/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11423/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11423/events | https://github.com/pandas-dev/pandas/issues/11423 | 113,122,941 | MDU6SXNzdWUxMTMxMjI5NDE= | 11,423 | incorrect warning for pandas.io.pytables.HDFStore() | {
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The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11424 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11424/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11424/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11424/events | https://github.com/pandas-dev/pandas/issues/11424 | 113,130,168 | MDU6SXNzdWUxMTMxMzAxNjg= | 11,424 | DOC: document level-dropping for empty columns | {
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... | 1 | 2015-10-24T02:39:57Z | 2021-06-12T00:55:28Z | null | CONTRIBUTOR | null | I might have missed it, but I couldn't find this behavior documented anywhere. It is occasionally useful, if a bit magical.
``` python
In [1]: df1 = pd.DataFrame([1,2,3], columns=[['a'],['b']])
In [2]: df2 = pd.DataFrame([1,2,3], columns=[['a'],['']])
In [5]: df1['a'], type(df1['a'])
Out[5]:
( b
0 1
1 2
2 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11425 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11425/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11425/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11425/events | https://github.com/pandas-dev/pandas/pull/11425 | 113,148,626 | MDExOlB1bGxSZXF1ZXN0NDg2NTI5NjQ= | 11,425 | DOC: add some examples to Index set operations | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11426 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11426/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11426/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11426/events | https://github.com/pandas-dev/pandas/pull/11426 | 113,150,758 | MDExOlB1bGxSZXF1ZXN0NDg2NTM2NDg= | 11,426 | DOC: fix cython example (failed on Windows) (GH6002) | {
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The signature of `apply_integrate_f_wrap` (http://pandas-docs.github.io/pandas-docs-travis/enhancingperf.html#more-advanced-techniques) used `Py_ssize_t` but on Windows that gives an error, as the dataframe is generated with numpy random arrays, which gives int32 on Windows, while `Py_ssize_t` is int64 o... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11427 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11427/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11427/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11427/events | https://github.com/pandas-dev/pandas/pull/11427 | 113,183,325 | MDExOlB1bGxSZXF1ZXN0NDg2NjUyNzg= | 11,427 | BUG: vectorized DateOffset match non-vectorized | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-10-24T21:09:45Z | 2016-01-15T01:03:40Z | 2015-12-13T20:42:04Z | CONTRIBUTOR | null | closes #11370
Also added a bit of documentation on the semantics for anchored offsets
currently blocked by #11406 - either need to change the vectorized or non-vectorized implementation of `QuarterBegin` As discussed in that issue, my opinion is the vectorized version is the "right" (or at least consistent) one.
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https://api.github.com/repos/pandas-dev/pandas/issues/11428 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11428/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11428/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11428/events | https://github.com/pandas-dev/pandas/issues/11428 | 113,212,063 | MDU6SXNzdWUxMTMyMTIwNjM= | 11,428 | .loc lookup with missing values | {
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0.16.2
(numpy version 1.10)
```
In [15]: s
Out[15]:
0
2001-01-01 2
2001-01-02 5
2001-01-03 8
2001-01-04 11
In [16]: ddd
Out[16]: array(['2001-01-04', '2001-01-02', '2001-01-04', '2001-01-14... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11429/events | https://github.com/pandas-dev/pandas/pull/11429 | 113,233,709 | MDExOlB1bGxSZXF1ZXN0NDg2ODExNjE= | 11,429 | CLN: (more) boundscheck warnings in tslib | {
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... | open | false | null | [] | null | 19 | 2015-10-25T17:54:12Z | 2021-04-20T05:56:33Z | null | CONTRIBUTOR | null | I assume that this was officially supported before. Haven't narrowed it down any more than sometime between 0.16.2 and 0.17.0.
``` python
In [1]: pd.__version__
Out[1]: '0.16.2'
In [2]: pd.date_range("Jan 1", "March 31", name="date")
Out[2]:
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',
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https://api.github.com/repos/pandas-dev/pandas/issues/11431 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11431/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11431/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11431/events | https://github.com/pandas-dev/pandas/issues/11431 | 113,321,737 | MDU6SXNzdWUxMTMzMjE3Mzc= | 11,431 | UnicodeEncodeError with to_sql and unicode column names | {
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To reproduce:
```
from sqlalchemy import create_engine
engine = create_engine('sqlite:///:memory:')
df = pd.DataFrame([[1,2],[3,4]], columns = [u'\xe9',u'b'])
df.to_sql('data', engine... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11432 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11432/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11432/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11432/events | https://github.com/pandas-dev/pandas/pull/11432 | 113,323,507 | MDExOlB1bGxSZXF1ZXN0NDg3MjAzNTg= | 11,432 | BUG: fix UnicodeEncodeError with to_sql and unicode column names (GH11431) | {
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Still have to add tests
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https://api.github.com/repos/pandas-dev/pandas/issues/11433 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11433/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11433/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11433/events | https://github.com/pandas-dev/pandas/issues/11433 | 113,357,620 | MDU6SXNzdWUxMTMzNTc2MjA= | 11,433 | Series(index=DatetimeIndex(...)) constructor very slow | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-10-26T13:42:56Z | 2015-11-15T16:55:16Z | 2015-11-15T16:55:16Z | CONTRIBUTOR | null | When instantiating a series with a datetimeindex but no data value (=None), a very slow codepath is used (involving Timestamp creation), compared to if a NaN value is supplied. The results are identical.
```
dr = pd.date_range(
start=datetime(2015,10,26),
end=datetime(2016,1,1),
freq='10s'
) # ~500k long
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https://api.github.com/repos/pandas-dev/pandas/issues/11434 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11434/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11434/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11434/events | https://github.com/pandas-dev/pandas/issues/11434 | 113,384,366 | MDU6SXNzdWUxMTMzODQzNjY= | 11,434 | Pandas groupby plot problem | {
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... | 11 | 2015-10-26T15:45:14Z | 2021-04-20T05:58:55Z | null | NONE | null | In Pandas 0.162 I used:
```
df[df['var']==True].groupby('var2').plot(ax=ax[1],x='dfcolumnx',y='dfcolumy',legend=False,use_index=False ,kind='line',style='o-')
```
this plots df['dfcolumx'] as the x-axis and df['dfcolumy'] as the y-axis on ax[1].
This is not true for pandas 0.17:
This gives the wrong plots: instead ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11435 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11435/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11435/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11435/events | https://github.com/pandas-dev/pandas/issues/11435 | 113,393,964 | MDU6SXNzdWUxMTMzOTM5NjQ= | 11,435 | Series.str.cat() with NaN in series returns NaN, rather than ignoring NaN | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2015-10-26T16:24:06Z | 2016-02-12T15:01:29Z | 2016-02-12T15:01:29Z | CONTRIBUTOR | null | I find this surprising as the rest of the pandas `Series.str.*` API ignores NaN values.
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: pd.__version__
Out[3]: u'0.17.0'
In [4]: s = pd.Series(['asdf','sdfg',np.nan,'qwer','wert'])
In [5]: s.str.cat(sep=' ')
Out[5]: nan
```
I think this sho... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11436 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11436/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11436/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11436/events | https://github.com/pandas-dev/pandas/issues/11436 | 113,483,661 | MDU6SXNzdWUxMTM0ODM2NjE= | 11,436 | DataFrame.duplicated detects duplicates when none exist | {
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"de... | closed | false | null | [] | null | 3 | 2015-10-27T01:13:04Z | 2020-06-26T13:29:17Z | 2015-10-27T01:16:05Z | NONE | null | Hello,
I'm running into what I think is a bug in DataFrame.duplicated where it detects duplicates, but the data frame does not actually have any duplicated rows. It seems to only happen with integer columns, and somewhat large datasets (>600,000 rows).
I created a test data set to show the issue:
```
df = pd.read... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11437 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11437/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11437/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11437/events | https://github.com/pandas-dev/pandas/issues/11437 | 113,536,978 | MDU6SXNzdWUxMTM1MzY5Nzg= | 11,437 | pandas2ri.py2ri(df) | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-10-27T09:12:16Z | 2018-05-15T03:11:17Z | 2015-11-01T16:21:42Z | NONE | null | i want use pandas2ri.py2ri(df) ro replace r_dataframe = com.convert_to_r_dataframe(df)
for my data,i find the old function can work well but the pyr2 report error
message like:
```
NotImplementedError
Traceback (most recent call last) <ipython-input-40-5278a5dfa2ad> in <module>()
18
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https://api.github.com/repos/pandas-dev/pandas/issues/11438 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11438/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11438/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11438/events | https://github.com/pandas-dev/pandas/issues/11438 | 113,565,004 | MDU6SXNzdWUxMTM1NjUwMDQ= | 11,438 | Tilde expansion for paths (~/...) inconsistent between read_csv and to_csv | {
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"description": "IO issue... | closed | false | null | [] | null | 6 | 2015-10-27T11:46:48Z | 2019-02-02T10:17:51Z | 2019-02-02T10:17:41Z | CONTRIBUTOR | null | When specifying a path in DataFrame.read_csv(), pandas has no problem with the tilde expansion:
``` python
In[1]:
import pandas as pd
test = pd.read_csv('~/Documents/test.csv', index_col=0)
test.head()
Out[1]:
Foo Bar
0 1 2
1 3 4
2 5 6
3 7 8
4 9 10
```
But ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11439 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11439/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11439/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11439/events | https://github.com/pandas-dev/pandas/pull/11439 | 113,573,735 | MDExOlB1bGxSZXF1ZXN0NDg4NjQ3OTc= | 11,439 | CI: Py3.5 / numpy 1.10 testing | {
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closes #11138
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https://api.github.com/repos/pandas-dev/pandas/issues/11440 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11440/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11440/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11440/events | https://github.com/pandas-dev/pandas/issues/11440 | 113,592,576 | MDU6SXNzdWUxMTM1OTI1NzY= | 11,440 | OverflowError when loading uint64 from csv | {
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"color": "531... | closed | false | null | [] | null | 15 | 2015-10-27T14:15:25Z | 2015-10-27T22:05:30Z | 2015-10-27T22:05:24Z | NONE | null | Hi,
I am trying to plot a set of values that are all uint64 with panda
The csv file is available here https://transfer.sh/Sy5DS/iperf-client-linux-2rtrs-f30b30-f30b30-w140k-lia-run1.csv and this is the command I used:
```
In [6]: df = pd.read_csv('/home/teto/ns3testing/iperf-client-linux_2rtrs_f30b30_f30b30_w140K_li... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11441 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11441/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11441/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11441/events | https://github.com/pandas-dev/pandas/pull/11441 | 113,594,865 | MDExOlB1bGxSZXF1ZXN0NDg4NzY5NDY= | 11,441 | Fix for DataFrame.hist() with by- and weights-keyword | {
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] | closed | false | null | [] | null | 19 | 2015-10-27T14:23:49Z | 2017-04-03T15:25:51Z | 2017-04-03T15:25:51Z | NONE | null | will make the following work
``` python
import numpy as np
import pandas as pd
d = {'one' : ['A', 'A', 'B', 'B', 'C'],
'two' : [4., 3., 2., 1., np.nan],
'three' : [10., 8., np.nan, 5., 7.]}
df = pd.DataFrame(d)
df.hist('two', by='one', weights='three', bins=range(0, 10))
# or
df.hist('two', by=df.one.va... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11442 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11442/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11442/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11442/events | https://github.com/pandas-dev/pandas/issues/11442 | 113,624,661 | MDU6SXNzdWUxMTM2MjQ2NjE= | 11,442 | Groupby.groups doesn't work by a groups convert from DateTimeIndex | {
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... | closed | false | null | [] | {
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i = pd.DatetimeIndex(pd.date_range('2015/01/01', periods=5), name='date')
d = pd.DataFrame({'A':[5,6,7,8,9], 'B':[1,2,3,4,5]}, index=i)
print i
print d
dg = d.groupby(level='date')
print dg.get_group('2015-01-01')
print dg.groups
```
**Output:**
```
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '201... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11443 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11443/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11443/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11443/events | https://github.com/pandas-dev/pandas/pull/11443 | 113,664,121 | MDExOlB1bGxSZXF1ZXN0NDg5MjEyMjE= | 11,443 | DOC:Remove supression from ipython directive to make object visible | {
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"followers_url": "https://api.github.com/users/jr... | 8 | 2015-10-27T19:37:12Z | 2015-11-14T19:32:56Z | 2015-11-14T19:32:53Z | CONTRIBUTOR | null | Fix for https://github.com/pydata/pandas/issues/11223
@TomAugspurger should these two Ipython blocks be merged into one?
(this is what it looks like at the mo)
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https://api.github.com/repos/pandas-dev/pandas/issues/11444 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11444/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11444/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11444/events | https://github.com/pandas-dev/pandas/issues/11444 | 113,679,827 | MDU6SXNzdWUxMTM2Nzk4Mjc= | 11,444 | Groupby aggregation of date/datetime columns returns datetime64 rather than numeric type | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-10-27T20:22:04Z | 2017-02-27T19:52:14Z | 2015-10-27T22:03:56Z | NONE | null | ```
import pandas as pd
import datetime
u = [datetime.datetime(2015, x, 1) for x in range(12)]
v = list('aaabbbbbbccd')
df = pd.DataFrame('X':v, 'Y':u)
df.groupby('X')['Y'].agg(len)
## Returns the following:
X
a 1970-01-01 00:00:00.000000003
b 1970-01-01 00:00:00.000000006
c 1970-01-01 00:00:00.000000002
d 1970... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11445 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11445/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11445/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11445/events | https://github.com/pandas-dev/pandas/pull/11445 | 113,694,360 | MDExOlB1bGxSZXF1ZXN0NDg5MzU2Mjc= | 11,445 | BUG: fix Panel.fillna() ignoring axis parameter (re-submission) | {
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"description": "np.nan, pd.NaT, pd.NA, d... | closed | false | null | [] | null | 6 | 2015-10-27T21:31:46Z | 2016-01-20T14:12:47Z | 2016-01-20T14:12:24Z | CONTRIBUTOR | null | closes #3570
closes #8251
This is a re-submission of PR #8395 and addresses issue #8251. My apologies for the duplicate PR, but despite rebasing to pydata/pandas master, the original PR would not update with my new commits and I couldn't get rid of the `LooseVersion()` errors in the Travis build.
This PR may need so... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11446 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11446/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11446/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11446/events | https://github.com/pandas-dev/pandas/issues/11446 | 113,696,320 | MDU6SXNzdWUxMTM2OTYzMjA= | 11,446 | ENH: Support returning the same dtype as the caller for window ops (including extension dtypes) | {
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... | 2 | 2015-10-27T21:42:07Z | 2021-09-01T04:51:25Z | null | NONE | null | In sample code below rolling_apply takes an argument 'ix' which is a numpy array of dtype = 'int64' and by the time this array gets to get_type() function, its dtype has changed to 'float64'. I can make an explicit call in get_type() function to change this back: ix = ix.astype('int64'), but was curious why it gets cha... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11447 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11447/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11447/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11447/events | https://github.com/pandas-dev/pandas/issues/11447 | 113,707,301 | MDU6SXNzdWUxMTM3MDczMDE= | 11,447 | ENH read_excel error when accessing AWS S3 URL | {
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read_excel fails with the following error:
``` python
>>> import pandas as pd
>>> df = pd.read_excel("s3://my-bucket/my_file.xlsx")
Traceback (most recent ... | {
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... | closed | false | null | [] | null | 1 | 2015-10-27T23:50:31Z | 2015-10-27T23:52:02Z | 2015-10-27T23:51:52Z | NONE | null | This below code segfaults every time
```
import pandas as pd
pd.DataFrame({'cat': ['a','b','a']})
df = df['cat'].astype('category')
df.to_json()
```
## INSTALLED VERSIONS
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.0.9-boot2docker
machine: x86_64
processor:
byteorder: little
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https://api.github.com/repos/pandas-dev/pandas/issues/11450 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11450/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11450/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11450/events | https://github.com/pandas-dev/pandas/pull/11450 | 113,716,014 | MDExOlB1bGxSZXF1ZXN0NDg5NTAyMzA= | 11,450 | CLN/PERF: remove used functions; use C skip list for rolling median | {
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reverts this commit https://github.com/pydata/pandas/commit/a40226e5c4c3d29e5e2383433c5f0b94c3c0ecc1
performance consideration
```
import pandas
import numpy
arr = numpy.random.rand(1000000)
%timeit pandas.rolling_median(arr, 1000)
```
master
```
1 loops, best of 3: 4.94 s per loop
```
br... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11451 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11451/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11451/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11451/events | https://github.com/pandas-dev/pandas/issues/11451 | 113,730,005 | MDU6SXNzdWUxMTM3MzAwMDU= | 11,451 | REGR: Panel.where | {
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``` python
In [78]: panel=pd.Panel(pd.np.random.rand(5,4,3))
In [80]: panel[0]
Out[80]:
0 1 2
0 0.191947 0.324781 0.070749
1 0.396628 0.966418 0.120066
2 0.551517 0.643907 0.230434
3 0.245471 0.485606 0.315219
In [79]: panel.where(panel>0.5, None)[... | {
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... | 8 | 2015-10-28T04:45:23Z | 2020-03-31T05:25:27Z | 2020-03-31T05:25:17Z | NONE | null | If you have a dataframe with a multiindex, and use groupby and then shift, using the frequency argument, the shifting fails, resulting in an empty result. For example:
``` python
import numpy as np
import pandas as pd
dates1 = pd.date_range('2015-10-01', '2015-10-03')
df1 = pd.DataFrame(dict(grp='A', blah=[1,2,3]), i... | {
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... | closed | false | null | [] | null | 2 | 2015-10-28T11:03:33Z | 2015-10-28T12:21:40Z | 2015-10-28T11:10:47Z | NONE | null | I am currently writing a script to take data from an API and save it to an Excel file for non-technical users who need to access the data. One of the requirements is to have separate date and time column, rather than a single datetime column.
I am using pandas 0.17.0.
``` python
import pandas as pd
# creating date... | {
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The operation is a addition. The results of the first two rows are correct most of the time, on occasion a value 1.5-315 appears. Every test performed with same input. Fail rate is about 3-5% of the time.
df['v1']+=10.0
I copied the dataframe results to numpy and perform the... | {
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```
import pandas as pd
s = pd.Series([pd.Timestamp('2011-01-01'), pd.NaT])
s
#0 2011-01-01
#1 NaT
# dtype: datetime64[ns]
stz = pd.Series(pd.DatetimeIndex(['2012-01-01', '2012-01-02'], tz='Asia/Tokyo... | {
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"des... | closed | false | null | [] | null | 1 | 2015-10-28T12:58:42Z | 2018-06-29T05:25:44Z | 2018-06-29T05:25:44Z | MEMBER | null | Followings should be changed to coerce to `object` dtype.
## Coerce to tz-naive / UTC
- [x] Slice (`__getitem__`) resets tz #11351
- [x] `.loc` assignment coerces to naive #11365
- [x] `GroupBy.first` coerces to naive #10668
- [x] `DataFrame.combine_first` #10567
- [x] `Index` creation #11488 -> changed to be... | {
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"id": ... | closed | false | null | [] | null | 2 | 2015-10-28T15:10:10Z | 2015-10-28T17:07:51Z | 2015-10-28T15:13:31Z | CONTRIBUTOR | null | This code generates an error in Pandas 0.17.0, but works fine in pandas 0.16.2
``` python
import datetime
import pandas as pd
import numpy as np
print pd.show_versions()
SHOW_BUG=True
if SHOW_BUG:
df = pd.DataFrame({
'a': 1 * np.ones(10),
'b': [datetime.datetime.now() for nn in range(10)]... | {
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Added calls to `_expand_user()` in multiple Formatter classes so that writer functions, such as `to_csv()` and `to_html()`, can perform tilde expansion on file paths beginning `~/`. This makes the writer functions consistent with the reader functions, such as `read_csv()`. I also changed the `__init__()`... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11459 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11459/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11459/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11459/events | https://github.com/pandas-dev/pandas/issues/11459 | 113,867,416 | MDU6SXNzdWUxMTM4Njc0MTY= | 11,459 | 0.17 drop_duplicates() incorrectly dropping non-unique values | {
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"de... | closed | false | null | [] | null | 1 | 2015-10-28T16:55:25Z | 2015-10-28T16:56:54Z | 2015-10-28T16:56:45Z | NONE | null | Under 0.17, numpy 1.10
```
In [32]: import pandas
In [33]: df = pandas.DataFrame(data={"a" : [0, 1, 3, 4], "b":[20, 16, 8, 4]})
In [34]: df
Out[34]:
a b
0 0 20
1 1 16
2 3 8
3 4 4
In [35]: df.drop_duplicates(['a','b'], keep='last')
Out[35]:
a b
3 4 4
```
0.16.2 has the correct behavior.
```
In... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11460 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11460/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11460/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11460/events | https://github.com/pandas-dev/pandas/pull/11460 | 113,869,383 | MDExOlB1bGxSZXF1ZXN0NDkwMzYxMzc= | 11,460 | implemented fix for groupby date bug, #11324 | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-10-28T17:04:13Z | 2015-11-13T21:48:21Z | 2015-11-13T21:48:21Z | CONTRIBUTOR | null | This is a fix for issue #11324 in which grouping when datetime fields are involved raises an exceptoin
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https://api.github.com/repos/pandas-dev/pandas/issues/11462 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11462/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11462/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11462/events | https://github.com/pandas-dev/pandas/pull/11462 | 113,896,607 | MDExOlB1bGxSZXF1ZXN0NDkwNTA4MjU= | 11,462 | CLN: move routines for filling missing data from core/common.py to core/missing.py | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-10-28T18:54:11Z | 2015-10-28T21:56:56Z | 2015-10-28T21:49:00Z | CONTRIBUTOR | null | This PR moves the routines used for filling missing data into their own module as suggested in PR #11445.
Thoughts on re-naming `interpolate_1d()` and `interpolate_2d()`? They way they are currently named makes them sound like they're the same functions for arrays of differing dimensions. However, `interpolate_1d()` i... | {
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] | closed | false | null | [] | null | 1 | 2015-10-28T19:40:02Z | 2015-10-28T19:49:50Z | 2015-10-28T19:49:41Z | NONE | null | I experienced a crash in 0.17.0 when using a pandas that's built from PyPI:
```
import pandas as pd
import StringIO
f=StringIO.StringIO("id\n2015091624765185258700105041865\n")
pd.read_csv(f)
==> Segmentation Fault
```
I traced it to a null pointer dereference in parser.c as found in the tarball that's
on PyPI. It do... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-10-28T22:25:24Z | 2015-11-13T17:17:58Z | 2015-11-13T17:17:58Z | CONTRIBUTOR | null | I was very confused about the purpose of the iterator and chunksize parameters when looking at the [help for read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html). It wasn't until I Google'd upon [the docs for IO Tools](http://pandas.pydata.org/pandas-docs/stable/io.html) that I began to... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11465 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11465/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11465/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11465/events | https://github.com/pandas-dev/pandas/issues/11465 | 113,973,986 | MDU6SXNzdWUxMTM5NzM5ODY= | 11,465 | X axis weirdness with DataFrame.plot(kind='bar') | {
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... | 7 | 2015-10-29T03:45:05Z | 2021-01-18T15:35:32Z | null | NONE | null | I encountered this issue when plotting some vertical axes spans on the same axes as a bar plot. Some example code is below to reproduce the issue. When using either scatter or line plots, the spans show up where they are supposed to, but the bar plot is off.
Digging a little deeper, I found that the plot call is setti... | {
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I have a file which has lot of \0 which are to be treated as line separator.
But above code is not separating based on '\0'.
Any fix for this?
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"f... | 7 | 2015-10-29T04:12:28Z | 2015-10-29T21:08:48Z | null | NONE | null | current lineseparator for pd.read_csv() takes only string with 1 char.
Please provide the facility to be able to break the lines based on regex pattern
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does not mention the keyword "names"
``` python
import pandas as pd
df = pd.read_excel('yourfile.xls', header=None, names=['Attribut', 'Value'])
```
is equivalent but simpler to
``` python
df = pd.read_excel(file, sheetname='Metadata', head... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11470 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11470/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11470/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11470/events | https://github.com/pandas-dev/pandas/pull/11470 | 114,028,984 | MDExOlB1bGxSZXF1ZXN0NDkxMjYzNDk= | 11,470 | ENH: Adding origin parameter in pd.to_datetime | {
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Fixes #11745
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https://api.github.com/repos/pandas-dev/pandas/issues/11471 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11471/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11471/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11471/events | https://github.com/pandas-dev/pandas/issues/11471 | 114,057,326 | MDU6SXNzdWUxMTQwNTczMjY= | 11,471 | plot with unsorted indices misses data points | {
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```
import pandas as pd
from numpy.random import rand
testarr1=rand(100)
testarr2=rand(100)
pd.DataFrame(testarr2,index=testarr1).plot(style='.')
pd.DataFrame(testarr2,index=testarr1).sort().plot(style='.')
```
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"descript... | closed | false | null | [] | null | 9 | 2015-10-29T14:27:54Z | 2019-03-14T10:33:28Z | 2015-10-29T14:34:16Z | NONE | null | here is my problem.
What I have is a DataFrame as follows:
```
df:
2013-10-24 1
2013-10-25 2
2013-11-27 3
2013-11-28 4
2013-12-01 5
2013-12-02 6
```
What I want is a DataFrame such like this:
rolling_mean(df, window='1M'):
```
2013-10 1.5
2013-11 3.5
2013-12 5.5
```
... | {
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>>> import pandas as pd; import pytz; import datetime; faulthandler
>>> faulthandler.enable()
>>> pd.DataFrame.from_records([{'ts': datetime.datetime(2015,1,1, tzinfo=pytz.UTC)}]).to_json()
Fatal Python error: Segmentation fault
Current thread 0x00007fff73afd300 (most recent call first):
File "<snip>lib/python2... | {
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"description": "Related t... | closed | false | null | [] | null | 3 | 2015-10-29T18:35:46Z | 2015-10-29T19:10:50Z | 2015-10-29T18:48:40Z | CONTRIBUTOR | null | I don't think this is related to #9732 or #10331, but maybe I'm missing something.
``` python
index = pd.MultiIndex.from_product([pd.date_range('2015-01-01', '2015-04-01', freq='8H'), ['A', 'B', 'C']], names=['level1', 'level2'])
s = pd.Series(np.random.randn(813), index=index)
```
Now I'm trying to select a slice f... | {
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"color": "00... | closed | false | null | [] | null | 12 | 2015-10-29T18:49:44Z | 2015-10-30T17:10:38Z | 2015-10-29T21:07:56Z | NONE | null | I appear to have some code that is sometimes (inexplicably) running in pure Python (i.e. **not** Cython-optimized). Specifically, the problem is with `loc[]` indexing on a boolean Series:
![pandas slow loc lookup example](https://cloud.githubusercontent.com/assets/9703332/10828484/e9995bbc-7e4a-11e5-9b00-ae367f328541.... | {
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"de... | closed | false | null | [] | null | 2 | 2015-10-29T20:07:46Z | 2015-10-29T21:06:05Z | 2015-10-29T21:05:46Z | NONE | null | Is it possible to pass all columns to query? e.g.
```
df.query('substring in all_columns')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11477 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11477/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11477/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11477/events | https://github.com/pandas-dev/pandas/issues/11477 | 114,139,340 | MDU6SXNzdWUxMTQxMzkzNDA= | 11,477 | Time Series / Date functionality Holiday lookup - possible bug | {
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In 2015, the July 4th hol... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11478 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11478/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11478/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11478/events | https://github.com/pandas-dev/pandas/issues/11478 | 114,186,844 | MDU6SXNzdWUxMTQxODY4NDQ= | 11,478 | Column headers from CSV are different when using read_csv vs DataFrame.from_csv | {
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"descript... | closed | false | null | [] | null | 3 | 2015-10-30T02:21:58Z | 2015-10-30T21:14:41Z | 2015-10-30T13:13:39Z | NONE | null | Steps to reproduce,
- Create a string representation of a CSV file
- Convert it into a StringIO object
- Read it using pandas.read_csv
- Read it using pandas.DataFrame.from_csv
- The parsed headers will be different
```
# Testcase
import pandas
import cStringIO as StringIO
data = """col1|col2\ndata1|data2\ndata3|data... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11479 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11479/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11479/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11479/events | https://github.com/pandas-dev/pandas/issues/11479 | 114,220,961 | MDU6SXNzdWUxMTQyMjA5NjE= | 11,479 | BUG: reset_index of level on a MultiIndex with NaT converts to np.nan | {
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It's similar to this closed one:
https://github.com/pydata/pandas/issues/10388
using pandas 0.17 and numpy 1.10.1
code to reproduce the issues:
```
import pandas as pd
from pandas import DataFrame
import numpy as np
idx = np.arange(0, 10) # could ha... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11480 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11480/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11480/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11480/events | https://github.com/pandas-dev/pandas/issues/11480 | 114,243,191 | MDU6SXNzdWUxMTQyNDMxOTE= | 11,480 | read_csv not parsing numeric na_values against floats | {
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] | closed | false | null | [] | null | 1 | 2015-10-30T10:32:23Z | 2015-10-30T12:29:50Z | 2015-10-30T12:29:46Z | NONE | null | On Pandas 0.17.0, `read_csv` fails with a `TypeError` when csv file contain floats and `na_values` contains numeric value(s). Consider this example:
``` python
#!/usr/bin/env python2
import numpy as np
import pandas as pd
pd.show_versions()
# create a dataframe filled with integers 0 to 15
data = np.arange(16).resha... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11481 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11481/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11481/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11481/events | https://github.com/pandas-dev/pandas/issues/11481 | 114,248,490 | MDU6SXNzdWUxMTQyNDg0OTA= | 11,481 | Wrong hour of DST end for Europe TZ | {
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In Europe DST ends by 2 am (this year on Sunday 2015-10-25), when clock is moved one hour back. However in Timestamp this transition is one hour earlier ( 1 am - which is hour, when transition happens in US I think). pytz works as expected.
See code ( tested in pandas 0.17.0 and 0.16.2):
``` ipython
I... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11482 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11482/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11482/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11482/events | https://github.com/pandas-dev/pandas/issues/11482 | 114,248,954 | MDU6SXNzdWUxMTQyNDg5NTQ= | 11,482 | fillna(method='ffill') fills all NA values with the same value. | {
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If I now to:
``` python
s.fillna(method='ffill').plot()
```
I end up w... | {
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... | open | false | null | [] | null | 4 | 2015-10-30T12:21:54Z | 2021-04-20T06:01:34Z | null | NONE | null | Found this when working on
https://github.com/pydata/pandas/pull/11441
``` python
import pandas as pd
d = {'one' : ['A', 'A', 'B', 'B', 'C'],
'two' : [4., 3., 2., 2, 1],
'three' : [10., 8., 3, 5, 7.]}
df = pd.DataFrame(d)
# this works
df.hist('two', by='one', bins=range(0, 10))
# this does not work ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11484 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11484/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11484/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11484/events | https://github.com/pandas-dev/pandas/pull/11484 | 114,277,431 | MDExOlB1bGxSZXF1ZXN0NDkyNzM1NTA= | 11,484 | BUG: Holiday observance rules could not be applied | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-10-30T14:11:20Z | 2015-11-14T14:57:29Z | 2015-11-14T14:57:23Z | CONTRIBUTOR | null | Closes #11477
Closes #11533
There were some other bugs here that I added tests for. For example, `holiday.dates` for MLK day was returning all holidays from the holiday start date up to the end date rather than just between the range.
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https://api.github.com/repos/pandas-dev/pandas/issues/11485 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11485/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11485/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11485/events | https://github.com/pandas-dev/pandas/issues/11485 | 114,331,487 | MDU6SXNzdWUxMTQzMzE0ODc= | 11,485 | API: chained reshaping ops | {
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- ~~`.query`~~ (not changed, see #12539)
- [x] `.where/.mask` (#12539)
- [x] `.loc` (and indexers) (#12539)
```
In [4]: df = DataFrame({'A' : [1,2,3,4], 'B' : ['a',np.nan,'b','a']})
In [5]: df
Out[5]:
A B
0 1 a
1 2 NaN
2 3 b
3 4 a
```
an operation that changes the shape of the D... | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-10-30T19:25:38Z | 2015-10-31T04:22:11Z | 2015-10-30T22:08:05Z | CONTRIBUTOR | null | Fix https://github.com/pydata/pandas/issues/10770 by adding days_in_month to Time/Date Components. Am I right in thinking `days_in_month` (rather than `daysinmonth`) is the right name to use going forward? Is there anywhere else in the documentation that it should be added that I missed?
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] | closed | false | null | [] | null | 14 | 2015-10-30T22:45:08Z | 2015-11-20T19:59:36Z | 2015-11-20T19:59:36Z | NONE | null | The following example will lead to a segfault error (with pandas 0.17.0 and matplotlib 1.5.0)
`import pandas as pd`
`import matplotlib.pyplot as plt`
`df = pd.DataFrame({'data': range(10)})`
`df.plot()`
`plt.show()`
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https://api.github.com/repos/pandas-dev/pandas/issues/11488 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11488/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11488/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11488/events | https://github.com/pandas-dev/pandas/issues/11488 | 114,379,906 | MDU6SXNzdWUxMTQzNzk5MDY= | 11,488 | API: DatetimeIndex creation with mixed tz timestamps | {
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"closed_at": "2016-03-12T16:19:08Z",
"closed_issues": 469,
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```
pd.Index([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02', tz='US/Eastern')])
# DatetimeIndex(['2010-12-31 19:00:00-05:00', '2011-01-02 00:00:00-05:00'], dtyp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11489 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11489/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11489/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11489/events | https://github.com/pandas-dev/pandas/pull/11489 | 114,393,052 | MDExOlB1bGxSZXF1ZXN0NDkzNDM1MDI= | 11,489 | ENH: Standardized timeseries accessor names, #9606 | {
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... | closed | false | null | [] | null | 15 | 2015-10-31T06:53:19Z | 2016-01-30T15:47:59Z | 2016-01-30T15:47:59Z | CONTRIBUTOR | null | In accordance with https://github.com/pydata/pandas/issues/9606, I added `day_of_week` and `day_of_year` accessors to DatetimeIndex, Timestamp, and Period. Since https://github.com/pydata/pandas/issues/9606 didn't mention `weekofyear`, and there's already `week` as a replacement for that, I didn't add a `week_of_year`,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11490 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11490/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11490/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11490/events | https://github.com/pandas-dev/pandas/issues/11490 | 114,404,227 | MDU6SXNzdWUxMTQ0MDQyMjc= | 11,490 | Empty preconstructed pandas results in error in Variable Explorer | {
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iv_bd = pd.DataFrame(columns=['Jan','Feb','Mar'])
It causes an internal error instead of showing something like e.g. "empty Dataframe"
```
Spyder Internal Console
This console is used to report application
internal errors and to inspect Spyder
internals with the following commands:
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https://api.github.com/repos/pandas-dev/pandas/issues/11491 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11491/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11491/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11491/events | https://github.com/pandas-dev/pandas/pull/11491 | 114,422,483 | MDExOlB1bGxSZXF1ZXN0NDkzNTQyMzI= | 11,491 | Update parser.pyx | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11492 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11492/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11492/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11492/events | https://github.com/pandas-dev/pandas/issues/11492 | 114,430,159 | MDU6SXNzdWUxMTQ0MzAxNTk= | 11,492 | DataFrame.where() fails with some binary comparisons | {
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] | closed | false | null | [] | null | 2 | 2015-10-31T19:46:07Z | 2015-11-18T12:12:59Z | 2015-11-18T12:12:59Z | NONE | null | Suppose we want to cap values in a dataframe of shape (6,5).
``` python
import pandas as pd,numpy as np
# WORKS WITH DF OF SHAPE (6,5)
ix=pd.date_range('1/1/2015',periods=6,freq='D')
df0=pd.DataFrame(np.random.normal(size=(len(ix),5)),index=ix)
print df0
df1=df0.quantile(q=.5,axis=1)
print df1
df2=df0.where(df0<df1,df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11493 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11493/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11493/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11493/events | https://github.com/pandas-dev/pandas/issues/11493 | 114,430,691 | MDU6SXNzdWUxMTQ0MzA2OTE= | 11,493 | read_csv "CParserError: Error tokenizing data" with variable number of fields | {
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"descript... | closed | true | null | [] | null | 21 | 2015-10-31T19:59:08Z | 2021-06-18T11:32:25Z | 2015-10-31T23:23:41Z | NONE | resolved | I am having trouble with `read_csv` (Pandas 0.17.0) when trying to read a 380+ MB csv file. The file starts with 54 fields but some lines have 53 fields instead of 54. Running the below code gives me the following error:
```
parser = lambda x: datetime.strptime(x, '%y %m %d %H %M %S %f')
df = pd.read_csv(filename,
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https://api.github.com/repos/pandas-dev/pandas/issues/11494 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11494/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11494/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11494/events | https://github.com/pandas-dev/pandas/issues/11494 | 114,433,609 | MDU6SXNzdWUxMTQ0MzM2MDk= | 11,494 | AssertionError: Number of Block dimensions (1) must equal number of axes (2) | {
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] | closed | false | null | [] | null | 6 | 2015-10-31T20:51:08Z | 2015-11-05T12:56:44Z | 2015-11-05T12:56:04Z | NONE | null | The following code throws AssertionError
df = pd.DataFrame({"x-1": X[:,0], "x-2": X[:,1], "label": np.where(y==1, "Driver's License", "Check")})
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https://api.github.com/repos/pandas-dev/pandas/issues/11495 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11495/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11495/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11495/events | https://github.com/pandas-dev/pandas/issues/11495 | 114,436,804 | MDU6SXNzdWUxMTQ0MzY4MDQ= | 11,495 | pandas.tests.test_panel4d.TestPanel4d.test_sum fails in 0.17.0 | {
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"d... | closed | false | null | [] | null | 2 | 2015-10-31T22:00:30Z | 2015-11-03T02:19:27Z | 2015-11-02T12:04:30Z | CONTRIBUTOR | null | ```
% nosetests -v pandas.tests.test_panel4d:TestPanel4d.test_sum
test_sum (pandas.tests.test_panel4d.TestPanel4d) ... FAIL
======================================================================
FAIL: test_sum (pandas.tests.test_panel4d.TestPanel4d)
---------------------------------------------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11496 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11496/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11496/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11496/events | https://github.com/pandas-dev/pandas/pull/11496 | 114,437,538 | MDExOlB1bGxSZXF1ZXN0NDkzNjAwODA= | 11,496 | ENH: support Akima 1D interpolation | {
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"description": "np.nan, ... | closed | false | null | [] | null | 3 | 2015-10-31T22:12:28Z | 2018-05-31T14:13:33Z | 2016-01-20T14:13:35Z | CONTRIBUTOR | null | I went hunting for some low-hanging fruit in the area of missing data and came across #7588. I had to implement it a little differently than krogh, pchip, etc. because there is no convenience function for Akima like there are for the already supported methods.
Once #11445 is merged I can rebase.
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https://api.github.com/repos/pandas-dev/pandas/issues/11497 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11497/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11497/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11497/events | https://github.com/pandas-dev/pandas/pull/11497 | 114,466,497 | MDExOlB1bGxSZXF1ZXN0NDkzNzA1NTM= | 11,497 | BUG: .loc with duplicated label may have incorrect index dtype | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-11-01T12:05:05Z | 2015-11-29T19:42:00Z | 2015-11-29T18:01:15Z | MEMBER | null | `.loc` result with duplicated keys may have incorred `Index` dtype.
```
import pandas as pd
ser = pd.Series([0.1, 0.2], index=pd.Index([1, 2], name='idx'))
# OK
ser.loc[[2, 2, 1]].index
# Int64Index([2, 2, 1], dtype='int64', name=u'idx')
# NG, Int64Index(dtype=object)
ser.loc[[3, 2, 3]].index
# Int64Index([3, 2, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11498 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11498/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11498/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11498/events | https://github.com/pandas-dev/pandas/issues/11498 | 114,470,708 | MDU6SXNzdWUxMTQ0NzA3MDg= | 11,498 | Sum across NaNs yields zero? | {
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"color": "006b75"... | closed | false | null | [] | null | 2 | 2015-11-01T13:54:36Z | 2015-11-01T14:46:53Z | 2015-11-01T14:46:53Z | NONE | null | I'm running pandas 0.17.0 and was surprised by this behavior, which seems inconsistent with the docs (I expected to get NaN, not zero, at index 2). If I substitute "None" for "np.nan", I get the same result, but I get the expected result--NaN--if I use "NaT". Wondering if I've stumbled on a bug . . .
```
In [136]: df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11499 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11499/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11499/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11499/events | https://github.com/pandas-dev/pandas/issues/11499 | 114,474,098 | MDU6SXNzdWUxMTQ0NzQwOTg= | 11,499 | Support for openpyxl when reading XLSX (Excel 2010) files | {
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{
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"default": false,
"description": "read_exc... | closed | false | null | [] | {
"closed_at": "2019-07-19T00:34:29Z",
"closed_issues": 1289,
"created_at": "2018-10-23T02:34:15Z",
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"followers_url": "https://api.github.com/users/j... | 13 | 2015-11-01T14:58:05Z | 2019-11-22T19:38:44Z | 2019-06-28T16:44:10Z | CONTRIBUTOR | null | I took a brief look at the existing code for importing files with xlrd but it looks tightly coupled with that library and not particularly straightforward to adapt.
I think that the read-only mode of openpyxl would be a good fit for Pandas and would like to work with you to provide the necessary API to go from rows to... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11500 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11500/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11500/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11500/events | https://github.com/pandas-dev/pandas/pull/11500 | 114,479,488 | MDExOlB1bGxSZXF1ZXN0NDkzNzU0MTA= | 11,500 | Copy on write using weakrefs | {
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"description": ""... | closed | false | null | [] | null | 20 | 2015-11-01T16:41:21Z | 2020-09-06T18:46:05Z | 2016-01-11T13:49:28Z | CONTRIBUTOR | null | Working model of copy-on-write. Aims to close #10954, alternative to #10973, extension of #11207.
## Copy-on-Write Behavior:
**Setting on child doesn't affect parent, but still uses views when can for efficiency**
```
parent = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
child = parent.loc[0:0,]
child._is_view
Out[1]:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11501 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11501/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11501/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11501/events | https://github.com/pandas-dev/pandas/issues/11501 | 114,482,514 | MDU6SXNzdWUxMTQ0ODI1MTQ= | 11,501 | Pandas display.date option not working | {
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... | 9 | 2015-11-01T17:40:53Z | 2015-11-01T19:14:02Z | 2015-11-01T19:14:02Z | NONE | null | pandas: 0.17.0
python: 3.5.0.final.0
```
import pandas as pd
list1 = {"Date":["01/11/15"]} #1st November 2015
pd.set_option('display.date_dayfirst', True)
#pd.set_option('display.date_yearfirst', False)
df = pd.DataFrame(list1, columns=['Date'])
df['Date'] = pd.to_datetime(df['Date'], dayfirst = [True])
df1 = df.sort... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11502 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11502/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11502/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11502/events | https://github.com/pandas-dev/pandas/issues/11502 | 114,484,917 | MDU6SXNzdWUxMTQ0ODQ5MTc= | 11,502 | ValueError: buffer source array is read-only | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-11-01T18:37:24Z | 2016-01-14T12:42:46Z | 2016-01-14T12:42:46Z | NONE | null | After upgrading to 0.17 (anaconda 2.7 + OSX), I'm finding that some previously working code is failing with `ValueError: buffer source array is read-only`. Driver code is below. The issue is basically the series version of #10043. I think the problem is that the patch for #10043 (#10070) only fixed the `take_2d` tem... | {
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