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https://api.github.com/repos/pandas-dev/pandas/issues/11003 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11003/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11003/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11003/events | https://github.com/pandas-dev/pandas/pull/11003 | 105,041,075 | MDExOlB1bGxSZXF1ZXN0NDQyODc3MzY= | 11,003 | ENH: add Series.astype with the new tz dtype | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11004 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11004/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11004/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11004/events | https://github.com/pandas-dev/pandas/pull/11004 | 105,041,076 | MDExOlB1bGxSZXF1ZXN0NDQyODc3Mzc= | 11,004 | DOC: consistent doc-string with function declaration, added missing param doc-string for sql.py | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11005 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11005/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11005/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11005/events | https://github.com/pandas-dev/pandas/issues/11005 | 105,041,505 | MDU6SXNzdWUxMDUwNDE1MDU= | 11,005 | Errors when running nosetest test_excel.py | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11007 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11007/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11007/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11007/events | https://github.com/pandas-dev/pandas/issues/11007 | 105,043,411 | MDU6SXNzdWUxMDUwNDM0MTE= | 11,007 | BUG: to_excel swaps order of values of duplicate columns | {
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```
In [1]: df = pd.DataFrame([[1,2,3,4],[5,6,7,8]], columns=['A','B','A','B'])
In [2]: df
Out[2]:
A B A B
0 1 2 3 4
1 5 6 7 8
In [4]: df.to_excel('test_excel_duplicate_columns.xlsx')
```
gives:
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before this patch:
```
In [1]: from string import ascii_lowercase
In [2]: np.random.seed(2718281)
In [3]: n = 1 << 21
In [4]: dr = date_range('2015-08-30', periods=n // 10, freq='T')
In [5]: df = DataFrame({
...: '1st':np.random.choice(list(ascii_lowercase), n),
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https://api.github.com/repos/pandas-dev/pandas/issues/11012 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11012/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11012/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11012/events | https://github.com/pandas-dev/pandas/issues/11012 | 105,052,470 | MDU6SXNzdWUxMDUwNTI0NzA= | 11,012 | read_csv index callback functionality | {
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"descript... | closed | false | null | [] | null | 3 | 2015-09-05T22:06:11Z | 2015-09-05T22:13:34Z | 2015-09-05T22:13:34Z | NONE | null | I have 3 csv files, which could be linked together via different columns. I want to merge all the 3 csv files in 1 dataframe.
```
import pandas as pd
a_df = pd.read_csv(a_file, sep=';', index_col=['booking_id'])
b_df = pd.read_csv(b_file, sep=';', index_col=['ga:adwordsCreativeID', 'ga:date', 'ga:transactionId'])
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https://api.github.com/repos/pandas-dev/pandas/issues/11013 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11013/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11013/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11013/events | https://github.com/pandas-dev/pandas/pull/11013 | 105,054,360 | MDExOlB1bGxSZXF1ZXN0NDQyOTE2MDY= | 11,013 | CLN: removes cython implementation of groupby count | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11014 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11014/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11014/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11014/events | https://github.com/pandas-dev/pandas/issues/11014 | 105,125,536 | MDU6SXNzdWUxMDUxMjU1MzY= | 11,014 | ValueError adding boolean dataframe to panel fails in 0.16.2 but not 0.15.2 | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-09-07T00:34:38Z | 2015-09-07T20:32:13Z | 2015-09-07T20:32:13Z | NONE | null | In Pandas 0.16.2 an attempt to assign a boolean array (created by pd.notnull) to a Panel fails with "ValueError: could not broadcast input array from shape...". The same code is successful using Pandas 0.15.2.
``` python
>>> import pandas as pd
>>> import numpy as np
>>>
>>> # create a test panel
... dates = pd.date... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11015 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11015/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11015/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11015/events | https://github.com/pandas-dev/pandas/pull/11015 | 105,143,718 | MDExOlB1bGxSZXF1ZXN0NDQzMTkxNDY= | 11,015 | BUG: .ix with PeriodIndex is fixed | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-09-07T04:21:55Z | 2015-12-08T03:52:34Z | 2015-09-09T21:40:50Z | CONTRIBUTOR | null | Solves https://github.com/pydata/pandas/issues/4125
It doesn't feel like an elegant solution, but I think it works.
I think when Period gets its own dtype, these will all go away?
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https://api.github.com/repos/pandas-dev/pandas/issues/11016 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11016/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11016/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11016/events | https://github.com/pandas-dev/pandas/issues/11016 | 105,225,302 | MDU6SXNzdWUxMDUyMjUzMDI= | 11,016 | groupby on NaN-only column gives IndexError | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-09-07T14:27:19Z | 2015-09-09T12:39:53Z | 2015-09-09T11:17:16Z | NONE | null | Using pandas from master (0.16.2+590.g81b647f) in Python 3.4.2, the following code gives an `IndexError: index out of bounds`:
```
import pandas as pd, numpy as np
df = pd.DataFrame(dict(a=[np.nan]*3, b=[1,2,3]))
g = df.groupby(('a', 'b'))
len(g) # IndexError
```
The same problem occurs when calling `list(g)` instea... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11017 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11017/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11017/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11017/events | https://github.com/pandas-dev/pandas/pull/11017 | 105,228,564 | MDExOlB1bGxSZXF1ZXN0NDQzNjIwNDA= | 11,017 | BUG: Index dtype may not be applied properly | {
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"followers_url": "https://api.github.com/users/jr... | 10 | 2015-09-07T14:46:47Z | 2015-09-11T14:36:06Z | 2015-09-11T14:24:25Z | MEMBER | null | Fixed 2 problems:
- Specified `dtype` is not applied to other iterables.
```
import numpy as np
import pandas as pd
pd.Index([1, 2, 3], dtype=int)
# Index([1, 2, 3], dtype='object')
```
- Specifying `category` to ndarray-like results in `TypeError`
```
pd.Index(np.array([1, 2, 3]), dtype='category')
# TypeError: dat... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11018 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11018/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11018/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11018/events | https://github.com/pandas-dev/pandas/pull/11018 | 105,238,541 | MDExOlB1bGxSZXF1ZXN0NDQzNjY3NTQ= | 11,018 | BUG: Unable to infer negative freq | {
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- `date_range` can't handle negative calendar-based freq (like `A`, `Q` and `M`).
```
import pandas as pd
# OK
pd.date_range('2011-01-01', freq='-1D', periods=3)
# DatetimeIndex(['2011-01-01', '2010-12-31', '2010-12-30'], dtype='datetime64[ns]', freq='-1D')
# NG
pd.date_range('2011-01-01', freq='-1M'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11019 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11019/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11019/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11019/events | https://github.com/pandas-dev/pandas/issues/11019 | 105,239,841 | MDU6SXNzdWUxMDUyMzk4NDE= | 11,019 | read_csv: Inconsistent behaviour with parse_dates if the first row is short | {
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... | 1 | 2015-09-07T15:55:08Z | 2021-04-18T23:34:07Z | null | NONE | null | I suspect this is related to #6710.
```
import pandas
from io import StringIO
t = "a,b,c,d\n1970-01-01,2,3,4"
s2 = "a,b,c\n1970-01-01,2,3\n1980-01-01,2,3,4"
s = "a,b,c\n1970-01-01,2,3,4"
pandas.read_csv(StringIO(t), parse_dates=['a'])
pandas.read_csv(StringIO(s2), parse_dates=['a'])
pandas.read_csv(StringIO(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11020 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11020/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11020/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11020/events | https://github.com/pandas-dev/pandas/issues/11020 | 105,247,285 | MDU6SXNzdWUxMDUyNDcyODU= | 11,020 | pandas.read_csv(..., skiprows=2, engine='c') with unix-style line breaks crashes python on windows | {
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"followers_url": "https://api.github.com/users/wesm... | 5 | 2015-09-07T16:59:35Z | 2020-01-04T17:25:15Z | 2020-01-04T17:25:15Z | NONE | null | The following makes python crash ("Kernel died, restarting" in IPython) on my windows 7 machine:
```
import pandas
myfile = open("test.csv", "w", newline="\n")
myfile.write("blah\n\ncol_1,col_2,col_3\n\n")
myfile.close()
dat = pandas.read_csv("test.csv", skiprows=2, encoding="utf-8", engine="c")
```
Note the unix-sty... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11021 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11021/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11021/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11021/events | https://github.com/pandas-dev/pandas/pull/11021 | 105,255,358 | MDExOlB1bGxSZXF1ZXN0NDQzNzQ2Njc= | 11,021 | BUG: Exception when setting a major- or minor-axis slice of a Panel with RHS a DataFrame | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-09-07T18:33:05Z | 2015-09-19T00:38:09Z | 2015-09-07T20:16:06Z | CONTRIBUTOR | null | Fixes GH #11014
This isn't actually a recent regression. The bug was introduced in 0.15.0 with this commit: https://github.com/pydata/pandas/commit/30246a7d2b113b7115fd0708655ae8e1c3f2e6c5; it was just made more common (i.e., it happens on panels without mixed dtype) in 0.16.2 by this commit: https://github.com/pydata... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11022 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11022/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11022/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11022/events | https://github.com/pandas-dev/pandas/issues/11022 | 105,257,959 | MDU6SXNzdWUxMDUyNTc5NTk= | 11,022 | datetime64[ns] astype datetime64[s] | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2015-09-07T19:05:11Z | 2016-11-30T15:30:51Z | 2015-09-07T20:14:54Z | NONE | null | Hello all,
I'm trying to convert a `datetime64[ns]` object to a unix timestamp without an `apply`.
I was wondering if it was the desired behavior to throw an error when trying to do:
``` python
import pandas as pd
import datetime as dt
import numpy as np
my_time = np.datetime64(dt.datetime.utcnow())
my_df = pd.DataFr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11023 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11023/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11023/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11023/events | https://github.com/pandas-dev/pandas/pull/11023 | 105,273,878 | MDExOlB1bGxSZXF1ZXN0NDQzODE4Mjk= | 11,023 | PERF: use NaT comparisons in int64/datetimelikes #11010 | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-09-07T22:43:56Z | 2015-09-08T00:30:05Z | 2015-09-08T00:30:05Z | CONTRIBUTOR | null | closes #11010
```
In [10]: %timeit gr['3rd'].count()
100 loops, best of 3: 15.1 ms per loop
In [11]: %timeit gr['3rd'].min()
100 loops, best of 3: 8.4 ms per loop
In [12]: %timeit gr['3rd'].max()
100 loops, best of 3: 8.47 ms per loop
In [13]: %timeit gr['3rd'].first()
100 loops, best of 3: 10.5 ms per loop
In [1... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11024/events | https://github.com/pandas-dev/pandas/pull/11024 | 105,376,756 | MDExOlB1bGxSZXF1ZXN0NDQ0Mjk2NzA= | 11,024 | BUG: DatetimeIndex.freq can defer by ufunc | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-09-08T13:07:49Z | 2015-09-09T08:47:50Z | 2015-09-09T08:47:32Z | MEMBER | null | Follow up of #10638. `DatetimeIndex.freq` also can be changed when other arg is an ndarray.
As the same as #10638, freq is re-infered only when it exists. Otherwise, leave it as `None` even if the result can be on specific freq. This is based on other functions behavior as below:
```
idx = pd.DatetimeIndex(['2011-01-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11025/events | https://github.com/pandas-dev/pandas/pull/11025 | 105,385,359 | MDExOlB1bGxSZXF1ZXN0NDQ0MzQzOTg= | 11,025 | DOC: Add GroupBy.count | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-09-08T13:51:54Z | 2015-09-08T20:35:25Z | 2015-09-08T15:21:31Z | MEMBER | null | Related to #11013. Define `GroupBy.count` for API doc and added docstring.
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https://api.github.com/repos/pandas-dev/pandas/issues/11026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11026/events | https://github.com/pandas-dev/pandas/issues/11026 | 105,421,388 | MDU6SXNzdWUxMDU0MjEzODg= | 11,026 | DataFrame fast search | {
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... | 15 | 2015-09-08T16:40:44Z | 2020-04-17T03:20:19Z | null | NONE | null | Would be really cool if would be implemented to quickly find and retrieve rows from DataFrame. Currently I previously sort a field and the use "**searchsorted**" function in order to get the index of the selection... but this is limited to one field only and still doesn't handle multiple searches. It would be really s... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11027 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11027/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11027/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11027/events | https://github.com/pandas-dev/pandas/pull/11027 | 105,429,704 | MDExOlB1bGxSZXF1ZXN0NDQ0NTkyNjQ= | 11,027 | Fix common typo in documentation | {
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] | closed | false | null | [] | null | 1 | 2015-09-08T17:22:31Z | 2015-09-08T17:28:01Z | 2015-09-08T17:27:36Z | CONTRIBUTOR | null | documenation -> documentation
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https://api.github.com/repos/pandas-dev/pandas/issues/11028 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11028/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11028/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11028/events | https://github.com/pandas-dev/pandas/pull/11028 | 105,463,640 | MDExOlB1bGxSZXF1ZXN0NDQ0Nzk1ODA= | 11,028 | Fix for DataFrame.hist() with by- and weights-keyword | {
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for example:
``` python
import pandas as pd
d = {'one' : ['A', 'A', 'B', 'C'],
'two' : [4., 3., 2., 1.],
'three' : [10., 8., 5., 7.]}
df = pd.DataFrame(d)
df.hist('two', by='one', weights='three', bins=range(0, 10))
```
does not seem to break anythin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11029/events | https://github.com/pandas-dev/pandas/issues/11029 | 105,471,929 | MDU6SXNzdWUxMDU0NzE5Mjk= | 11,029 | DataFrameGroupBy.filter returns empty dataframe when grouping by multiple columns and one column is type datetime64[ns] | {
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... | closed | false | null | [] | null | 2 | 2015-09-08T21:22:16Z | 2015-09-08T21:57:46Z | 2015-09-08T21:44:41Z | NONE | null | There seems to be a bug in the `filter` method when grouping by multiple columns, where one of the grouping columns has type `datetime64[ns]`. Here is an example:
``` python
data = [
('a', pd.Timestamp('2015-08-01'), 0),
('a', pd.Timestamp('2015-08-01'), 0),
('a', pd.Timestamp('2015-08-02'), 1),
('b', ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11030 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11030/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11030/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11030/events | https://github.com/pandas-dev/pandas/pull/11030 | 105,498,259 | MDExOlB1bGxSZXF1ZXN0NDQ1MDEyMDk= | 11,030 | BUG: Fixed bug in len(DataFrame.groupby) causing IndexError when there's a NaN-only column (issue11016) | {
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... | closed | false | null | [] | null | 3 | 2015-09-09T01:18:09Z | 2015-09-09T01:52:44Z | 2015-09-09T01:52:42Z | CONTRIBUTOR | null | Fixed #11016
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https://api.github.com/repos/pandas-dev/pandas/issues/11031 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11031/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11031/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11031/events | https://github.com/pandas-dev/pandas/pull/11031 | 105,501,785 | MDExOlB1bGxSZXF1ZXN0NDQ1MDI4MDQ= | 11,031 | BUG: Fixed bug in len(DataFrame.groupby) causing IndexError when there's a NaN-only column (issue11016) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11032/events | https://github.com/pandas-dev/pandas/issues/11032 | 105,511,203 | MDU6SXNzdWUxMDU1MTEyMDM= | 11,032 | BUG?: Runtime Warning invalid value encountered in greater has_large_values | {
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```
/Users/Nick/github/pandas/pandas/core/format.py:2052: RuntimeWarning: invalid value encountered in greater... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11033 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11033/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11033/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11033/events | https://github.com/pandas-dev/pandas/issues/11033 | 105,575,631 | MDU6SXNzdWUxMDU1NzU2MzE= | 11,033 | accept pathlib paths wherever a path/file is expected | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11034/events | https://github.com/pandas-dev/pandas/issues/11034 | 105,599,200 | MDU6SXNzdWUxMDU1OTkyMDA= | 11,034 | Timestamp equality broadcasts incorrectly | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-09-09T13:43:38Z | 2015-09-09T21:09:28Z | 2015-09-09T21:09:28Z | CONTRIBUTOR | null | Always returns true for equality and inequality:
``` python
In [168]: t = pd.Timestamp('2000-01-29 01:59:00')
In [169]: a = pd.Series([t])
In [170]: b = pd.Series([t])
In [171]: a
Out[171]:
0 2000-01-29 01:59:00
dtype: datetime64[ns]
In [172]: a == b
Out[172]:
0 True
dtype: bool
In [173]: a != b
Out[173]:
0 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11035 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11035/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11035/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11035/events | https://github.com/pandas-dev/pandas/issues/11035 | 105,627,275 | MDU6SXNzdWUxMDU2MjcyNzU= | 11,035 | BUG: Invisible rounding error in the results of Dataframe.interpolate() | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11036 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11036/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11036/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11036/events | https://github.com/pandas-dev/pandas/pull/11036 | 105,641,405 | MDExOlB1bGxSZXF1ZXN0NDQ1NzU2NjE= | 11,036 | BUG: Fix Timestamp __ne__ comparison issue | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11037/events | https://github.com/pandas-dev/pandas/pull/11037 | 105,675,826 | MDExOlB1bGxSZXF1ZXN0NDQ1OTQ3Nzk= | 11,037 | DOC: Fix misspelling of max_colwidth in documentation. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11038/events | https://github.com/pandas-dev/pandas/issues/11038 | 105,704,135 | MDU6SXNzdWUxMDU3MDQxMzU= | 11,038 | groupby.nth with dropna is buggy | {
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... | 3 | 2015-09-09T23:35:42Z | 2019-10-21T19:52:15Z | null | CONTRIBUTOR | null | ``` python
>>> df
1st 2nd
0 1 NaN
1 2 3
2 1 NaN
3 2 4
>>> df.groupby('1st')['2nd'].nth(0, dropna=True)
1st
1 3
2 4
Name: 2nd, dtype: float64
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11039/events | https://github.com/pandas-dev/pandas/pull/11039 | 105,704,428 | MDExOlB1bGxSZXF1ZXN0NDQ2MTM1NjU= | 11,039 | PERF: improves performance in GroupBy.cumcount | {
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```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
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https://api.github.com/repos/pandas-dev/pandas/issues/11040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11040/events | https://github.com/pandas-dev/pandas/issues/11040 | 105,727,590 | MDU6SXNzdWUxMDU3Mjc1OTA= | 11,040 | to_csv | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-09-10T03:57:40Z | 2015-09-21T12:41:12Z | 2015-09-10T10:33:31Z | NONE | null | > > > import pandas as pd
> > > text = 'this is "out text"'
> > > df = pd.DataFrame(index=['1'],columns=['1','2'])
> > > df.loc['1','1']=123
> > > df.loc['1','2']=text
> > > df.to_csv('foo.txt',index=False,header=False,quoting=3,sep=' ')
---
Error Traceback (most recent call last)
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https://api.github.com/repos/pandas-dev/pandas/issues/11041 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11041/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11041/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11041/events | https://github.com/pandas-dev/pandas/issues/11041 | 105,730,669 | MDU6SXNzdWUxMDU3MzA2Njk= | 11,041 | grouped.filter with axis=1 gave filtered rows | {
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minimum example here:
```
import numpy as np
import pandas as pd
index = pd.MultiIndex.from_product([range(10), [0, 1]])
data = pd.DataFrame(np.arange(100).reshape(-1, 20), columns=index)
data.groupby(level=0, axis=1).filter(lambda x: x.iloc[0, 0] > 10)
```
expected: get all filtered (grouped) co... | {
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... | 1 | 2015-09-10T05:59:15Z | 2019-10-23T05:23:37Z | null | MEMBER | null | As a follow up on #9321:
We should start adding optional arguments that are common between multiple plot types to the signatures for plotting sub-methods, such as `figsize` and `axis`. Currently, these are only documented on the main `.plot()` methods and we use `**kwargs` for the rest. This would be nice because we c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11043 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11043/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11043/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11043/events | https://github.com/pandas-dev/pandas/issues/11043 | 105,737,686 | MDU6SXNzdWUxMDU3Mzc2ODY= | 11,043 | DOC: consider updating visualization.rst to use plot submethods rather than kind | {
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Most of the documentation examples in visualization.rst (the [main plotting docs](http://pandas.pydata.org/pandas-docs/stable/visualization.html)) currently use the `kind` argument. We should probably switch these to use the plotting submethods instead, to push users in that direction.
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https://api.github.com/repos/pandas-dev/pandas/issues/11044 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11044/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11044/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11044/events | https://github.com/pandas-dev/pandas/issues/11044 | 105,739,206 | MDU6SXNzdWUxMDU3MzkyMDY= | 11,044 | Wrong string comparison in df.eval | {
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... | 3 | 2015-09-10T06:15:34Z | 2019-10-22T16:15:12Z | null | NONE | null | Why string comparison return 'False'? How I can compare strings?
```
In [3]: import pandas as pd
In [4]: df = pd.DataFrame( {'col1':[1,0]} )
In [5]: external_var = 'yes'
In [6]: df.eval( '(@external_var == "yes") == col1' )
Out[6]:
0 False
1 True
dtype: bool
In [7]: df.eval( '(@external_var == "yes")')
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https://api.github.com/repos/pandas-dev/pandas/issues/11045 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11045/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11045/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11045/events | https://github.com/pandas-dev/pandas/issues/11045 | 105,749,080 | MDU6SXNzdWUxMDU3NDkwODA= | 11,045 | gc.collect(2) call shouldn't be forced by pandas | {
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https://github.com/pydata/pandas/blob/master/pandas/core/generic.py#L1278
Ideally pandas shouldn't call the garbage collector manually, or at the very list t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11046 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11046/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11046/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11046/events | https://github.com/pandas-dev/pandas/pull/11046 | 105,762,127 | MDExOlB1bGxSZXF1ZXN0NDQ2NDIwMzY= | 11,046 | DOC: correct quoting constants order | {
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```
In [1]: import csv
In [2]: csv.QUOTE_NONNUMERIC
Out[2]: 2
In [3]: csv.QUOTE_NONE
Out[3]: 3
```
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] | closed | false | null | [] | null | 3 | 2015-09-10T09:01:48Z | 2015-09-10T11:43:23Z | 2015-09-10T11:43:23Z | NONE | null | I want to upload a file from csv to mysql with the following commands
```
df = pandas.read_csv('test.csv', encoding = 'utf-8')
engine = sqlalchemy.create(<Mysql-Connection-URI>, encoding = 'utf-8')
df.to_sql('testtable', con = engine)
```
mysql has utf-8 as global encoding. If I upload the file manually to the datab... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11049 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11049/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11049/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11049/events | https://github.com/pandas-dev/pandas/pull/11049 | 105,789,427 | MDExOlB1bGxSZXF1ZXN0NDQ2NTczMzI= | 11,049 | BUG: GH10645 and GH10692 where operation on large Index would error | {
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closes #10692
replaces #10675
Do I need `@slow` for these tests?
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] | closed | false | null | [] | null | 2 | 2015-09-10T12:15:57Z | 2015-10-08T19:23:35Z | 2015-09-10T12:24:04Z | NONE | null | There appears to be a memory leak in the DataFrame.dropna(inplace=True) function. Please see the ipython session:
```
[1]:
import ipython_memory_usage.ipython_memory_usage as imu
imu.start_watching_memory()
import pandas as pd
import numpy as np
pd.__version__
Out[1]: '0.16.2'
In [1] used 23.6875 MiB RAM in 0.70s, ... | {
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As long as API differences can be reconciled, I think it would be a nice idea to deprecate (and eventually remove) `DataFrame.hist`, `DataFrame.boxplot` and `Series.hist` in favor of the ... | {
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``` python
# Setup test DF
df = pd.DataFrame(np.random.randint(0, 10, size=(10,5)))
df['date'] = pd.date_range(start='2015-01-01', periods=10)
df.loc[0:4, 'id'] = '... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11057 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11057/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11057/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11057/events | https://github.com/pandas-dev/pandas/pull/11057 | 105,946,633 | MDExOlB1bGxSZXF1ZXN0NDQ3NDc2MjQ= | 11,057 | DOC: Updated doc-string using new doc-string design for DataFrameFormatter | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11058 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11058/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11058/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11058/events | https://github.com/pandas-dev/pandas/issues/11058 | 105,955,829 | MDU6SXNzdWUxMDU5NTU4Mjk= | 11,058 | pd.concat on two (or more) series produces all-NaN dataframe | {
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I'm trying to concat two series w... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11059 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11059/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11059/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11059/events | https://github.com/pandas-dev/pandas/pull/11059 | 105,974,392 | MDExOlB1bGxSZXF1ZXN0NDQ3NjE3MzM= | 11,059 | DOC: clean up 0.17 whatsnew | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11060 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11060/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11060/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11060/events | https://github.com/pandas-dev/pandas/issues/11060 | 106,020,298 | MDU6SXNzdWUxMDYwMjAyOTg= | 11,060 | TST: random html testing issues | {
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"followers_url": "https://api.github... | 0 | 2015-09-11T14:20:16Z | 2019-01-03T00:40:12Z | 2019-01-03T00:40:12Z | CONTRIBUTOR | null | I have seen these errors spuriously: https://travis-ci.org/jreback/pandas/jobs/79852164
but they go away when re-run...any ideas?
```
======================================================================
FAIL: test_to_html_truncate (pandas.tests.test_format.TestDataFrameFormatting)
----------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11061 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11061/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11061/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11061/events | https://github.com/pandas-dev/pandas/issues/11061 | 106,022,822 | MDU6SXNzdWUxMDYwMjI4MjI= | 11,061 | shifting across blocks results in NaNs if dataframe has multiple blocks (mixed types) | {
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... | 2 | 2015-09-11T14:34:54Z | 2015-09-12T13:16:30Z | 2015-09-12T13:16:30Z | NONE | null | variant of #10907
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a' : [1],'b':[2]})
In [3]: df
Out[3]:
a b
0 1 2
In [4]: df['c'] = 3.5
In [5]: df.shift(axis=1)
Out[5]:
a b c
0 NaN 1 NaN
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11062/events | https://github.com/pandas-dev/pandas/issues/11062 | 106,025,631 | MDU6SXNzdWUxMDYwMjU2MzE= | 11,062 | Provide option to render HTML tags | {
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... | 1 | 2015-09-11T14:51:15Z | 2021-09-09T14:14:19Z | 2021-09-09T14:14:19Z | NONE | null | Exploring [in-lining plots](https://github.com/iiSeymour/sparkline-nb/blob/master/sparkline-nb.ipynb) in DataFrames I found it would be useful to have a display option for setting HTML escaping. This would avoid the work around of `HTML(df.to_html(escape=False))` every time you want to render the DataFrame.
I propose ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11063 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11063/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11063/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11063/events | https://github.com/pandas-dev/pandas/pull/11063 | 106,027,356 | MDExOlB1bGxSZXF1ZXN0NDQ3OTIxOTA= | 11,063 | Adds the option display.escape_notebook_repr_html | {
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] | closed | false | null | [] | null | 3 | 2015-09-11T14:59:20Z | 2015-11-02T12:08:44Z | 2015-11-02T12:08:44Z | NONE | null | Adds the option `display.escape_notebook_repr_html` for controlling HTML escaping when rendering DataFrames in IPython notebooks. Unset, this argument does not change the default behaviour.
Fixes #11062
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https://api.github.com/repos/pandas-dev/pandas/issues/11064 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11064/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11064/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11064/events | https://github.com/pandas-dev/pandas/issues/11064 | 106,029,608 | MDU6SXNzdWUxMDYwMjk2MDg= | 11,064 | Iterate over an Excel file (.xls/.xlsx) without loading the data into the memory | {
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I work with large excel files which I use as... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11065 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11065/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11065/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11065/events | https://github.com/pandas-dev/pandas/pull/11065 | 106,048,025 | MDExOlB1bGxSZXF1ZXN0NDQ4MDMxNzg= | 11,065 | BUG: matplotlib inset-axis #10407 | {
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I just added a bunch of try-catch AttributeError around the offending code - it seemed to do the trick for me.
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```
In [16]: pd.MultiIndex.from_product([pd.to_datetime(['2000-01-01', 'NaT', '2000-01-05'])])
Out[16]: DatetimeIndex(['2000-01-01', '2000-01-05', '2000-01-05'], dtype='datetime64[ns]', freq=None)
In [25]: pd.MultiIndex.from_product([[1.0, np.nan, 2.0]])
Out[25]: Float64I... | {
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@jreback
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---
Closes #6100
Closes #3800
Just to check how this takes on travis
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- [x] #11073 Allow streaming reads<br>
This is the most important improvement for me. The current pandas code downloads the entire file ... | {
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Checking for the file extension in the `_read` function should make the checks inside the parsers redu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11075 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11075/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11075/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11075/events | https://github.com/pandas-dev/pandas/issues/11075 | 106,170,122 | MDU6SXNzdWUxMDYxNzAxMjI= | 11,075 | DOC: Comparison with SAS | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-09-12T16:21:30Z | 2015-09-13T19:10:41Z | 2015-09-13T19:10:41Z | CONTRIBUTOR | null | I don't really use SAS any more, but I've got some SAS -> pandas notes I was thinking of expanding to a doc section (like the R & SQL ones) as long as people don't think it's too out of scope for the pandas docs.
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https://api.github.com/repos/pandas-dev/pandas/issues/11076 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11076/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11076/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11076/events | https://github.com/pandas-dev/pandas/issues/11076 | 106,171,863 | MDU6SXNzdWUxMDYxNzE4NjM= | 11,076 | DataFrame.plot() with secondary_y is missing y-axis gridlines, has x-axis gridlines above data | {
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... | open | false | null | [] | null | 1 | 2015-09-12T16:51:35Z | 2021-04-18T23:42:14Z | null | NONE | null | Info:
matplotlib (1.4.3)
pandas (0.16.2)
Python 3.4.2
Example:
```
dataframe.plot(secondary_y=['error'])
```
Yields:
The gridlines appear on top of the `power` data and there are no lines parallel to the y-a... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11077 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11077/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11077/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11077/events | https://github.com/pandas-dev/pandas/issues/11077 | 106,175,299 | MDU6SXNzdWUxMDYxNzUyOTk= | 11,077 | Broken nunique on Series group by | {
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``` python
data = pd.DataFrame(
[[100, 1, 'Alice'],
[200, 2, 'Bob'],
[300, 3, 'Charlie'],
[-400, 4, 'Dan'],
[500, 5, 'Edith']],
columns=['amount', 'id', 'name']
)
expected = data.groupby(['id', 'amount'])['name'].nunique()
```
G... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-09-12T20:52:47Z | 2015-10-14T10:56:26Z | 2015-10-14T10:56:26Z | CONTRIBUTOR | null | We don't keep the sortedness state in an index per-se, but it is rather cheap to check
- `is_monotonic_increasing` or `is_monotonic_decreasing` on a reg-index
- MultiIndex should check `is_lexsorted` (this might be done already)
```
In [8]: df = DataFrame(np.random.randn(1000000,2),columns=list('AB'))
In [9]: %timei... | {
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import numpy as np
import pandas as pd
s1 = pd.Series([1, 2, 3], name='x')
s2 = pd.Series(name='y')
# NG, no columns is added
pd.concat([s1, s2], axis=1)
# x
#0 1
#1 2
#2 3
# If padded by value, OK
pd.concat([s1, pd.Series([np.nan]*3, name='z')], axis=1)
# x z
#0 1 NaN
#1 2 NaN
#2 3 NaN
# If conve... | {
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The regressions pages is now working [here](http://pydata.github.io/pandas/#regressions?sort=3&dir=desc)
- [ ] http://pydata.github.io/pandas/#groupby.groupby_indices.time_groupby_indices
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https://api.github.com/repos/pandas-dev/pandas/issues/11086 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11086/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11086/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11086/events | https://github.com/pandas-dev/pandas/issues/11086 | 106,209,958 | MDU6SXNzdWUxMDYyMDk5NTg= | 11,086 | Union of index with itself not invariant, sets frequency attribute | {
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```
In [56]: index = pd.bdate_range('20150101', periods=10)
In [57]: index.freq = None
In [58]: index
Out[58]:
<class 'p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11087 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11087/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11087/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11087/events | https://github.com/pandas-dev/pandas/pull/11087 | 106,219,780 | MDExOlB1bGxSZXF1ZXN0NDQ4NzM1NDk= | 11,087 | DOC: Comparison with SAS | {
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xref #4052
There doesn't seem to be a straightforward way to syntax highlight the SAS code, so it will format as generic code blocks.
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Valid BigQuery credentials are required to properly test the gbq module on travis.
See PR #10857
```
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```
-------------------------------------------------------------
#15 nose.failure.Failure.runTest: need httplib2 and auth libs
-------------------------------------------------------------
```
I'm not familar with the ga m... | {
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This commit should resolve the following error in the travis build log.
```
-------------------------------------------------------------
#15 nose.failure.Failure.runTest: need httplib2 and auth libs
-------------------------------------------------------------
```
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CC @sklam
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cc @parthea
now that the testing infrastructure is more sound, I think this only requires a newish version of the apiclient (e.g. > 1.4)
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so the Call node as changed
need to do something like this: https://bitbucket.org/pytest-dev/pytest/pull-requests/296/astcall-signature-changed-on-35/diff
If this is fixed I think everything will pass. I guess this i... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11101 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11101/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11101/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11101/events | https://github.com/pandas-dev/pandas/issues/11101 | 106,511,141 | MDU6SXNzdWUxMDY1MTExNDE= | 11,101 | SeriesGroupBy.count() failing with TypeError | {
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"gists_url": "https://api.g... | [] | closed | false | null | [] | null | 1 | 2015-09-15T08:47:46Z | 2015-09-15T10:18:44Z | 2015-09-15T10:18:44Z | CONTRIBUTOR | null | ``` python
>>> import pandas as pd
>>> pd.__version__
u'0.17.0rc1'
>>> df = pd.DataFrame({'A': [0, 0, 1, 1], 'B': range(4)})
>>> df.groupby('A').count()['B']
A
0 2
1 2
Name: B, dtype: int64
>>> df.groupby('A')['B'].count()
Traceback (most recent call last):
...
TypeError: Cannot cast array data from dtype('int6... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11102 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11102/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11102/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11102/events | https://github.com/pandas-dev/pandas/pull/11102 | 106,546,271 | MDExOlB1bGxSZXF1ZXN0NDUwNDM1ODk= | 11,102 | ENH: Data formatting with unicode length | {
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"description": "__repr__ of pandas objects, to_string",
"id": 13101118,
"name": "Output-Formatting",
"node_id": "MDU6TGFiZWwxMzEwMTExOA==",
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"closed_at": "2015-10-09T18:34:35Z",
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"followers_url": "https://api.github.com/users/jr... | 23 | 2015-09-15T12:38:20Z | 2015-10-03T22:24:44Z | 2015-10-03T14:41:03Z | MEMBER | null | Closes #2612. Added `display.unicode.east_asian_width` options, which calculate text width considering East Asian Width. Enabling this option affects to a performance as width must be calculated per characters.
#### Current results (captured)
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